1

Name:

Personal Genomes: Accessing,

Sharing and Interpretation

Wellcome Genome Campus Conference Centre, Hinxton, Cambridge, UK

11-12 April 2019

Scientific Programme Committee:

Stephan Beck

University College London, UK

Mad Price Ball

Open Humans Foundation, USA

Manuel Corpas

Cambridge Precision Medicine, UK

Mahsa Shabani

University of Leuven, Belgium

Tweet about it: #PersGen19

@ACSCevents /ACSCevents /c/WellcomeGenomeCampusCoursesandConferences

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Wellcome Genome Campus Scientific Conferences Team:

Rebecca Twells

Head of Advanced Courses and

Scientific Conferences

Treasa Creavin

Scientific Programme

Manager

Nicole Schatlowski

Scientific Programme

Officer

Jemma Beard

Conference and Events

Organiser

Lucy Criddle

Conference and Events

Organiser

Laura Hubbard

Conference and Events Manager

Sarah Offord

Conference and Events Office

Administrator

Zoey Willard

Conference and Events

Organiser

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Dear colleague,

I would like to offer you a warm welcome to the Wellcome Genome Campus Advanced Courses and

Scientific Conferences: Personal Genomes: Accessing, Sharing and Interpretation. I hope you will find

the talks interesting and stimulating, and find opportunities for networking throughout the schedule.

The Wellcome Genome Campus Advanced Courses and Scientific Conferences programme is run on a

not-for-profit basis, heavily subsidised by the Wellcome Trust.

We organise around 50 events a year on the latest biomedical science for research, diagnostics and

therapeutic applications for human and animal health, with world-renowned scientists and clinicians

involved as scientific programme committees, speakers and instructors.

We offer a range of conferences and laboratory-, IT- and discussion-based courses, which enable the

dissemination of knowledge and discussion in an intimate setting. We also organise invitation-only

retreats for high-level discussion on emerging science, technologies and strategic direction for select

groups and policy makers. If you have any suggestions for events, please contact me at the email

address below.

The Wellcome Genome Campus Scientific Conferences team are here to help this meeting run

smoothly, and at least one member will be at the registration desk between sessions, so please do

come and ask us if you have any queries. We also appreciate your feedback and look forward to your

comments to continually improve the programme.

Best wishes,

Dr Rebecca Twells Head of Advanced Courses and Scientific Conferences rebecca.twells@wellcomegenomecampus.org

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General Information

Conference Badges

Please wear your name badge at all times to promote networking and to assist staff in identifying you.

Scientific Session Protocol

Photography, audio or video recording of the scientific sessions, including poster session is not

permitted.

Social Media Policy

To encourage the open communication of science, we would like to support the use of social media at

this year’s conference. Please use the conference hashtag #PersGen19. You will be notified at the

start of a talk if a speaker does not wish their talk to be open. For posters, please check with the

presenter to obtain permission.

Internet Access

Wifi access instructions:

Join the ‘ConferenceGuest’ network

Enter your name and email address to register

Click ‘continue’ to send an email to the registered email address

Open the registration email, follow the link ‘click here’ and confirm the address is valid

Enjoy seven days’ free internet access!

Repeat these steps on up to 5 devices to link them to your registered email address

Presentations

Please provide an electronic copy of your talk to a member of the AV team who will be based in the

meeting room.

Poster Sessions

Posters will be displayed throughout the conference. Please display your poster in the Conference

Centre on arrival. There will be one poster sessions during the conference. Which takes place on

Thursday, 11 April at 18:30-19:30.

The abstract page number indicates your assigned poster board number. An index of poster

numbers appears in the back of this book.

Conference Meals and Social Events

Lunch and dinner will be served in the Hall, apart from lunch on Thursday 11 April when it will be

served in the Conference Centre. Please refer to the conference programme in this book as times will

vary based on the daily scientific presentations. Please note there are no lunch or dinner facilities

available outside of the conference times.

All conference meals and social events are for registered delegates. Please inform the conference

organiser if you are unable to attend dinner.

The Hall Bar (cash bar) will be open from 19:00 – 23:00 on Thursday, 11 April.

Dietary Requirements

If you have advised us of any dietary requirements, you will find a coloured dot on your badge.

Please make yourself known to the catering team and they will assist you with your meal request.

If you have a gluten allergy, we are unable to guarantee the non-presence of gluten in dishes even if

they are not used as a direct ingredient. This is due to gluten ingredients being used in the kitchen.

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For Wellcome Genome Campus Conference Centre Guests

Check in

If you are staying on site at the Wellcome Genome Campus Conference Centre, you may check into

your room from 14:00. The Conference Centre reception is open 24 hours.

Breakfast

Your breakfast will be served in the Hall restaurant from 07:30 – 09:00

Telephone

If you are staying on-site and would like to use the telephone in your room, you will need to

contact the Reception desk (Ext. 5000) to have your phone line activated - they will require your

credit card number and expiry date to do so.

Departures

You must vacate your room by 10:00 on the day of your departure. Please ask at reception for

assistance with luggage storage in the Conference Centre.

Taxis

Please find a list of local taxi numbers on our website. The conference centre reception will also be

happy to book a taxi on your behalf.

Return Ground Transport

Complimentary return transport has been arranged for 18:30 on Friday, 12 April to Cambridge

station and city centre (Downing Street), and Stansted and Heathrow airports.

A sign-up sheet will be available at the conference registration desk from 15:30 on Thursday, 11

April. Places are limited so you are advised to book early.

Please allow a 30 minute journey time to both Cambridge and Stansted Airport, and two and a half

hours to Heathrow.

Messages and Miscellaneous

Lockers are located outside the Conference Centre toilets and are free of charge.

All messages will be posted on the registration desk in the Conference Centre.

A number of toiletry and stationery items are available for purchase at the Conference Centre

reception. Cards for our self-service laundry are also available.

Certificate of Attendance

A certificate of attendance can be provided. Please request one from the conference organiser

based at the registration desk.

Contact numbers

Wellcome Genome Campus Conference Centre – 01223 495000 (or Ext. 5000)

Wellcome Genome Campus Conference Organiser (Jemma) – 07771 666665

If you have any queries or comments, please do not hesitate to contact a member of staff who will

be pleased to help you.

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Conference Summary

Thursday 11 April

11:30-12:30 Registration with lunch

12:30-12:40 Welcome and Introductions

12:40-13:30 Keynote lecture: George Church

13:30-15:30 Session 1: Personal genetic testing: opportunities and limitations

15:30-16:00 Afternoon Tea

16:00-17:30 Session 2: Interpretation of personal genomes

17:30-18:30 Panel discussion: benefits from testing and sharing personal genome data

18:30-19:30 Poster Session with drink reception

19:30 Dinner

Friday 12 April

09:00-10:30 Session 3: Citizen science and personal genomics: users, customers and

patients

10:30-11:00 Morning Coffee

11:00-12:30 Session 4: Return of data to research participants and personal data access

12:30-14:00 Lunch

14:00-15:30 Session 5: Society challenges: data protection and privacy and the ethics of

data sharing

15:30-16:00 Afternoon Tea

16:00-17:00 Session 6: Clinical perspective – from patients to the public

17:00-18:00 Keynote Lecture: Yaniv Erlich

18:00-18:15 Concluding Remarks

18:30 Coaches depart to Cambridge City Centre and Train Station, Heathrow

Airport via Stansted Airport

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Conference Sponsors

www.cpm.onl

8

Personal Genomes: accessing, sharing and interpretation

Wellcome Genome Campus Conference Centre,

Hinxton, Cambridge

11-12 April

Lectures to be held in the Francis Crick Auditorium

Lunch and dinner to be held in the Hall Restaurant

Poster session to be held in the Conference Centre

Spoken presentations - If you are an invited speaker, or your abstract has been selected for a

spoken presentation, please give an electronic version of your talk to the AV technician.

Poster presentations – If your abstract has been selected for a poster, please display this in the

Conference Centre on arrival.

Conference programme

Thursday, 11 April

11:30-12:30 Registration with lunch

12:30-12:40 Welcome and Introductions

Stephan Beck, University College London, UK

12:40-13:30 Keynote Lecture:

New Technologies & Sharing Comprehensive Personal Precision-Medicine

Data George Church, Harvard University, USA

13:30-15:30 Session 1: Personal genetic testing: opportunities and limitations

Chair: Manuel Corpas

13:30 Personal Genomes and beyond for the Indian population

Anu Acharya

Mapmygenome, India

14:00 Codigo46: Bridging the gap between the promises and realities of

personalized medicine in Mexico

Lorenza Haddad

Codigo46, Mexico

14:30 From Biobanking to Precision Medicine

Andres Metspalu

Estonian Genome Centre, Estonia

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15:00 The Personal Genome Project Canada: findings from whole genome

sequences of the inaugural cohort

Naveed Aziz

CGEn, Canada

15:15 Korean Personal Genome project

Sungwon Jeon

Ulsan National Institute of Science and Technology, South Korea

15:30-16:00 Afternoon Tea

16:00-17:30 Session 2: Interpretation of personal genomes

Chair: Mad Price Ball

16:00 Analyzing personal genomes, phenomes and electronic health records

at scale

Gustavo Glusman

Institute of Systems Biology, USA

16:30 Empowering the public to use personal genomic information: a genetic

counsellor perspective

Nicki Taverner

University of Cardiff, UK

17:00 Using personal genomes to calculate and interpret polygenic risk

scores in preparation for genomic medicine

Cathryn Lewis

King's College London, UK

17:15 Leveraging phenome-wide information to improve accuracy and

applicability of genetic risk predictions for complex traits

Vincent Plagnol

GenomicsPlc, UK

17:30-18:30 Panel discussion: benefits from testing and sharing personal genome

data

Chair: Mahsa Shabani

Fiona Nelson Repositive, UK

Tom Stubbs Chronomics, UK

18:30 -19:30 Poster Session with Drinks Reception

19:30 Dinner

Friday, 12 April

09:00-10:30 Session 3: Citizen science and personal genomics: users, customers

and patients

Chair: Stephan Beck

09:00 Harnessing the power of open crowdsourcing for personal genomics

Bastian Greshake Tzovaras Open Humans, USA

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09:30 Open donation of personal genome sequence data from the

perspective of a hybrid scientist/citizen scientist

Colin Smith

University or Brighton, UK

10:00 Genomics Aotearoa, a platform for best practice genomic science in

partnership with indigenous people

Ben Te Aika

Genomics Aoteraoa, New Zealand

10:15 The rise of genetic genealogy as a citizen science

Maurice Gleeson

International Society of Genetic Genealogy

10:30-11.00 Morning Coffee

11:00-12:30 Session 4: Return of data to research participants and personal data

access

Chair: Fiona Nielsen

11:00 What is the behavioral impact of personal genomic information, and

why does it matter?

Saskia Sanderson

University College London, UK

11:30 Genomics England - treating data with care

Joanne Hackett

Genomics England, UK

12:00 Health data sharing and data protection law in Africa: a South African

perspective

Ciara Staunton

Middlesex University, UK

12:15 Evaluating utility of patient-centered deep phenotyping

Monica Munoz-Torres

Oregon State University, USA

12:30-14:00 Lunch

14.00-15:30 Session 5: Society challenges: data protection and privacy and the

ethics of data sharing

Chair: Mahsa Shabani

14:00 Personal genomes and the police: public opinion and ethical

considerations

Christi Guerrini

Baylor College of Medicine, USA

14:30 Access, Storage, and Sharing of personal genomic information

Pascal Borry

University of Leuven, Belgium

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15:00 Accessing 1M Genomes transnationally across Europe by 2022

Gary Saunders

ELIXIR Europe, UK

15:15 Genomics as a personalized medicine approach in disease risk

prediction - P5.fi FinHealth

Heidi Marjonen

National Institute for Health and Welfare, Finland

15:30-16:00 Afternoon Tea

16:00-17:00 Session 6: Clinical perspective – from patients to the public

Chair: Johan den Dunnen

16:00 Knowns and unknowns in genomic testing; a clinician’s eye view

Frances Elmslie

St George's, University of London

16.30 Embedding genomics into routine health care

Reecha Sofat

University College London, UK

17:00-18:00 Keynote Lecture:

Genetic privacy: friend or foe?

Yaniv Erlich

MyHeritage, Israel

18:00-18:15 Concluding remarks Programme committee

18:30 Coaches depart to Cambridge City Centre and Train Station,

Heathrow Airport via Stansted Airport

12

These abstracts should not be cited in bibliographies. Materials

contained herein should be treated as personal communication and

should be cited as such only with consent of the author.

S1

Spoken Presentations New Technologies & Sharing Comprehensive Personal Precision-Medicine Data George Church Harvard University, USA PersonalGenomes.org (PGP) is a unique international cohort (US, UK, Canada, Austria, China) with fully open consent (IRB, LREC, REB ethics approved) for genomes, many other omes, imaging and medical records. PGP has enabled: (1) NIST+FDA Genomeinabottle (GIAB) Diverse trios, (2) High-Quality Reference Genomes (HQRG) of the GIAB samples, hopefully soon filling all sequence gaps, (3) differentiation factor libraries, (4) ENCODE isogenic multiple cell types, (5) in situ barcoding of RNA, DNA and protein at conventional and super-resolution (20 nm). (6) Critical Assessment of Genome Interpretation (CAGI) (7) Re-identification tests, (8) Tests of avoiding identification including Nebula.org homomorphic encryption queries, (9) full individual comprehensive precision medicine datasets. All of these benefit for fully shareable cells and data without restrictions (analogous to Wikipedia). Also we describe new "omic" reading, 3D-imaging and editing technologies developed using these cells.

S2

Notes

S3

Personal Genomes and beyond for the Indian population

Anuradha Acharya1

1Mapmygenome India Limited, Hyderabad, Telangana, India

Indians currently make up ~20% of the global population, a number projected to touch 1.5

billion by the year 2030. However, if we look at global genetic information databases, Indian

data accounts for less than 0.2% of the total data.

Today, genomic data holds tremendous potential in improving healthcare strategies across

various dimensions - be it disease prevention, enhanced diagnosis, optimised treatment or

optimal drug development. The efforts of genetic and medical researchers are constantly

driven towards utilization of this potential and its translation into actionable information and

clinical applications thereof. The two biggest hurdles facing the medical and research

community today are the lack of genotype-phenotype correlations for Indians at a

population-wide and individual level, and the inefficient translation of genomic information

into the decision making process in traditional medical practice. Population-wide sequencing

projects for Indian genomes help overcome these hurdles. By creating a centralized

database of Indian genomic data (anonymized and de-identified), analytical efforts can be

made to identify biomarkers (and their clinically significant ranges) specific to health

conditions and traits, via case-control associations and bioinformatics studies. These

findings, when integrated with biological data points (electronic health records, medical

reports, family history), can be used to tailor an individual’s healthcare plan (personalised),

for disease prevention (predictive), and for continuous monitoring and feedback

(participatory). The database would be constantly updated with inputs (from wearable

devices, health apps and medical concierge services) for active tracking of individual health

status, whilst performing trend analysis, making it truly dynamic. Machine-learning

(ML)/Artificial Intelligence (AI) integration would enable high-end analytics and automation

with the benefit of deep learning algorithms for pattern recognitions and enhanced

predictions.

The benefits of personal genomics spread across many verticals in the health and wellness

industry - nutritional intervention and therapy (nutrigenomics), personalized medicine by

drug-response profiling (pharmacogenomics), sports and exercise genomics, reproductive

medicine (carrier screening), lifetime disease risk assessment and mitigation, etc - which

would be elaborated upon in the form of case studies during the presentation.

S4

Notes

S5

Codigo46: Bridging the gap between the promises and realities of personalized medicine in Mexico

Lorenza Haddad Talancon1

1Codigo 46 Precision medicine is supposed to be the future of healthcare. Genetics is just one

component to be able to truly individualize medicine, but the promises of it fall short

for two main reasons; we need more research, and for some populations the current

tools for precision medicine might not be accurate. Codigo 46’s goals lie in both

providing current genetics applications for health in an accessible way as well as to

generate knowledge from within those understudied populations in order to make

precision medicine a reality for everyone. We are far away from what science fiction

depicts, but there are real applications for where science is today. For example, in

prescribing certain medication and correct dosage for patients, or helping doctors

realize a patient’s risk for a disease and planning prevention strategies or testing.

The problem is these have been developed using mostly people of European

descent as reference, excluding understudied and underprivileged populations like

the Mexican one, and not prioritizing the diseases or risk factors, both genetic and

environmental, these populations face. Codigo 46 is already building its offerings

based on research for these populations as well as a data base to promote the

creation of knowledge for Mexicans.

S6

Notes

S7

From Biobanking to Precision Medicine

Andres Metspalu

The Estonian Genome Center, Institute of Genomics, University of Tartu, Estonia

The Estonian Biobank was founded in 2000 as a population-based biobank. 19 years later,

the biobank includes a collection of health and genetics data of around 156 000 people and

by the end of the 2019 it will be increased to 200 000, or approximately 20% of the adult

population. All participants of the biobank have donated blood samples for purification of DNA

and plasma. The whole cohort of 200 000 will be genotyped with Illumina GSA array (currently

152 000). The Human Genes Research Act (from year 2000) allows regular updating of data

through linkage to national registries enabling long-term follow-up of the cohort and to re-

contact the gene donors and the changes needed by GDPR were mostly cosmetic. WGS is

performed on 2600 and WES on 2500 genomes allowing to use this as population based

reference for imputing. In the past few years increasing amount of attention has been placed

on translating the results of genetic research to improve public health. A nationwide technical

infrastructure (X-road) for the secure electronic exchange of medical data has also been

established and is maintained by the state. This allows creating the disease (or life!)

trajectories on all gene donors from the birth in the Estonian Biobank, where all contacts with

the medical systems incl. ICD-10 diagnoses, prescriptions, lab data and EMR are included.

Recently, we have completed the deep sequencing of the (~30X coverage, PCR-Free) whole

genomes of 3,000 gene donors and in addition 2500 whole exomes. Using these data, we

have demonstrated in the case of familiar hypercholesterolemia that “the genetics first

approach” can discover many new FH patients not seen by medical system before and over

50% of cases the treatment was changed. We are conducting several pilot projects in order to

work out the best ways to return the heath related research data - genetic risks scores

(GRS) back to people in the biobank who are asking for it. This is the instrument of early

prediction and prevention of the disease. For that purpose, we have developed the decision

support tools for several major diseases like CAD, T2D, breast cancer, pharmacogenomics

etc. During the first contact with the genetic counsel and/or medical geneticist the rapport will

be explained and if needed further recommendation given. It will be transferred to the medical

system in next few years and together with the RITA program on personalized medicine in two

largest hospital in Estonia the personalized medicine as 4P medicine (personal, predictive,

preventive and participatory) has reached to the point of no return.

S8

Notes

S9

The Personal Genome Project Canada: findings from whole genome sequences of the inaugural cohort

Stephen W. Scherer, Naveed Aziz

CGEn - National Platform for Genome Sequencing & Analysis

Rapid technological advances are enabling a view of human genetic variation in ever-increasing detail and at plummeting costs.1 Until recently, analysis has been targeted largely to defined genes, but pan-genomic approaches, such as microarrays, gene-panel testing and exome sequencing, have become mainstream. Now, whole genome sequencing can capture all of the genes (about 1% of the whole genome) and most of the rest of the genome in a single experiment, with the potential to recognize all types of genetic variation and thereby usurp the less comprehensive technologies Information from whole genome sequencing can already identify the molecular causes of suspected heritable conditions and cancer; however, we anticipate that genomic analysis will become a standard component of proactive health care, given its potential to identify predisposition to medically actionable conditions, explain uncharacterized disease and reveal carriers for recessive disorders and predictors of medication safety and response. Interpretation of sequence data remains challenging, with unknown clinical utility and predictive value among the general population. The Personal Genome Project Canada was launched in 2007 and shares the guiding principles and open consent policy of the parent project in the United States. It aims to develop a public data set of fully annotated genomic information, connected with human trait information. It can provide control data for other studies, but it also aims to forecast effects of integrating DNA-derived knowledge into routine clinical practice. The project will evaluate the utility of such information, and how best to gather and apply it within Canada's provincially administered, publicly funded health care system. Participants in this ongoing project are highly motivated to promote genomic research and explicitly forego privacy commitments. We report the data and experiences from whole genome sequencing and medical annotation of genomes of the first 56 participants in the Personal Genome Project Canada.

S10

Notes

S11

Korean Personal Genome Project Sungwon Jeon1, Asta Blazyte1, Sungwoong Jho2, Jungeun Kim2, Jong Bhak1,2 1KOGIC, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea 2Personal Genomics Institute, Genome Research Foundation, Cheongju 28160, Republic of Korea jongbhak@genomics.org KPGP or PGP-Korea is the first and longest-lasting Korean genome project. It was initiated by

the Korean Bioinformation Center (KOBIC) in 2006 to characterize ethnicity-relevant variome

of Koreans. It has three major goals. The first is to provide personal genome data to the public

by democratizing genomic information in Korea. The second goal is to build a unique Korean

reference genome (KOREF) that has both single genome (KOREF_S) assembly and

population consensus assembly (KOREF_C) to sufficiently represent the population as a

whole. The third is to develop Korean population variome, KoVariome, which aims to provide

as much genomic information as openfreely as possible. Since KPGP published the first

Korean genome data in 2009, the number of openfreely accessible complete genomes of

KPGP database has reached to 111 personal genomes as of 2018. Moreover, this database

was used to construct the first consensus Korean Reference genome standard (KOREF_C)

and KoVariome. These genome data were the first of its kind that were generated under

standard reference construction protocol as a joint project of National Center for Standard

Reference Data of Korea. However, we also collected 2,400 Korean genomes last two years

through 10,000 Korean genome project (KU10K). We also conducted Personal Welfare

Genome project in Korea which recruited 1,000 healthy Koreans for three years, providing a

free health check-up and a genetic counselling to the Ulsan citizen through a private hospital.

While the collected genomic data is used to expand KoVariome, the participants are provided

with our developed personal genome research report that contains relevant information such

as ancestry analysis results and allele information related to certain phenotypes and diseases.

S12

Notes

S13

Analyzing personal genomes, phenomes and electronic health records at scale Gustavo Glusman Institute for Systems Biology, USA Soon, millions of individual human genomes with rich phenotype data will be available for analysis, posing a data management challenge and offering significant discovery opportunities. Rich genomic and phenomic knowledge will help improve our understanding of genome structure, function and evolution, and will translate into actionable opportunities for improving health and wellness. We have developed several algorithms and methods for studying and visualizing personal genome data in family, cohort and population context. In particular, our 'genome fingerprinting' method enables ultrafast and private genome comparisons in very large cohorts, and our 'data fingerprinting' method offers fast, semantically and structurally agnostic method for analyzing electronic health records (e.g., in FHIR format). Our locality-sensitive hashing strategies summarize complex data into highly compressed representations which cannot recreate details in the data, yet simplify and greatly accelerate the comparison and clustering of data records by preserving similarity relationships. Applications include detection of duplicates, clustering and classification, which support higher goals including summarizing large and complex data sets, analyzing cohort structure, quality assessment, evaluating methods for generating simulated patient data, and data mining. Beyond genomes and electronic health records, our approach is applicable to any domain in which semi-structured data (e.g., in JSON or XML formats) are commonly used.

S14

Notes

S15

Empowering the public to use personal genomic information: a genetic counsellor perspective Nicki Taverner Cardiff University and All Wales Medical Genetics Service The rapid development of genomic testing provides real opportunities for individuals to learn more about their genetic health risks, and this genetic information can be empowering, enabling them to make informed decisions to manage their health. However, interpreting risk information is challenging, and many individuals will need support to understand and use this information. The rapid upscaling of testing means that we are not yet able to understand the implications of much of the genetic variation that we identify: we do not yet fully understand the exome, which is only around 1-1.5% of the genome. Genomic testing is important for us to develop this knowledge but, in the interim, it is important for individuals to understand what personal genomes can and cannot tell them. Genomic screening can also have significant psychosocial implications: examples in the news include individuals finding out about non-paternity or considering termination of pregnancy based on inaccurate information but, more commonly, individuals may be told that they have increased health risks and need support to manage the emotional impact of this. These impacts have significant implications for the NHS in the UK: testing may be carried out privately but individuals then seek the support of the NHS to understand and interpret their results, a significant burden both in terms of bioinformatic interpretation and provision of support. We have also seen examples where results of private testing have not been reproduced within NHS laboratories, highlighting the importance of laboratory standards. Genomic testing holds great promise but, in the medium term, there is a need for education and support to ensure that individuals are able to benefit from the interpretation of their personal genomes.

S16

Notes

S17

Using personal genomes to calculate and interpret polygenic risk scores in preparation for genomic medicine

Cathryn Lewis, Lasse Folkersen

King’s College London, UK Sankt Hans Hospital, Denmark

Interpretation of personal genomes has focused on single variants conferring disease risk, but most disorders of major public concern are polygenic. Polygenic risk scores (PRS) give a single measure of disease liability by summarising disease risk across hundreds of thousands of genetic variants. They can be calculated in any genome-wide genotype data-source, using a prediction model based on genome-wide summary statistics from external studies. As genome-wide association studies increase in power, the predictive ability for disease risk will also increase. While PRS are unlikely ever to be fully diagnostic, they may give valuable medical information for risk stratification, prognosis, or treatment response prediction. Public engagement on the potential use and acceptability of PRS is therefore becoming important. The current public perception of genetics is as a 'Yes/No' test. This model is only true for exceptionally strong effects, such as rare genetic disease or breast cancer mutations - variants that are FDA approved for reporting in consumer genetics. Meanwhile, unregulated third-party apps are being developed to satisfy consumer demand for information on lower risk variants and for common diseases that are highly polygenic. Many apps report results from single SNPs, with little regard to effect size, which is inappropriate for common, complex disorders where everybody carries risk alleles. Consequently, sites such as Promethease and Codegen.eu enable users to highlight (false) genetic causes for any disease: good business, but poor science. Communication tools are therefore needed to aid our understanding of genetic predisposition as a continuous trait, where a genetic liability confers risk for disease. Impute.me is one such a tool, whose focus is on education and explanation of polygenic disorders. Its research-focused open-source website allows users to upload consumer genetics data and obtain pseudo-PRS, with results reported on a population-level normal distribution. Diseases can be browsed by ICD10-chapter-location or alphabetically but are never sorted by worst risk-score. This ensures the user to consider genetic risk scores by medical context, not by risk. Clinical research studies indicate that PRS may already have predictive utility for coronary artery disease and breast cancer, despite problems in their interpretation across ancestry and the incomplete information captured. Personal genomes can play an important role in preparing for implementation of PRS in genomic medicine.

S18

Notes

S19

Leveraging phenome-wide information to improve accuracy and applicability of genetic risk predictions for complex traits

Vincent Plagnol, Eva Kraphol, Peter Sorensen, Chris Spencer, Peter Donnelly

GenomicsPlc

The broadening the availability of population cohorts combining genetic data and health records is bringing into focus the potential value of genetic risk prediction in health care. However, these opportunities also raise a range of concerns, in particular the limited predictive ability of these approaches and their much poorer performance outside populations with European ancestry. Motivated by these limitations Genomics plc have gathered, curated, and aligned, genome-wide summary statistics from over 10,000 association studies. This allows us simultaneously to develop genetic-based risk predictions across many common human diseases and traits. Additionally, by developing sophisticated statistical methods to leverage cross-trait information we can also considerably improve risk prediction for individual traits. Firstly, joint colocalisation across all 10,000 studies substantially improves fine-mapping resolution, and hence power to identify true causal variants. This not only improves the overall prediction ability, but critically it increases the utility of these predictions to populations outside of the groups in which the association studies were undertaken. It also substantially improves the ability to make predictions from complex regions such as the major histocompatibility complex (MHC), with major implications for autoimmune diseases. Secondly, compared with analyses of individual traits, statistical approaches which model and utilise the correlations between related traits can lead to more accurate estimates of effect sizes, which also improves prediction accuracy. Finally, we show that cross-ethnic association studies are sufficiently consistent across populations to improve effect size estimates and therefore prediction accuracy when jointly analysed with studies from different ancestry groups. Together, these strategies improve the resolution at established loci and better define a truly polygenic component based on a large number of loci with effect sizes that are individually small, but collectively meaningful. We illustrate the value of this phenome-wide approach to individual risk prediction on a range of traits, including coronary artery disease and immune related traits.

S20

Notes

S21

Harnessing the power of open crowdsourcing for personal genomics

Bastian Greshake Tzovaras1,2, Philipp E. Bayer3, Helge Rausch

1 Lawrence Berkeley National Laboratory, Berkeley, USA

2 Open Humans Foundation, USA

3 The University of Western Australia, Crawley, Australia

Direct-To-Consumer (DTC) genetic testing is rapidly gaining traction, with an exponential

growth and well over 10 million people already having been genotyped. This enormous

collection of genetic data has the potential to enable wide-reaching genetic research,

especially for researchers who lack funding to genotype large cohorts themselves. The

potential use of these data is demonstrated by the over 100 publications that 23andMe alone

has published using data from their customers. The full potential of these data is hard to turn

into action though, as it usually stays in the hands of the DTC testing companies, with only

limited access to it for individual researchers.

We built openSNP, to enable a wider access to DTC genotyping data. OpenSNP is a

crowdsourced repository for genotyping data through which individuals who participated in

DTC genotyping can deposit their own genotypes into the public domain – along with their

own, crowdsourced, phenotypic annotation. As all the data are in the public domain, no access

or data usage restrictions apply to them, making them well-suited for re-using the data in

various research contexts. Since its launch in late 2011, nearly 5,000 personal genotype and

exome data sets have been donated into the public domain through openSNP, making it one

of the world’s largest repositories of its kind. As a community-driven project, openSNP is fully

open source and funded through crowdfunding by the community.

Data provided through openSNP has been used in community-driven as well as academic

projects. For example, the data has been used in a CrowdAI machine learning competition,

evaluating the best methods to predict height from genotyping data. Other examples include

psychological studies that associate genotype-associations with exploration/exploitation

behaviour and research into genomic privacy and re-identification. Together, these examples

highlight how a crowdsourced open data platform such as openSNP can facilitate new kinds

of research and fuel scientific progress.

S22

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S23

Open donation of personal genome sequence data from the perspective of a hybrid

scientist/citizen scientist

Colin P. Smith

School of Pharmacy and Biomolecular Sciences, University of Brighton, Brighton, BN2 4GJ,

UK

I was fortunate to get the opportunity to have my whole genome sequenced in 2013 as a

participant in one of Illumina’s Understand Your Genome symposia. Since then I have been

keen to investigate the genome sequence in more detail and to exploit it as the subject for

outreach activities and debates on personal data sharing. My introduction to personal

genetic testing was quite extreme as my father had inherited an autosomal dominant

mutation for an untreatable fatal condition and I went through the lengthy and unsettling

process of being tested for the mutation. By contrast, having a full genome sequence report

for (principally) single nucleotide variants was relatively stress free! I am a strong advocate

for open sharing of personal genome sequence data and became an ambassador for the

Personal Genome Project UK (PGP-UK), led by Stephan Beck at University College London.

In this role I participated in the development of ‘GenoME’, an educational personal genomics

iPad app designed for the general public. I had the opportunity to enrol in the PGP-UK

programme in 2015 as the first ‘donor’ of a whole genome sequence, shortly after they had

received ethical approval to receive donations of whole human genome sequences under

open consent, from individuals who had had their genomes sequenced independently of the

PGP family of organisations. Participation with the PGP-UK team has been very rewarding,

providing an approachable, engaging and interactive opportunity for updating interpretation

of sequence variants and for engaging in additional genomic studies, such as the

epigenomic analysis and reporting that is also conducted by PGP-UK.

S24

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S25

Genomics Aotearoa, a platform for best practice genomic science in partnership with indigenous people.

Ben Te Aika, Prof Peter Dearden

University of Otago, University of Auckland

New Zealand has a unique and diverse bio-heritage landscape. The indigenous people (Māori) of New Zealand have an extensive and closely knit relationship with this landscape and are positioned centrally within it. Unfortunately, the role of Māori within genomic, as well as other, science, has been largely limited to that of research subject. This research subject role is at odds with the Treaty-based relationship that exists between the New Zealand Government and Māori. This obligates the government in a number of ways including; providing partnership in decision making, the active protection of Māori culture, and roles and engagement in how the country is governed and administered. International human rights agreements, such as the UN Covenant on Economic, Social and Cultural Rights and the UN Convention on Biological Diversity, also set out significant responsibilities for Māori and the Crown. The Government inquiry into its obligations to protect Māori interests in fauna and flora, cultural and intellectual property has identified significant issues and suggested outcomes. These obligations shape what is required and what is possible in New Zealand genomic science. Genomics Aotearoa has recently been established as a nationally significant data repository and research platform. It brings high-quality research and the role of Māori forward into a 21st century practice, where western science and indigenous people come together in an inclusive, proactive and mutually enriching approach to engagement, data management and genomic research. Genomics Aotearoa seeks to improve research ethical and professional practices by adopted new research principles, guidelines and practices relating to its indigenous peoples. Data storage is managed within a Māori-values framework underpinned by new research and data-storage guidelines. Genomics Aotearoa is embedded with Māori personnel, expert in their relevant fields and researchers work closely with the Māori community with the goal of research being far more relevant to the Māori people and New Zealand as a whole. Programs include increasing the involvement of Māori in the science through education. 12 Months after establishment Genomics Aotearoa is experiencing increasing inquiries from Māori about ethical storage of data. Māori organisations and businesses are expressing interest in knowing the benefits of the science and partnership opportunities. Researchers also, are becoming increasingly interested in knowing more about Māori culture and best practices for engaging and consulting groups outside of established western norms. International opportunities are emerging for researchers with increased diverse engagement skills.

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S27

The Rise of Genetic Genealogy as a Citizen Science

Maurice Gleeson

Education Ambassador, International Society of Genetic Genealogy

Ever since commercial DTC (direct to consumer) DNA tests became available in 2003,

people have been using these tests to run their own DNA projects. The first tests to become

available were Y-DNA and mitochondrial DNA tests. The Y-DNA tests were STR-based and

started with only 12 STR markers but quickly evolved to 111 markers. As the Y chromosome

and inherited surnames are passed down along the same direct male line, the Y-DNA test

lent itself to the study of surnames and many surname studies emerged. The company

FamilyTreeDNA (FTDNA) created an infrastructure to allow ordinary people to run their own

projects, and there are currently more than 10,000 such studies hosted on their website. In

addition haplogroup projects emerged which rapidly took over from the academic

researchers in helping to build the human Y-Haplotree (the Tree of Mankind). This research

was further boosted by the introduction of Y-SNP testing using Next Generation Sequencing.

To date more than 100,000 SNPs have been discovered on the Y-chromosome. Similar

research is ongoing with mitochondrial DNA and the construction of the Tree of Womankind

(the mitochondrial Haplotree). Geographic projects helped characterize the distribution of

both Y-DNA and mitochondrial DNA signatures in particular geographic locations. Some

studies have used Y-DNA to explore the accuracy (or otherwise) of the Ancient Annals of

Ireland and the ancient genealogies of Scotland. The introduction of autosomal DNA tests in

2007 by 23andMe added a new segment to the customer base that was primarily interested

in the health-related aspects of DNA testing. Few of these people were interested in

genealogy. Subsequently, the largest of the commercial companies (Ancestry) launched

their autosomal DNA test (a SNP-based microarray test assessing some 700,000 SNPs).

Additional “specialist groups” have emerged since then including those primarily interested in

adoptee research, and more recently the rise of forensic genetic genealogy and the ability to

use the databases to discover the identity of unknown persons. This latter development may

evolve further as it has potential applications in mass grave situations such as the mass

grave at Fromelles which contains the remains of 250 WWI soldiers and the mass grave at

Tuam, Co. Galway where 800 children are believed to have been interred. This presentation

explores how the advent of DTC DNA testing has turned ordinary citizens into Citizen

Scientists.

S28

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S29

What is the behavioural impact of personal genomic information, and why

does it matter?

Saskia C Sanderson

UCL

Research participants, as well as patients and consumers, arguably have the right to access

their own personal genomic data and to receive health-related information arising from the

data, including that about complex diseases. But what does this really mean, if anything, for

how complex diseases such as cancer and heart disease might be prevented, detected

and/or managed? One answer to this important question is that identifying people in the

general population who are at increased disease risk early on in their lives will lead to much-

needed improvements in primary prevention by empowering patients and their doctors to

take action to reduce their risk. Another answer is that using personal genomic information

alongside other ‘omic information about a person’s condition may usefully inform clinical

recommendations in secondary care. Whether these hopes are borne out will rely to a large

part on human behaviour: using genomics to identify “high risk” people or to inform

management strategies will only lead to improved disease prevention and treatment if the

patients and/or their clinicians believe this information is worth acting on, and if they are

subsequently able to make the necessary behavioural changes to reduce their risk. These

behaviours include medication initiation and adherence, screening, other tests and

procedures, as well as diet, exercise and other lifestyles. This talk covers what is currently

known about the impact of personal genomic information on people’s behaviours based on

existing empirical research (the ‘known knowns’) and what is likely to be learned over the

next few years (the ‘known unknowns’). This includes the behavioural impact of personal

genomic information based on (1) a single or a few common DNA variants with weak effects

on disease risk, (2) a single or a few rare DNA variants with strong or moderately strong

effects on disease risk, and (3) hundreds or thousands of common DNA variants that,

together with other ‘omics and health information, may have strong or moderately strong

effects on disease risk. The talk also explores how psychological and behavioural theories

can be applied to suggest possible explanations for past research findings, and pose

testable hypotheses for future studies. Given that many of the potential benefits (and

potential harms) of providing people with access to their personal genomic data, are

psychological and behavioural in nature, the talk concludes with discussion of the need for

truly interdisciplinary, collaborative and large-scale research on these questions going

forwards.

S30

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S31

Genomics England - treating data with care

Joanne M. Hackett

Genomics England

Genomics England has always placed the participant at the heart of everything they do. This includes working closely with the Participants Panel to help them understand the journey from whole genome sequencing to the return of results. Every step must be treated with care, but the data also needs to be accessed. Creating a Research Environment that is ‘read only’ is one way to ensure the genomic and clinical data is safely stored yet readily accessible to qualified academics, clinicians and industry.

S32

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S33

Health Data sharing and Data Protection Law in Africa: A South African perspective Ciara Staunton, Nóra Ni Loideain, Jantina de Vries School of Law, Middlesex University London & Centre for Biomedicine, EURAC Italy In recent years there has been exponential growth in genomic research in Africa. Fuelled by initiatives such as MalariaGEN, H3Africa and B3Africa, this has led to a dramatic increase in genomic data sharing between African countries and other international collaborators. South Africa has been a key player in many of these initiatives, and the importance of genomic research, bioinformatics and open science have been identified as key drivers in improving innovation and health outcomes by many government reports. These policymaking developments include the National Development Plan, the Bioeconomy Report, the Draft White Paper on Science, Technology and Innovation, and the Academy of Science of South Africa’s consensus study on Human Genetics and Genomics in South Africa: Ethical, Legal and Social Implications. The current governance of genomic data sharing in South Africa involves navigating a complex patchwork of laws comprising of the Constitution, various legislation, regulations, and guidelines. The Protection of Personal Information Act [No.4 of 2013] (POPIA) is the first comprehensive data protection regulation to be passed in South Africa and seeks to give effect to the constitutional right to privacy although it is not due to come into force until 2020. POPIA draws largely from an early draft version of the EU General Data Protection Regulation (GDPR) (enacted in 2016 with entry into force in 2018). Critically, however, unlike the GDPR, POPIA lacks any special provisions for research. Rather than clarify the regulation of genomic data, it introduces certain ambiguities. To be an effective and key international player in genomic research, a coordinated response between government, industry and academia within South Africa - that recognises international legal norms and best practice - is essential. In February 2019, a two-day workshop was convened in Cape Town, South Africa to discuss the governance of genomic data in South Africa. Approximately 30 legal, ethical and scientific experts were in attendance and were drawn from academia, industry and government. This interdisciplinary group identified challenges and gaps in the currently regulatory framework and pertinent issues that must be addressed, notably around broad consent, institutional oversight, compliance and enforcement, and alignment with international standards. A Position Paper from this workshop addresses the challenges and issues identified in this workshop, as well as the recommendations outlined in the position paper to ensure that regulations in South Africa foster the sharing of genomic data.

S34

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S35

Evaluating utility of patient-centered deep phenotyping

Monica Munoz-Torres, Melissa Haendel (1, 2), Nicole A. Vasilevsky (2), Julie A. McMurry (1), Chris Mungall (3), Catherine Brownstein (4), Ingrid Holm (4), Kent Shefchek (1), Sebastian Köhler (5), Peter N. Robinson (6).

(1) Oregon State University, Corvallis, OR, 97331, USA; (2) Oregon Health and Science University, Portland, OR, 97239, USA; (3) Lawrence Berkeley National Labortatory, Berkeley, CA, 94720, USA; (4) Boston Children's Hospital, Boston, MA 02115, USA; (5) Charité University Hospital, 10117 Berlin, Germany; (6) The Jackson Laboratory for Genomics Medicine, Farmington, CT 06032, USA.

The Human Phenotype Ontology (HPO) is the de facto terminology for clinical 'deep phenotyping' in humans. The HPO enables non-exact matching of sets of phenotypic features (phenotype profile) against known diseases, other patients, and model organisms and is a flagship of the Monarch Initiative. Algorithms based on HPO have been implemented into many diagnostic and variant prioritization tools and are used by the 100,000 Genomes project, the NIH Undiagnosed Diseases Program/Network, and thousands of other clinics, labs, tools, and databases worldwide. Patients are an eager and untapped source of accurate information about phenotypes - some of which may go unnoticed by the clinician. However, medical terminology is often perplexing to patients, making it difficult to use resources like HPO. To support use of HPO by patients, we created a 'layperson' translation of HPO. To determine the feasibility of using lay-HPO phenotyping in diagnostic tools, we evaluated lay-HPO in recalling correct diseases in phenotype comparison algorithms. We created synthetic profiles ('slim annotations') for each disease and compared them against the gold standard curated set. We also permuted these profiles by adding or removing annotations at random to determine how robust the lay annotation profiles might be in the face of missing or noisy data from patients. We then measured the semantic similarity between HPO gold standard annotations and the derived profiles (with and without noise added). 57% of profiles scored 80% similarity or higher, and 75% of profiles scored 70% similarity or higher. Preliminary analyses suggest that lay-HPO has the features required to be useful in a diagnostic setting, in that lay terms are: a) sufficiently specific and b) well-represented in our disease-to-phenotype database that is utilized by the aforementioned tools for differential diagnostics. This patient-friendly version of HPO uses the same infrastructure as the primary HPO, so patient-generated phenotyping data can readily be combined with clinical phenotyping data to improve variant prioritization. New patient-centered tools are being developed to help patients assist clinicians in creating robust computational phenotype profiles; improving these profiles can empower patients to be active participants in their diagnostic odysseys, potentially improving the accuracy and speed of their diagnosis. Finally, such tools can also assist in creation of patient generated phenotypic profiles for sharing in patient registries, forums, and on the Web for cohort definitions and community formation.

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S37

Personal genomes and the police: public opinion and ethical considerations

Christi J. Guerrini, JD, MPH1 Amy L. McGuire, JD, PhD1 Jill O. Robinson, MA1 Devan Petersen, MPH1 1 Baylor College of Medicine, Center for Medical Ethics and Health Policy, Houston, Texas USA On April 24, 2018, a suspect in California’s notorious Golden State Killer cases was arrested after decades of hiding in plain sight. Using a novel forensic approach, investigators identified the suspect—who was wanted for murdering at least a dozen individuals and raping at least 50 women—by first identifying his relatives using a free, online genetic database populated by individuals researching their family trees. The technique has since been used by U.S. law enforcement to identify dozens of other criminal suspects. Yet, concerns that police use of genetic genealogy databases might violate the privacy rights or expectations of their contributors (and family members) are persistent. Public opinion is a critical but thus far underdeveloped input to policy discussions regarding whether the police should be permitted to use genetic genealogy databases to generate investigative leads. To fill this gap, we conducted a survey of 1,587 individuals in May 2018 to assess their perspectives on forensic use of genetic genealogy databases. This presentation will report the survey’s findings of strong support of police access when the purpose is to identify violent criminals that was not predicted by age, race, ethnicity, annual household income, criminal experiences, or law enforcement employment. These findings will be discussed in the context of recent legal and ethical scholarship at the intersection of privacy and public safety. Finally, implications of these findings for policymakers will be suggested.

S38

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S39

Access, Storage, and Sharing of personal genomic information

Pascal Borry Centre for Biomedical Ethics and Law, Department of Public Health and Primacy Care, KU Leuven, Belgium The increasing availability of genomic information, within and outside the context of the traditional healthcare system, provides new opportunities for individuals to engage with this information. Individuals are now able to have their own genetic data interpreted by all kinds of third-party interpretation services, outside of a clinical context. Healthcare professionals will increasingly being challenged by requests from individuals to help interpret genetic information that was obtained outside a traditional context. The emerging possibilities for obtaining and storing genomic information and making it available to individuals, raise novel challenges with regard to the data privacy, storage and processing. In particular, processing genomic data may raise informational risks for the data subjects, their family members or specific groups. Previous studies have demonstrated that individuals are willing to share their personal data for research purposes. Data sharing by patients will be beneficial for both clinical and research purposes, and optimize the use of massive raw data that are generated. Nevertheless the ethical and legal grounds of emerging (for-profit and non-profit) web platforms for personal data sharing should be a subject for ongoing scrutiny,

S40

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S41

Accessing 1M Genomes transnationally across Europe by 2022

Gary Saunders, Thomas Keane, Jordi Rambla, Ilkka Lappalainen, and Serena Scollen

ELIXIR

Over the last forty years, we have seen the emergence of large cohorts of human samples from research and national healthcare initiatives. Many countries in Europe now have nascent personalised medicine programmes meaning that human genomics is undergoing a step change from being a predominantly research-driven activity to one funded through healthcare. This is evidenced by the recent Declaration of 19 European countries to sequence and share transnationally at least 1M human genomes by 2022. This initiative will catalyse the transition of genomics from the bench to bedside in Europe. We envisage that a significant subset of these data will be made available for secondary research. However genetic data generated through healthcare is not likely to be shared as widely as research data. Healthcare is subject to national laws, and it is often unacceptable for health data from one country to be exported outside regional or national jurisdictions. Our vision for the ELIXIR Federated Human Data Community is to create a federated ecosystem of interoperable services that enables population scale genomic and biomolecular data to be accessible across international borders accelerating research and improving the health of individuals resident across Europe. In this presentation we shall describe our work within the ELIXIR Federated Human Data Community which coordinates the delivery of FAIR compliant metadata standards, interfaces, and reference implementation to support the federated ELIXIR network of human data resources. The overall goal is to provide secure, standardized, documented and interoperable services under the framework of the European Genome-phenome Archive (EGA). We will describe our structured roadmap for the ELIXIR Nodes to join the EGA federated network by providing the necessary technical, logistical, and training coordination across the network. This project builds on earlier work in the ELIXIR-EXCELERATE, CORBEL, and Tryggve projects. It will be led by the European Genome-phenome Archive (EGA) to ensure work described in this proposal is aligned with the policies, legal agreements, and governance model for establishing the Federated EGA. We will also describe how this work builds on work in EXCELERATE to create a reference software implementation, the Local EGA, that Nodes can use to operate their federated node for the secure archival and for providing access to sensitive human research data. The result will be a coordinated bioinformatics infrastructure across Europe that enables the transnational access for approved researchers to 1M genomes by 2022.

S42

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S43

Genomics as a personalized medicine approach in disease risk prediction - P5.fi FinHealth

Heidi Marjonen, Minttu Marttila1, Teemu Paajanen1, Niko Kallio1, Ari Haukkala2, Helena Kääriäinen1, Kati Kristiansson1, Markus Perola1,3

1National Institute for Health and Welfare, Helsinki, Finland 2Faculty of Social Sciences, University of Helsinki, Finland 3Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Finland

In P5.fi we utilize polygenic risk scores to provide personalized information on the individual disease risk related to three common diseases (coronary heart disease, type 2 diabetes and venous thromboembolism) for 3.400 volunteering participants. We hypothesize that genetic risk information would improve prevention, diagnosis and treatment. We validated the polygenic risk scores in whole genome genotyped population based FINRISK cohorts (N=20.000) using Cox regression models. Follow up data from national health care registers allowed us to model the impact of genetic and traditional risk factors such as smoking, cholesterol, blood pressure and BMI on a person's risk of disease within the next 10 years. We observed that type 2 diabetes (T2DM) PRS significantly associates with the T2DM disease risk (HR:1.5 per 1 sd PRS, p-value:<2*10-16). Also the top 8% of the FINRISK population who had inherited the highest PRS had fourfold increased risk for T2DM. Moreover, people with >35 BMI and the highest PRS tend to get diabetes at younger age. By combining the systemic genetic analyses with more traditional disease risk factors in the FINRISK cohort, we produced estimates on the impact of PRS and selected covariates on risk of T2DM. We use these estimates to assess the future risk of T2DM in P5.fi FinHealth participants who will receive this disease risk information including genetic risk via a web portal. Our approach enables to identify the individuals within highest genetic risk and those with pre-disease symptoms. We will monitor the reception of the information by questionnaires and follow the participants for disease end points using registry data.

S44

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S45

Knowns and unknowns in genomic testing; a clinician’s eye view.

Dr Frances Elmslie Consultant Clinical Geneticist, President, UK Clinical Genetics Society, South West Thames Regional Genetics Service, St George’s, University of London Clinical Genetics Services have traditionally focussed on the diagnosis and management of rare diseases. With increasing reliance on whole genome and whole exome sequencing, genetics clinicians have become more closely integrated with scientific staff to aid in the interpretation of genomic data. Even in patients with a clear phenotype, correct interpretation of variants can present significant challenges. The availability of direct to consumer genomic testing has led to an influx of enquiries from primary care or even from the consumers themselves into genetics services, requesting help in the interpretation of genomic data. The agreed approach has been to prioritise those in whom a clearly pathogenic variant in a known disease-causing gene has been identified through these analyses, but as a result there will be individuals concerned about their genomic information who are unable to access support. Do publicly funded genetics services have a duty of care to these individuals? If so, how do services need to change? In my talk, I will present a number of case studies that illustrate the challenges and benefits of access to the new genomic technologies, and will consider how these can be safely integrated into routine clinical care.

S46

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S47

Embedding genomics into routine health care Reecha Sofat University College London, UK Rapid advances have been made in platform –omics technologies including but not limited to genomics, proteomics and transcriptomics. We are used to dealing with these data in an research environment and the breadth of utility is being demonstrated for example by large national biobanks. However what utility these technologies, in particular genomics will have in routine clinical care remains unexplored and untested. Embedding some of these practices into routine care may begin to yield answers to this. Moreover, how this complex data is handled within routine clinical care environments, how they are stored, accessed repeatedly through an individual’s life course to inform health remains unknown. AboutMe is an institutional initiative at University College London and University College London Hospital Foundation Trust which is beginning to answer these questions.

S48

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S49

Genetic privacy: friend or foe? Yaniv Erlich MyHeritage, Israel We generate genetic information for research, clinical care and personal curiosity at exponential rates. Sharing these genetic datasets is vital for accelerating the pace of biomedical discoveries and for fully realizing the promises of the genetic revolut ion. However, one of the key issues of broad dissemination of genetic data is finding an adequate balance that ensures data privacy. I will present several strategies to breach genetic privacy using open internet tools, including a systematic analysis of the strategy that implicated the Golden State Killer. Our analyses show that these strategies can identify major parts of the US population from their allegedly anonymous genetic information by anyone in the world. I will conclude my talk with practical suggestions to reconcile genetic privacy with the need to share genetic information.

P1

Profiting from sharing personal genomic data: A review of ethical concerns.

Eman Ahmed, Mahsa Shabani

Center for Biomedical Ethics and Law, Department of Public Health and Primary Care, University of Leuven, Leuven, Belgium.

In the recent years, some Direct-to-Consumer (DTC) genetic testing companies have

developed partnerships with third parties, such as pharmaceutical and biotech companies,

who are interested to have access to genomic data for medical research and drug

development purposes. Although the customers are mainly supporting research activities of

the DTC companies, for-profit nature of such data sharing raises some questions regarding

the rights of the data subjects and fairness in sharing benefits. In response, a new

generation of sequencing and data sharing companies such as Nebula Genomics are

emerging which aim for leaving the ownership and data control in the hands of each

individual customer. In particular, such business model allows individuals to receive various

types of monetary incentives to sequence their genome and share it with interested

commercial parties. Offering direct incentives to individuals for genomic data sharing may

seem beneficial, however, this needs to be in line with the overarching principles of

biomedical research and personal data protection. The pressing question here is how far

existing guidelines and policies regarding incentives in biomedical research should apply to

such data sharing by individuals for research purposes in exchange for free sequencing or

tokens? Also, the implications for withdrawal of consent and privacy rights of the individuals

after remuneration remain to be investigated. Moreover, the impact of such data sharing on

conventional ways of genomic data collection and sharing in biomedical research should be

scrutinized. In this paper, we offer a critical review of the associated ethical concerns that

may arise from for-profit genomic data sharing by the individuals and provide some points-

to-consider for future policy developments.

P2

Identification of a splice site mutation in DNAI1 gene in Multiplex Kuwaiti family with severe chronic respiratory symptoms of PCD with situs solitus.

Al-Mutairi DA [1],, Alsabah BH [2], Alkhaledi B[3], Pennekamp P [4], Omran H [4]

1 Department of Pathology, Faculty of Medicine, Health Sciences Center, Kuwait University, Safat, Kuwait. 2 Zain Hospital for Ear, Nose and Throat, Shuwaikh, Kuwait City, Kuwait. 3 Pediatric Pulmonary Unit, Al-Sabah Hospital, Kuwait.4 Hospital Muenster, Muenster, Germany

Introduction: Primary ciliary dyskinesia (PCD) is one of the congenital thoracic disorders

caused by dysfunction of motile cilia resulting in insufficient mucociliary clearance of the

lungs. Approximately 50% of all PCD patients have Kartagener syndrome, a triad of

bronchiectasis, sinusitis and situs inversus totalis. The overall aim of this study is to identify

causative mutated genes for PCD and CHD in the Kuwaiti population. Methods: A cohort of

multiple consanguineous PCD families was ascertained from Kuwaiti patients and genomic

DNA from the family members was isolated using standard procedures. The DNA samples

from all affected individuals were analyzed using whole Exome Sequencing technology and

Sanger sequencing method. Transmission electron microscopy (TEM) and

Immunofluorescence staining (IF) was performed for patient samples obtained by nasal

brushings in order to identify the structural abnormalities within ciliated cells. Here we

present one multiplex family from our cohort that has a splice site mutation in DNAI1 gene.

Results: Whole Exome sequencing show a homozygous splice site mutation in DNAI1 gene

(c.1311+2T>A) in Intron 13 that shared between the two affected sibling. Sanger sequencing

was performed for the patients and the parents and the results confirming the patients carry

a homozygous mutation and the parents are both carrier for the same mutation. In addition,

TEM for the patients show lacking of Outer Dynein Arms (ODAs). IF staining shows a

complete absence of DNAI1 protein. The expression of the other ciliary proteins such as

(GAS8, DNAH11 and RSPH9) were also tested by IF and found to be normally expressed in

this family. Conclusions: Splice site mutation in DNAI1 gene can cause severe symptoms of

PCD without affecting left/right body asymmetry as the patients have normal positions of the

internal organs that known as situs solitus. This study helped the PCD-families to get

confirmed diagnosis of PCD firstly by determining the defects in the cilia ultrastructure using

(IF and TEM) and then by mapping the disease mutations. Genetic screening is confirming

the type of ciliary defect for each family understudy.

P3

A Clonal Expression Biomarker Improves Prognostic Accuracy: TRACERx Lung

Dhruva Biswas*, Nicolai J Birkbak*#, Rachel Rosenthal, Crispin T. Hiley, Emilia L. Lim,

Krisztian Papp, Marcin Krzystanek, Dijana Djureinovic, Yin Wu, David A. Moore, Marcin

Skrypski, Christopher Abbosh, Maise Al Bakir, Thomas BK Watkins, Selvaraju Veeriah,

Gareth A. Wilson, Mariam Jamal-Hanjani, Arul M. Chinnaiyan, Patrick Micke, Jiri Bartek,

Istvan Csabai, Zoltan Szallasi, Javier Herrero, Nicholas McGranahan#, and Charles

Swanton#, on behalf of the TRACERx consortium.

At the point of cancer diagnosis, molecular biomarkers aim to stratify patients into precise

disease subtypes predictive of outcome independent of standard clinical parameters such as

tumour stage. Although prognostic gene expression signatures have been derived for many

cancer types, seldom have they been shown to improve therapeutic decision making, limiting

their clinical use. While intra-tumour transcriptomic heterogeneity (RNA-ITH) has been

shown to bias existing biomarkers, efforts to control for this biological parameter have not

been considered in biomarker development. Here, we analyse multi-region RNA-seq and

whole-exome data for 156 tumour regions from 48 TRACERx patients to explore RNA-ITH in

NSCLC. We show that chromosomal instability is a major driver of RNA-ITH, through the

generation of heterogeneous copy number events within tumours, and that existing

prognostic gene expression signatures are vulnerable to sampling bias. To address this

issue, we develop the Outcome Risk Associated Clonal Lung Expression (ORACLE) assay,

comprised of genes expressed homogeneously within individual tumours but

heterogeneously between patients. These genes are enriched in modules associated with

cell proliferation, such as mitosis and nucleosome assembly, that are often selected for

through copy number gain events occurring early in tumour evolution. Our approach to

identify “clonal” transcriptomic biomarkers in NSCLC overcomes tumour sampling bias,

improves survival risk forecasting over current clinicopathological risk factors, and may be

generalised to other cancer types, whilst revealing the early evolutionary selection of high

risk DNA copy number events driving poor clinical outcome.

P4

A highly admixed Kazakh personal genome of a complex history in Central Asia and the need for a set of representative ethnic and national genomes.

Madina Seidualy, Madina Seidualy1, Asta Blazyte1,2, Sungwon Jeon1,2, Youngjune Bhak1,2, Yeonsu Jeon1,2, Jungeun Kim3, Anders Eriksson4, Semin Lee1,2, Jong Bhak1,2,3,5

1) Korean Genomics Industrialization and Commercialization Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea 2) Department of Biomedical Engineering, School of Life Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea 3) Personal Genomics Institute, Genome Research Foundation, Cheongju 28160, Republic of Korea 4) Department of Medical and Molecular Genetics, King’s College London, London SE1 9RT, United Kingdom 5) Clinomics LTD, UNIST, Ulsan 44919, Republic of Korea

As a part of Pan Asia Population Genomics Initiative (PAPGI), many personal genomes

have been sequenced and compared. Until now there are multiple ethnic genomes analyzed

and published by PAPGI including the Chinese, Japanese, Koreans, Indians (Gujarati),

Pakistani (Pathan), Egyptians, and Malaysians. Central Asian genomes such as Kazakh can

provide highly admixed genome data for associating the genetic variants to phenotypic traits

and diseases. We have sequenced a Kazakh genome, which has a clear intertribal

admixture history. Analyses of this personal genome accomplished a highly heterozygous

individual's genomic composition reconstruction, which was supported by historical events.

We further carried out a further present-day Kazakh genome comparison with various

modern and ancient genomes to evaluate the impact of the ancient and recent admixtures.

As a result, we confirmed the expected heterozygosity, which proved to be high and

consisting of variants attributed to different continental groups. Heterozygosity was also

observed in the phenotypic trait, disease and pharmacogenomic profile determining variants.

We identified over 4 million SNPs, including 102,240 novel and 627 common functionally-

damaging variants. Phylogenetic analysis revealed the surrounding Central Asian

populations such as Kalmyk and Kyrgyz as genetically closest, however, a considerable

similarity to East Asians; Xibe, Korean, and Japanese suggested a complex admixture within

the continent of Asia. Overall, the biggest proportions of shared variants point towards fairly

recent admixtures traceable to the 16th -20th century. As a discussion point to various

personal genome projects across the world, researchers must consider how accurately they

can map the origins or ancestors of admixed samples, which is very difficult. To overcome

this problem, the construction of numerous ethnically and nationally representative genomes

utilized as anchors will enable us to efficiently dissect admixed personal genetic heritage.

P5

Elucidation of the phenotypic spectrum and genetic landscape in primary and secondary microcephaly

P Boonsawat, Paranchai Boonsawat1, Pascal Joset1, Katharina Steindl1, Beatrice Oneda1, Laura Gogoll1, Silvia Azzarello-Burri1, Frenny Sheth2, Chaitanya Datar3, Ishwar C. Verma4, Ratna Dua Puri4, Marcella Zollino5, Ruxandra Bachmann-Gagescu1, Dunja Niedrist1, Michael Papik1, Joana Figueiro-Silva1, Rahim Masood1, Markus Zweier1, Dennis Kraemer1, Sharyn Lincoln6, Lance Rodan6,7, Undiagnosed Diseases Network, Sandrine Passemard8,9, Séverine Drunat9, Alain Verloes9, Anselm H.C. Horn10, Heinrich Sticht10, Robert Steinfeld11, Barbara Plecko11, 12, Bea Latal13, Oskar Jenni13, Reza Asadollahi1, Anita Rauch1,14,15

1Institute of Medical Genetics, University of Zurich, Schlieren-Zurich, Switzerland 2FRIGE's Institute of Human Genetics, FRIGE House, Satellite, Ahmedabad, India 3Sahyadri Medical Genetics and Tissue Engineering Facility, Kothrud, Pune and Bharati Hospital and Research Center Dhankawadi, Pune, India 4Institute of Medical Genetics & Genomics, Sir Ganga Ram Hospital, Rajinder Nagar, New Delhi, India 5Institute of Genomic Medicine, Catholic University, Gemelli Hospital Foundation, Rome, Italy 6Division of Genetics and Genomics, Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts, USA 7Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts, USA 8Service de Neuropédiatrie, Hôpital Universitaire Robert Debré, APHP, Paris, France 9Département de Génétique, Hôpital Universitaire Robert Debré, APHP, Paris, France 10Division of Bioinformatics, Institute of Biochemistry, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany 11Division of Pediatric Neurology, University Children’s Hospital Zurich, Zurich, Switzerland 12Department of Pediatrics and Adolescent Medicine, Division of General Pediatrics, Medical University of Graz, Austria 13Child Development Center, University Children’s Hospital Zurich, Zurich, Switzerland 14Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland 15Zurich Center of Integrative Human Physiology, University of Zurich, Zurich, Switzerland

Introduction: Microcephaly is a sign of many genetic conditions but has been rarely

systematically evaluated. We therefore comprehensively studied the clinical and genetic

landscape of an unselected cohort of patients with microcephaly.

Materials and Methods: We performed clinical assessment, high-resolution chromosomal

microarray analysis, exome sequencing and functional studies in 62 patients (58% with

primary microcephaly (PM), 27% with secondary microcephaly (SM), and 15% of unknown

onset).

Results: We found severity of developmental delay/intellectual disability correlating with

severity of microcephaly in PM, but not SM. We detected causative variants in 48.4% of

patients and found divergent inheritance and variant pattern for PM (mainly recessive and

likely gene-disrupting (LGD)) versus SM (all dominant de novo and evenly LGD or

missense). While centrosome-related pathways were solely identified in PM, transcriptional

regulation was the most frequently affected pathway in both SM and PM. Unexpectedly, we

found causative variants in different mitochondria-related genes accounting for ~5% of

patients, which emphasizes their role even in syndromic PM. Additionally, we delineated

novel candidate genes involved in centrosome-related pathway (SPAG5, TEDC1), Wnt

signaling (VPS26A, ZNRF3) and RNA trafficking (DDX1).

Conclusions: Our findings enable improved evaluation and genetic counseling of PM and SM

patients and further elucidate microcephaly pathways.

P6

Ethical dilemmas and data sharing in genetic genealogy

John Cleary

Associate Professor, School of Social Sciences, Heriot-Watt University Edinburgh

A number of events in 2018-19 have put the data-sharing methods of genetic genealogy into

the public spotlight forcing open largely undiscussed issues about privacy and the ethics of

publishing personal DNA data of oneself and relatives. There is an expectation of reciprocal

sharing and shared genealogical purposes attached to using these company databases.

Revelations that these databases have been used by law enforcement agencies, chiefly in

the USA, to identify unknown murder victims and to apprehend suspects of violent crimes

have led to a growth of anxiety among the customer base which has been explored through

a series of interviews with lead players in this debate on their perceptions of the ethical

hazards involved and what remedies may be found. The analysis covers: (1) whether

privacy is actually compromised if the genomic data of individuals is not revealed; (2)

whether social benefits might justify the actions permitted by certain testing companies; (3)

how they may perceive the risk of ‘mission creep’ if tolerance of such usage for crimes of

violence may see it extend to other forms of criminal behaviour, and the effect that may have

on public support. As a result, we recommend that the testing companies all take

approaches that strongly foreground the principle of informed consent in order to avoid

potential long-term harm to their business models.

P7

The logical philosophy of Investigating the Challenges of Genetic Bioethics in the use of Pig Derivatives in Medical Manufacturing and its possible impact on personal human inheritance of genes

Gihan E-H Gawish, MSc, PhD, PostDoc-UBC Fellow, Female Section of Saudi Scientific Society for Juristic Medical Studies, IMSIU, Riyadh-SA

Ass Prof of Medical Biochemistry, Molecular Genetics and Cancer Genetics Member of the Board Directors & Supervisor of Female Section of Saudi Scientific Society for Juristic Medical Studies, IMSIU , Medical Biochemistry Department, College of Medicine and Medical Services. Al-Imam Muhammad Ibn Saud Islamic University, Riyadh-SA Member of the Board Directors of SSCC-SCFHS Founding Member of the Genome Research Chair (Former), KSU Medical Laboratories Specialties, mohp-eg & scfhs-sa https://imamu.academia.edu/DrGihanGawish gagawish@imamu.edu.sa +996553101340

The world's pork manufacturing revenue is $ 500 billion a year While Muslims and Jews

represent more than a quarter of the population of the earth, which means that it is inevitable

to examine the current status of food and medicine based on the prohibition in the heavenly

books to investigate if these derivatives could affect the personal human genes inheritances.

The residues genes of the pig in the presence of an appropriate environment of its viruses

resulting from the manufacture may to have an impact on the genetic content of the human

being. The objective of the philosophical examination of influences is not inconsistent with

the existing economy but is to find industrial solutions to preserve the moral legacy of human

genetic material in the event that eating a food or medicine containing the residues of taboos

from the sequences of nucleotides with the remnants of pigs with viruses may be a tool to

destroy parts of human genetic heritage or replace parts of them over the decades or at

least it could enhance the genotoxicity and encourage cellular deviation to carcinogensis.

Especially that these industries are modern and began in the sixties, and no research has

been carried out to ascertain their long-term bad consequences and how to avoid them. In

2014 when the Department of Islamic Development in Malaysia investigated samples of

Cadbury and they found parts of the pig's genetic material in chocolates. This prompted me

to make a comparison between the physical characterization of the pig's DNA and the

industry's impact on it in extraction, pressure, heat, cracking and purifications. And those

associated with the containment of biological residues such as viruses or vital compounds.

These residues may not be affected by pressure and industrial heat, and others. The impact

of which should be examined for the future of natural genetic replications in the human cells

to save their physical, chemical and ethical inheritances. My study is focusing in how we

could update the industrial process to protect personal human genome from any

transformation related to industrial development.

P8

Identifying persons unknown using genetic genealogy - a review of the methodology

Maurice Gleeson, Education Ambassador, International Society of Genetic Genealogy

The arrest of the alleged Golden State Killer in California in April 2018 created a media

storm that has never fully abated. Since then over 25 “cold case” criminal suspects have

been arrested as a result of the application of genetic genealogy techniques. The power of

these techniques to help solve “cold cases” has caught the attention of forensic scientists

and law enforcement agencies across the world, highlighting the power of these genetic

genealogy techniques to solve cases where standard forensic techniques have failed. The

genetic genealogy techniques used to identify these criminal suspects are rooted in adoptee

research that the genetic genealogy community has been engaged in for the past 12

years. There are many people who don't know the identity of one or both of their parents,

among them adoptees, foundlings, and donor-conceived children. For these people there is

frequently a desire to track down their biological relatives, learn about their roots, and forge

relationships with new family members. Another frequent objective is to acquire vital

information about medical histories that may impact on the individual's own health or that of

their children. Following the availability of commercial autosomal DNA tests in 2007

(specifically autosomal SNP microarray genotyping), many people realised that they could

potentially help adoptees trace their birth families. This was particularly important in those

US states where adoption files were not open to the public. As a result a whole new industry

in adoptee research was created. This presentation summarizes the genetic genealogy

methodology used to identify unknown persons, whether they be adoptees,

foundlings, unidentified murder or accident victims, unidentified human remains, rapists or

killers. The technique can be broken down into the following steps:

1) identify close genetic matches to the person of interest

2) cluster them into groups of Shared Matches

3) identify or build family trees for the members of each cluster

4) use these family trees to triangulate back to a common ancestor for each cluster

5) trace forward from the ancestral couples until one cluster’s descendants intersect with

another

6) use profiling to narrow down the potential candidates for the target person (or their

parents)

7) perform further targeted DNA testing to confirm the relationship

P9

ELIXIR: Providing a coordinated European Infrastructure for managing Human Genomics Translational Data and Services

Jen Harrow, On behalf of ELIXIR

ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK

ELIXIR unites Europe's leading life science organisations in managing and safeguarding the

increasing volume of data being generated by publicly funded research. It coordinates,

integrates and sustains bioinformatics resources across its member states and enables

users in academia and industry to access services that are vital for their research. There are

currently 22 countries involved in ELIXIR, bringing together more than 200 institutes and 600

scientists.

ELIXIR's activities are coordinated across five areas called 'Platforms', which have made

significant progress over the past few years. For instance, the Data Platform has developed

a process to identify data resources that are of fundamental importance to research and

committed to long term preservation of data, known as core data resources. The Tools

Platform has services to help search appropriate software tools, workflows, benchmarking as

well as a Biocontainer's registry, to enable software to be run on any operating system. The

Compute Platform has services to store, share and analyse large data sets and has

developed the Authorization and Authentication Infrastructure (AAI) single-sign on service

across ELIXIR. The Interoperability Platform develops and encourages adoption of

standards such as FAIRsharing, and the Training Platform helps scientists and developers

find the training they need via the Training e-Support System (TeSS).

The Beacon Project is an open sharing platform that allows any genomic data centre in the

world to make its data discoverable. The project is a first-of-its kind effort to make the

massive amounts of life sciences data being collected in healthcare and research settings

around the globe accessible and is being supported and funded by ELIXIR. To date, 70

beacons have been "lit," including seven in the UK and another nine across Europe, allowing

users unprecedented discovery of genomic variants in national and international cohorts.

The Authentication and Authorisation Infrastructure (AAI) provides a centralised user identity

and access management service (ELIXIR AAI). ELIXIR AAI will be used to access the

European Genome-Phenome Archive (EGA) resources and ELIXIR is working with the

GA4GH to have ELIXIR AAI approved as a standard. The focus now for ELIXIR Human

Genomics and Translational Data is to establish a federated suite of EGA services across

Europe, coordinating the national roadmaps and large EU projects to enable population

scale genomic, phenotypic, and biomolecular data to be accessible across international

borders.

P10

Mutational dynamics in the mouse mitochondrial genome

Maribel Hernández-Rosales Conacyt-Institute of Mathematics, UNAM, Juriquilla; Alfredo Varela-Echavarria, Institute of Neurobiology, UNAM Juriquilla.

In the cell there are from hundreds to thousands of mitochondria. Mitochondrial mutant genomes can coexist with wild-type genomes. Mutations in the mitochondrial genome have been associated to several diseases, such as aging, Alzheimer’s disease, Parkinson’s disease, some forms of cancer, infertility, neuromuscular disorders, etc. In this work, we address the following questions: what is the mutation load in the mitochondrial genome? does the mutation load change in the mouse brain in different stages of life? does the frequency of individual mutations change in different stages of life? how are mutations distributed in the mitochondrial genome? I will show preliminary results of this study in the mouse mitochondrial genome that will give us insights about the mutational dynamics in the human mitochondrial genome.

P11

Issues and experience in incorporating Personal Genome Project principles into Asian genome projects: the need for standardized protocols using future technologies

Sungwon Jeon, Sungwon Jeon1, Asta Blazyte1, Sungwoong Jho2, Dan Bolser3, and Jong Bhak1,2,4*

1) KOGIC, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea 2) Personal Genomics Institute, Genome Research Foundation, Cheongju 28160, Republic of Korea 3) Solidi, Cambridge Judge Business School, Trumpington Street, Cambridge CB2 1AG 4) Clinomics LTD, UNIST, Ulsan 44919, Republic of Korea jongbhak@genomics.org

Various Personal Genome Project concepts have been around for decades and the best

known project is PGP by George Church group at Harvard. Implementing detailed PGP

principles to other nations and cultures have serious issues. Here, we introduce several PGP

principle-adopting Asian genome projects and share the problems and critical assessment

on implementing the broadly democratic principles of PGP.

The first one - Personal Welfare Genome project in Korea recruited 1,000 healthy Koreans

for three years. We provided a free health check-up resulting in 111 phenotypical assays

and answers from 160 health related questionnaires through a private hospital providing a

genetic counselling. It was funded by the government and participants reported a high level

of satisfaction. However, it was impossible to implement a consent on sharing the genome

information openfreely outside the project.

The second project is a 10,000 Korean human genome project which collected 2,400

genomes for two years. In this case, the genomic data could be shared only if traditional

human sample access procedure is followed. In the end, the personal genome data will be

shared only in a secured cloud environment if de-identification rule is implemented.

The last PGP principle associated project is PAPGI, Pan Asian Population Genomics

Initiative, aimed to gather a wide variety of Asian personal genomes. The main problem of

this is that each nation's research and regulation environment is different, therefore, it is

impossible to implement any standardized data deposition and sharing.

Major issues: Privacy, ethnics, and legal regulations issues need to be overcome robustly.

We need to dissociate the scientific data from medical data because, currently, any

sequencing-based data are regarded medical and diagnostic automatically mostly controlled

by medical authorities. Human rights of knowing and sharing or deleting ones' personal

genomic data should be recognized. Each individual must take his responsibility of acquiring,

storing, and sharing at her/his own risk by becoming the center of his own genomics. The

society must recognize each individual's rights on free and unlimited usage of scientific and

biological genome data.

P12

KPGP: the Korean Personal Genome Project towards Personal Reference Genomes Era

Sungwoong Jho, Sungwoong Jho1, Jungeun Kim1, Sungwon Jeon2, and Jong Bhak1,2

1Personal Genomics Institute, Genome Research Foundation, Cheongju, 28190, Republic of Korea 2 KOGIC, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea

KPGP or PGP-Korea is the first and longest-lasting Korean genome project1. It was initiated

by the Korean Bioinformation Center (KOBIC) in 2006 to characterize ethnicity-relevant

variome of Koreans. It has three major goals. The first is to provide personal genome data to

the public by democratizing genomic information in Korea. The second aim is to build the

Korean reference genome (KOREF) that is not only of one person's but also of the public.

KOREF is unique because it has both single genome (KOREF_S) assembly and population

consensus assembly (KOREF_C)2. The third goal is developing Korean population variome,

KoVariome3. It aims to provide as much genomic information as openfreely as possible.

Since KPGP published the first Korean genome data in 20094, the number of openfreely

accessible complete genomes of KPGP database has reached to 111 personal genomes as

of 2018. This database was used to construct the first consensus Korean Reference genome

standard (KOREF_C) and KoVariome. These genome data was the first of its kind that were

generated under standard reference construction protocol as a joint project of National

Center for Standard Reference Data of Korea. KoVariome contains 12.7 M SNPs and 1.7 M

small indels from 50 unrelated healthy Korean individuals in the KPGP cohorts in 2018, and

the number of samples reached to 80 currently. The KoVariome in 2019 will contain 300

Korean samples.

P13

Structural variant calling by assembly in whole human genomes: applications in hypoplastic left heart syndrome

Matthew Kendzior, Sparsh Agarwal, Dave Istanto, Xiyu Ge, Xiaoman Xie, Zach Stephens, Jacob Heldenbrand, Timothy Olson, Jeanne Theis, Jared Evans, Eric Wieben, Liudmila Mainzer, Matthew Hudson

National Center for Supercomputing Applications: Matthew Kendzior, Sparsh Agarwal, Jacob Heldenbrand, Liudmila Mainzer, Matthew Hudson University of Illinois at Urbana-Champaign: Dave Istanto, Xiyu Ge, Xiaoman Xie, Zach Stephens Mayo Clinic: Timothy Olson, Jeanne Theis, Jared Evans, Eric Wieben

Alignment-based variant calling leaves many variants undetected, particularly structural

variants. Reads originating from large insertions or highly repetitive sequences may not map,

or map incorrectly. Large deletions (hundreds to thousands of nucleotides) can be left

unidentified. This problem can be remedied through variant calling by assembly. We used

Cortex-Var, a program that creates de Bruijn graphs for input samples and looks for

divergence among them within a population, or relative to a reference. This is a complex

multistep process that is difficult to deploy, and requires high performance, large memory

compute nodes. To aid in this process, we have developed a fully-automated solution using

Nextflow, an advanced workflow management system. We applied this workflow to high-

coverage whole genome sequencing data from 24 family trios, each containing a proband

affected with hypoplastic left heart syndrome (HLHS), a critical congenital heart defect with

poorly understood genetic underpinnings.

Current research into the etiology of HLHS has identified mutations in candidate genes

functioning in embryonic heart development. However, most of these are single nucleotide

variants (SNVs) that are present in few individuals. Current opinion based on statistical

genetic studies is that HLHS is unlikely to be caused by a small number of large-effect

variants, but rather a combination of alleles in affected individuals that result in the HLHS

phenotype. Many of these alleles are may reside in non-coding regulatory regions, that

would also be undetected by targeted exome sequencing alone. Using our cortex-var

workflow, we have identified a number of large structural variants in individuals affected with

HLHS.

We annotated variants based on location relative to genes and regulatory elements related

to congenital heart disease and embryonic heart development and found many that have

annotation related to a potential role in HLHS. These include structural variants removing

transcription factor binding sites within introns of NOTCH1, a gene previously implicated in

HLHS; variants affecting exons of genes important to embryonic cardiac development; and

variants in fetal cardiac enhancer regions identified through ChIP-Seq. We compared the

frequency of the variants in the probands versus the parents to estimate the likelihood that a

variant is de novo or inherited. Successful application of our workflow will enable faster and

cheaper detection of variants not only contributing to HLHS but also of other complex

diseases.

P14

Complex, Challenging Variants are a Significant Fraction of the Pathogenic Variants in Patients: Implications for Clinical WGS

Stephen Lincoln [1], Andrew Fellowes [2], Shazia Mahamdallie [3], Shimul Chowdhury[4], Eric Klee [5], Justin Zook [6], Rebecca Truty [1], Marc Salit [7], Nazneen Rahman [3], Stephen Kingsmore [4], Robert Nussbaum [1], Matthew Ferber [5], Brian Shirts [8]

. Invitae, San Francisco, USA, 2. Peter MacCallum Cancer Centre, Melbourne, Australia, 3. Institute of Cancer Research, London, UK, 4. Rady Children's Institute for Genomic Medicine, San Diego, USA, 5. Mayo Clinic, Rochester, USA, 6. National Institute of Standards and Technology, Gaithersburg, USA, 7. Stanford University, Palo Alto, USA, 8. University of Washington, Seattle, USA

Next-generation sequencing (NGS) is a capable technique for detecting single nucleotide

variants and small indels in relatively accessible parts of a patient's genome. However,

conventional NGS methods have important limitations. An analysis of over 200,000 patients,

tested using sensitive methods, showed that variants of other, technically challenging types

comprise between 9 and 19% of the reportable pathogenic findings, depending on clinical

indication. Approximately 50% of these variants were of challenging types (large indels,

single exon CNVs, etc.), 20% were in challenging genomic regions (homopolymers, non-

unique sequences, etc.), and 15% were in poorly covered regions. A further 15% presented

multiple challenges. These data have been deposited into ClinVar and prevalence data are

being made available.

It can be difficult to evaluate the sensitivity of DNA sequencing methods for such challenging

variants. The most recent AMP/CAP guidelines for clinical NGS bioinformatics [Roy et al.,

JMD 2018] recommend that validation studies include samples containing enough variants

of each type to achieve statistical significance, a goal that is difficult to achieve for complex

variants given the relative scarcity of positive controls.

As proof-of-concept for one potential method to address this issue, we developed a synthetic

specimen containing 22 challenging variants of diverse types in commonly tested genes

from the ACMG 59 list. This specimen was sequenced using 10 validated NGS tests by an

international group of collaborating laboratories: only 10 of the 22 challenging variants were

detected by all tests, and just 3 tests detected all 22. Some of these limitations were not

known to the respective laboratory directors, demonstrating the utility of this Most but not all

of the limitations appeared to be bioinformatic in nature.

We believe that both our prevalence data and control specimens such as ours may be a

valuable asset to improve the performance of genome sequencing in medical practice.

P15

The Personal Genome Project UK – An Update

Ismail Moghul1 & José Afonso Guerra-Assunção2 on behalf of the PGP-UK Consortium

1Medical Genomics, UCL Cancer Institute, University College London, UK; 2BLIC, UCL

Cancer Institute, University College London, UK

The Personal Genome Project UK (PGP-UK) is dedicated to making genome, health, and

trait data publicly available under an ethically approved, open-access and open-consent

model. Participant enrolment for the PGP-UK is an extensive process, where participants

have to demonstrate a thorough understanding of the risks involved in taking part in a project

of this nature by completing an online, multiple-choice exam.

To date, over 150 datasets (600+ data files) have been released from the project, including a

multi-omic pilot study of ten participants and a further 100 whole genome sequencing

datasets. The PGP-UK currently includes data produced from whole genome sequencing

(n=101), whole exome sequencing (n=2), whole transcriptome sequencing (n=20), whole

genome bisulphite sequencing (n=10) and genome-wide methylation arrays (n=23).

The entire PGP-UK dataset is freely available for download from public repositories (ENA,

EVA and ArrayExpress) with no access restrictions. Links to all datasets are provided on the

PGP-UK website (www.personalgenomes.org.uk). Basic phenotype data, which includes

self-reported age, sex, smoking status, etc, can be found on the project’s data web page

(www.personalgenomes.org.uk/data), alongside with genome and methylome reports,

generated by the PGP-UK.

Furthermore, all of the data and associated metadata are available through the PGP-UK

API. The API is compliant with the Open API Specification 3.0 and is documented at

www.personalgenomes.org.uk/api.

Data from the pilot study is available on the Seven Bridges Cancer Genomics Cloud, which

offers various tools and workflows for genomic and epigenomic data analysis. The entire

PGP-UK data is available on the Lifebit's Open Data Project (opendata.lifebit.ai/table/pgp),

where data can be exported to Lifebit’s cloud-computing platform Deploit (deploit.lifebit.ai) in

order to run custom pipelines.

In addition to generating open-access multi-omics data, we have developed an open source

iPad app, call ‘GenoME’. This app allows users to explore the personal genome and

epigenomes of four PGP-UK participants. As well as acting as a valuable educational tool,

this app explores novel methods of returning epigenomic data to participants for the first time.

P16

Robust governance for sustainable sharing of genomic data

Guro Meldre Pedersen, Vibeke Binz Vallevik, Sharmini Alagaratnam

DNV GL, N-1363 Høvik, Norway

The successful clinical implementation of precision medicine allows patient information from

a wide range of sources along the course of the patient journey to be combined for medical

decision support. More precise diagnosis and intervention requires comparing patient data

with a backdrop of population data, which in turn requires access to and sharing of

aggregated data, ideally across entities and borders.

Sharing of genomic data is technically challenging and requires interoperability, data

standardization and harmonization, as well as focus on data quality and security. Resolving

the technical bottlenecks for sharing of genomic data must be accompanied by adequate

governance of data, integrating regulatory, organizational and individual needs related to

data capture, aggregation, storage, access and sharing. Ideally, data governance will

recognise the individual's ownership of health data and balance privacy needs as guided by

the GDPR and national regulations with individual and societal benefits of data sharing.

Through the Norwegian Research Council funded project BigMed, and working with leading

Nordic clinical genetic labs in the Nordic Alliance for Clinical Genomics, we have developed

the Trusted Variant eXchange (TVX). The TVX is a concept for facilitation of safe sharing of

quality assured variant classifications between trusted partners of choice. The project has

allowed us to test and demonstrate technical solutions while exploring legal challenges in

precision medicine specific to our case with Norway's top legal experts. In this talk, we will

share our experiences on governance needs piloted through the TVX concept and discuss

needs for sharing of more complex genomic data in increasingly complex digital patient

pathways.

Driven by our mission to safeguard life, property and the environment and building on more

than 150 years of experience in combining technical domain knowledge, risk management

and quality assurance, DNV GL is working with stakeholders to understand needs related to

governance and sharing of genomic data. Being fully owned by an independent foundation,

DNV GL is a disinterested party to the data itself who aims to bring together producers and

users of data to establish robust and sustainable models for data sharing.

P17

MyEyeSite: a feasibility study and prototype for a patient-owned repository of rare-

disease clinical and genetic data using inherited retinal disease as a paradigm

Nikolas Pontikos1, Rose Gilbert1, Gavin Arno1, Rodrigo Young1, Nick Nettleton3 and Andrew

R. Webster1

1UCL Institute of Ophthalmology, 11-43 Bath Street, London EC1V 9EL, United Kingdom; 2Moorfields Eye Hospital, 162 City Road, London EC1V 2PD, United Kingdom; 3Loft Digital,

19-21 Christopher Street, London EC2A 2BS, United Kingdom

Rare diseases affect approximately 7% of the population. For these, it is harder to pool data

for research purposes, as, unlike other common disorders, the pertinent data is highly-

specialised, embedded and inaccessible within hospital networks (images, radiographs,

electrophysiology, genetic diagnosis). How then do we collate person-specific clinical

information from multiple locations and over time for the purposes of patient care and

research? A standard strategy might be to link data within the NHS Data Spine, and then

access the data en masse for research. This is technically challenging and ethically difficult

without explicit patient consent.

MyEyeSite will explore a different approach – give the job to the patient. Our unique insight

is to start with highly motivated patients and their medical community, within a specific

disease group, and support them with new, accessible technology. Here we apply to

undertake a feasibility appraisal and prototype of a suite of applications that will:

● facilitate subject-access requests from patients to hospitals for disease-appropriate

data

● provide a framework for hospitals to respond efficiently to such requests,

● allow patients to access their own data in an informative way, robust to sight-

impairment

● provide pooled data on consented patients for research purposes.

As part of our approach we will be educating patients about their data and how it can be

used for research and improvement of their clinical care.

P18

Validating the Key Implications of Data Sharing (KIDS) framework for the pediatric

infrastructure sciences in Canada: a policy Delphi study

Vasiliki Rahimzadeh1,2, Gillian Bartlett2, Bartha Maria Knoppers1

1. Centre of Genomics and Policy, McGill University 2. Department of Family Medicine,

McGill University

BACKGROUND: The informational feedback loops driving clinical progress in the

genomicsenabled earning health systems rely on the production, use and exchange of data,

including from children. The policies and practices guiding proportionate governance of such

production, access and exchange are, however, markedly lacking in the pediatric genomics

space. Despite the researchcare nexus that genomics-enabled learning health systems

afford, the respective ethical-legal traditions circumscribing appropriate oversight of data

sharing in clinical research and care remain separate and distinct in Canada. The need for

policy-practice coherence in genomic data sharing can be accentuated when involving

populations such as children, for whom such data may require special protections. Absent

understanding the ethical-legal bases upon which responsible pediatric data sharing rests,

present and future children may not reap the benefits of a healthcare system that continuous

‘learns’ from the production, use and exchange of genomic and associated clinical data.

METHODS: A systematic review of reasons was combined with policy Delphi to develop the

Key Implications of Data Sharing (KIDS) framework for pediatric genomics. The results of

the latter will be discussed in depth in this presentation. Thematic content, and descriptive

statistical analyses were used to understand how 12 Canadian pediatricians, genomic

researchers, ethicists and bioethics scholars prioritize the ethical-legal, social and scientific

policy positions outlined in the KIDS framework. RESULTS: The panel reached consensus

on 9 of 12 policy positions. Discrepant views related to informational risks, data access and

oversight of anonymized versus coded genomic data were primary sources of dissention.

CONCLUSION: This policy Delphi makes two contributions to the theory and practice of

responsible data sharing involving children in Canada. It suggests that skepticism of data

anonymization drives support for more stringent access controls and oversight when data

involve children. Greater emphasis on data accountability—coupled with data security—

could serve as more effective policy levers to preserve patient trust in data sharing in light of

rapid computational, and ensure children remain at the forefront of genomic innovation.

P19

The Heart Hive - a scalable solution for 21st century cardiovascular research

Angharad M Roberts, Rachel J Buchan, Sarah Chopping, Nichola Whiffin, Paul J R Barton, Stuart A Cook, James S Ware

Imperial College London, UK Royal Brompton & Harefield NHS Trust, UK

Our vision: All patients should have the opportunity to participate in research into their

condition, to advance knowledge and treatment. We are growing an online community to

connect willing research participants with active researchers and projects.

The challenge and opportunity: Inherited cardiovascular disease affects over 1 in 200

people, and is progressive. Tremendous advances have been made in understanding the

molecular basis and clinical manifestations. However, little is known about why disease

expression and clinical outcomes are so variable. Adequately-powered, systematic study is

needed to characterise the contributions of both common and rare genetic variation to

disease risk, and as modifiers of disease expression.

Patients living with cardiovascular disease want to participate in research. They also tell us

that this is difficult as research opportunities are clustered around certain centres.

Researchers also face major challenges in recruiting eligible patients and maintaining patient

engagement. Contemporary genetics requires large cohorts for well-powered studies; these

are beyond the reach of single centres and even stretch traditional collaborative networks. At

the other end of the spectrum, stratified approaches might demand recruitment of individuals

with a very specific set of phenotypic characteristics . Access to a large pool of individuals

allows for identification of a rare subset.

The solution: Give everyone the opportunity to participate in research. We are reaching

research participants through patient groups, social media and an engaging online presence.

Patients enrol in our study through an ethically-approved, fully-online and self-directed

consent interface. DNA for genetic analysis is be collected remotely using saliva kits

distributed by post.

Participants have full control of their own data, and which researchers can use it, through a

dynamic and interactive online consent process. Any researcher can offer an ethically and

scientifically approved study to the Heart Hive community through this unbiased platform.

Subsequent studies will contribute to a growing and sustainable cumulative resource of data

and experience that will transform the landscape.

The Heart Hive represents a scalable and effective solution for 21st century medical

research. It is a strategy applicable not only to these specific cardiac conditions, but across a

much broader range of medical research. By empowering patients to participate from home,

to control their own data and by sharing this online resource with the scientific community we

can bring together the large cohorts needed for modern genetic research, and generate a

cumulative collaborative resource with contributions from multiple researchers.

P20

Data Use Ontology: Classifying data access conditions for genomic data

Dr Dylan Spalding, The European Genome-phenome Archive The GA4GH DURI workstream

European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD,UK; Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), Barcelona, Spain Global Alliance for Genomics and Health Data Use and Researcher ID workstream, https://ga4gh-duri.github.io

Accessing personal genomic data while ensuring the individuals' consent is respected

requires the proposed research use to be compared to the consented use. This is usually

done manually and in free-text which generates inconsistency across different data

stewards. To expedite applications and facilitate data access, the Global Alliance For

Genomics and Health (GA4GH) Data Use Ontology (DUO) has been developed by the Data

Use and Researcher Identities (DURI) workstream to standardise the way these data access

conditions are categorised. DUO helps to ensure consistent understanding of data use

conditions amongst data stewards, so that the conditions can be applied congruently.

Additionally, by using a standardised terminology, data can be discovered based on possible

research use, improving data screening. To ensure the correct definition of a DUO term is

applied, DUO is versioned and available as a machine-readable file using the W3C standard

OWL Web Ontology Language Standard. DUO is updated

centrally and released using a PURL-based URI: users can use the latest version of the

ontology which is browsable at http://purl.obolibrary.org/obo/DUO_0000001 or downloadable

from http://purl.obolibrary.org/obo/duo.owl. Due to the structured nature of an ontology,

algorithms such as DUOS have been developed to determine access decisions without

human intervention, speeding up the application process. As well as DUO, work on

Researcher Identities is ongoing with the aim to allow fully automated application and data

access, based on the researcher's identity and proposed data use, providing faster data

access and resulting in more efficient research outcomes. As a GA4GH driver project, the

European Genome-phenome Archive (EGA) now supports DUO to tag the data use

conditions to datasets, and is working with the DURI workstream and ELIXIR to implement

Researcher Identities. EGA has been working with the Wellcome Trust Sanger Institute to

apply these codes to existing and new datasets, and the EGA now recommends new

datasets are submitted using DUO while working with submitters to enhance uptake.

P21

Storing and sharing personal genome variant and phenotype data in LOVD3

Peter EM Taschner1, Stephen Pieterman1, Ivo FAC Fokkema2, Marjolein Kriek3

1Leiden Centre for Applied Bioscience, University of Applied Sciences Leiden, 2Department of Human Genetics and 3Department of Clinical Genetics, Leiden University Medical Center, Leiden, Nederland

Personal exome and genome sequencing is already provided by commercial companies.

Storing and sharing variant information from personal genomes can be challenging. The

free, open-source, platform-independent Leiden Open-source Variation Database software

(LOVD, http://www.LOVD.nl) has been developed to build standardized databases for

curating and sharing gene variants(1). The latest version, LOVD3, is compatible with the

Gen2Phen data model, implemented with additional tables for phenotype, screening and

transcript information. Genome-wide sequence variant data can be stored in a single LOVD

installation using chromosomal nucleotide positions as reference. Web services retrieve

gene and transcript information on the fly. Data from exomes or genomes from one or more

individuals can be stored and displayed in several ways: variant-by-variant or all connected

to one or more individuals in the database. To promote data sharing, both phenotypes and

variants can be stored (and identified) individually. Data can be made public and non-public

for both with the option to query. Other features include: display of disease-specific

phenotype information, storage of temporal phenotype information, and queries in and

across data columns.

An example of personal genome variant information stored in LOVD3 can be found at

http://databases.generade.nl/personal_genomes

The Human Variome Project has granted LOVD the recommended system status for variant

collection.

[1] Fokkema IF, Taschner PE, Schaafsma GC, Celli J, Laros JF, den Dunnen JT (2011).

LOVD v.2.0: the next generation in gene variant databases. Hum Mutat. 2011 32:557-63.

P22

Notes

P23

Notes

P24

Notes

P25

Notes

Delegate List

Anuradha Acharya

Mapmygenome

anuacharya@gmail.com

Eman Ahmed

KU Leuven

eman.ahmed@student.kuleuven.be

Dalal AlMutairi

Kuwait University

dalal.almutairi@hsc.edu.kw

Mohamed Osman Arab

Oslo university Hospital

uxmoar@ous-hf.no

Kat Arney

First Create the Media

kat@firstcreatethemedia.com

Naveed Aziz

CGEn

naveed.aziz@sickkids.ca

Mark Bale

Genomics England

mark.bale@genomicsengland.co.uk

Sivia Barnoy

Tel-Aviv University

sivia@tauex.tau.ac.il

Stephan Beck

UCL

s.beck@ucl.ac.uk

Christopher Bell

University of Southampton

c.bell@qmul.ac.uk

Dhruva Biswas

UCL Cancer Institute

dhruva.biswas.15@ucl.ac.uk

Martina Bittoova

GENNET s.r.o.

martina.bittoova@gennet.cz

Alexandra Blakemore

xxx

Alex.blakemore@gmail.com

Asta Blazyte

Ulsan National Institute of Science and

Technology

blazyte.asta@gmail.com

Paranchai Boonsawat

University of Zurich

boonsawat@medgen.uzh.ch

Robert Borkowski

23andMe

rborkowski@23andMe.com

Pascal Borry

KU Leuven

Pascal.Borry@med.kuleuven.be

Nick Brain

Thermo Fisher Scientific

nick.brain@thermofisher.com

Darren Burgess

Nature Reviews Genetics

d.burgess@nature.com

Toby Call

Chronomics Ltd

toby@chronomics.com

Dorothée Caminiti

ETH Zurich

dorothee.caminiti@gmail.com

Jose L Campos

IGMM, University of Edinburgh

j.campos@ed.ac.uk

George Church

Harvard Medical School

church_lab_admin@hms.harvard.edu

John Cleary

Heriot-Watt University

j.a.cleary@hw.ac.uk

Hayley Clissold

Wellcome Sanger Institute

hc8@sanger.ac.uk

Manuel Corpas

Cambridge Precision Medicine

m.corpas@cpm.onl

James Dai

FHCRC

jdai@fredhutch.org

Johan den Dunnen

Leiden University Medical Center (LUMC)

lumc@johandendunnen.nl

Priya Dewan

Royal London Hospital

priyadewan111@hotmail.com

Huw Dorkins

University of Oxford

huw.dorkins@spc.ox.ac.uk

Simone Ecker

UCL Cancer Institute

s.ecker@ucl.ac.uk

Frances Elmslie

St George's, University of London

frances.elmslie@nhs.net

Yaniv Erlich

MyHeritage/Columbia University

erlichya@gmail.com

Bert Eussen

ErasmusMC

h.eussen@erasmusmc.nl

Eva Fisher

Robert Koch-Institute

fishere@rki.de

Natalie Fitzpatrick

UCL

n.fitzpatrick@ucl.ac.uk

Isabelle Foote

Queen Mary University of London

i.f.foote@qmul.ac.uk

Gihan Gawish

College of medicine, AlImam University

(IMSIU)

Galhusseinig@gmail.com

Maurice Gleeson

Genetic Genealogist

mauricegleeson@doctors.org.uk

Gustavo Glusman

Institute for Systems Biology

gustavo@systemsbiology.org

Jaap Goudsmit

Harvard School of Public Health

jgoudsmit@hsph.harvard.edu

Becki Green

King's College London

rebecca.e.green@kcl.ac.uk

Bastian Greshake Tzovaras

openSNP

bgreshake@googlemail.com

Jose Afonso Guerra Assuncao

UCL Cancer Institute

a.guerra@ucl.ac.uk

Christi Guerrini

Baylor College of Medicine

guerrini@bcm.edu

Joanne Hackett

Genomics England

joanne.hackett@genomicsengland.co.uk

Lorenza Haddad Talancon

Codigo 46

lorenza@codigo46.com.mx

Thomas Haizel

Nkaarco Diagnostics Limited

t.haizel@angliadna.co.uk

Mihail Halachev

University of Edinburgh

mihail.halachev@igmm.ed.ac.uk

Jennifer Harrow

ELIXIR

jen.harrow@elixir-europe.org

John Hatwell

Genomics England

hannah.mcculloch@genomicsengland.co.

uk

Charlotte J Haug

New England Journal of Medicine

charlottejohanne@gmail.com

Maribel Hernández Rosales

Institute of Mathematics UNAM

maribel@im.unam.mx

Dorota HoffmanZacharska

Institute of Mother and Child

dhoffman@wp.pl

Camilla Ip

University of Oxford

camilla.ip@well.ox.ac.uk

Irma Jarvela

University of Helsinki

irma.jarvela@helsinki.fi

SIRKKA JARVENPAA

University of Texas at Austin

sirkka.jarvenpaa@mccombs.utexas.edu

Sungwon Jeon

Ulsan National Institute of Science and

Technology

jsw0061@gmail.com

Kathleen Job

Cardiff University

JobK@cardiff.ac.uk

Lennart Karssen

PolyOmica

l.c.karssen@polyomica.com

Stephen Kearney

Griffith College Dublin

Skearney82@gmail.com

Matthew Kendzior

University of Illinois

mkendzi2@illinois.edu

Monika Koudova

GENNET s.r.o.

monika.koudova@gennet.cz

David Kovalic

Webster University

david.kovalic@gmail.com

Peter Lederer

FAU Erlangen

drprlr@mailbox.org

Heidi Ledford

Nature magazine

h.ledford@nature.com

Edmund Lehmann

Cambridge Precision Medicine

e.lehmann@cpm.onl

Cathryn Lewis

King's College London

cathryn.lewis@kcl.ac.uk

Stephen Lincoln

Invitae

steve.lincoln@me.com

Marina Lipkin Vasquez

INCa

Mari.lipkin@gmail.com

Jodie Lord

Miss

jodie.lord@kcl.ac.uk

Heidi Marjonen

National Institute for Health and Welfare

heidi.marjonen@thl.fi

Argyri Iris Mathioudaki

Uppsala University

a.iris.mathioudaki@imbim.uu.se

Karyn Megy

University of Cambridge

kmegy369@gmail.com

Andres Metspalu

Andres Metspalu

andres.metspalu@ut.ee

Ismail Moghul

University College of London

ismail.moghul@gmail.com

Tiffany Morris

Illumina

tmorris1@illumina.com

Miranda Mourby

University of Oxford

miranda.mourby@dph.ox.ac.uk

Monica Munoz-Torres

Oregon State University

munoztmo@oregonstate.edu

Sergey Nechaev

Illumina

snechaev@illumina.com

Fiona Nielsen

Repositive

fiona@repositive.io

Sandosh Padmanabhan

University of Glasgow

sandosh.padmanabhan@glasgow.ac.uk

Priit Palta

FIMM, University of Helsinki

priit.palta@helsinki.fi

Guro Meldre Pedersen

DNV GL

guro.meldre.pedersen@dnvgl.com

Minja Pehrsson

Helsinki Biobank

minja.pehrsson@hus.fi

Hartmut Peters

Charite -Universitaetsmedizin

hartmut.peters@charite.de

Vincent Plagnol

GenomicsPlc

vincent.plagnol@genomicsplc.com

Nikolas Pontikos

UCL Institute of Ophthalmology

n.pontikos@ucl.ac.uk

Mad Price Ball

Open Humans Foundation

mad@openhumans.org

Vasiliki Rahimzadeh

McGill University

vasiliki.rahimzadeh@mail.mcgill.ca

Jake Reeves

University of Surrey

j.reeves@surrey.ac.uk

Michael Rhodes

NanoString Technologies

mrhodes@nanostring.com

Angharad Roberts

Imperial College

angharad.roberts@imperial.ac.uk

Barjinder Sahota

SAHOTA SOLICITORS

sahota@libel-law.co.uk

Saskia Sanderson

UCL

saskia.sanderson@ucl.ac.uk

Rupa Sarkar

The Lancet Digital Health

r.sarkar@lancet.com

Gary Saunders

ELIXIR Europe

gary.saunders@elixir-europe.org

Cathleen Schulte

Office for Life Sciences

cathleen.schulte@officeforlifesciences.gov

.uk

Mahsa Shabani

University of Leuven

mahsa.shabani@kuleuven.be

Sevasti Skeva

KU Leuven

seviskeva@gmail.com

Colin Smith

University of Brighton

c.smith6@brighton.ac.uk

Reecha Sofat

University College London

r.sofat@ucl.ac.uk

Dylan Spalding

EMBL-EBI

spalding@ebi.ac.uk

Ciara Staunton

Middlesex University

c.staunton@mdx.ac.uk

David Stejskal

GENNET s.r.o.

david.stejskal@gennet.cz

Peter Taschner

University of Applied Sciences

taschner.p@hsleiden.nl

Nicki Taverner

Cardiff University and All Wales Medical

Genetics Service

tavernern@cardiff.ac.uk

Ben Te Aika

Genomics Aoteraoa

ben.teaika@otago.ac.nz

Philip Twiss

Addenbrookes Hospital

philip.twiss@addenbrookes.nhs.uk

Kees van den Berg

GenomeScan

k.vandenberg@genomescan.nl

Adam Vaughan

New Scientist

adam.vaughan@newscientist.com

Annemieke Verkerk

Erasmus Medical Center

j.verkerk@erasmusmc.nl

Natalia Volfovsky

Simons Foundation

nvolfovsky@simonsfoundation.org

Cyndi Williams

Quin

cyndi.williams@quintech.io

Howard Wu

Open Humans

BraveWuWorld@protonmail.com

Yanxiang Zhou

Illumina Ventures

yanxiang@illuminaventures.com

Index

Acharya, A S3 Pedersen, G P16

Ahmed, E P1 Pontikos, N P17

AlMutairi, D P2 Plagnol, V S19

Aziz, N S9

Rahimzadeh, V P18

Biswas, D P3 Roberts, A P19

Blazyte, A P4

Boonsawat, P P5 Sanderson, S S29

Borry, P S39 Saunders, G S41

Smith, C S23

Church, G S1 Sofat, R S47

Cleary, J P6 Spalding, D P20

Staunton, C S33

Elmslie, F S45

Erlich, Y S49 Taschner, P P21

Taverner, N S15

Gawish, G P7 Te Aika, B S25

Gleeson, M S27, P8

Glusman, G S13

Greshake Tzovaras, B S21

Guerrini, C S37

Hackett, J S31

Haddad Talancon, L S5

Harrow, J P9

Hernandez Rosales, M P10

Jeon, S S11,P11,P12

Kendzior, M P13

Lewis, C S17

Lincoln, S P14

Marjonen, H S43

Metspalu, A S7

Munoz-Torres, M S35

Moghul, I P15