SNPs and Diseases

Molecular School of Medicine

Thursday, November 16th, 2017

Carolina Medina Gomez PhD

- Quick look to the pioneers: HapMap

- 1000 Genomes project

-Description

- Diversity Panel

-The HRC consortium

- Local Panels

- Acquire awareness on the implications of population diversity

- Comprehend the utility of large haplotype reference panels and

large biobank data

- Use this knowledge for the mapping of complex traits

Topic outline

Learning Aims

AIM

Perform a comprehensive sampling of common genetic variation that

may form the basis of phenotypic differences in humans

The HapMap Project

YRI

CEU

CHB+JPT

A second generation human haplotype map of over 3.1 million SNPs 2007, Nature 449: 851-861.

The HapMap Project

YRI

CEU

CHB+JPT

HapMap II r 22: Build 36 - 2007

270 samples

HapMap III r22 Build 36 - 2010

1,184 Samples – DEPICT, LDSC

Name Population # of samples

ASW African ancestry in Southwest USA 53

CEU Utah residents with Northern and Western European ancestry

from the CEPH collection 112

CHB Han Chinese in Beijing, China 137

CHD Chinese in Metropolitan Denver, Colorado 109

GIH Gujarati Indians in Houston, Texas 101

JPT Japanese in Tokyo, Japan 113

LWK Luhya in Webuye, Kenya 110

MEX Mexican ancestry in Los Angeles, California 58

MKK Maasai in Kinyawa, Kenya 156

TSI Toscani in Italia 102

YRI Yoruba in Ibadan, Nigeria 147

Integrating common and rare genetic variation in diverse populations 2010, Nature 467: 52-58.

Phase 1 – 2010

AIM

Catalogue of human genetic variation sequencing whole genome of 1,092

individuals from 14 worldwide populations. Discover human genetic

variations of all types (95% of variation > 1% frequency) at the population

level

The 1000 Genomes project – Build 37

The 1000 Genomes Project Consortium 2010, Nature 467: 1061-1073.

Phase 3 – 2015

AIM

Catalogue of human genetic variation sequencing whole genome of 2,504

individuals from 14 worldwide populations. Discover human genetic

variations of all types (99% of variation > 1% frequency) at the population

level

The 1000 Genomes project – Build 37

A global reference for human genetic variation 2015, Nature 526: 68-.

Phase 3 – 2014

AIM

Catalogue of human genetic variation sequencing whole genome of 2,504

individuals from 14 worldwide populations. Discover human genetic

variations of all types (99% of variation > 1% frequency) at the population

level

The 1000 Genomes project – Build 37

A global reference for human genetic variation 2015, Nature 526: 68-.

The American Journal of Human Genetics 96, 37–53, January 8, 2015

Phased design

Generation R HapMap Imputation

3,021,329 SNPs

2,671,724 MAF>0.01 r2>=0.3

Generation R 1KG Imputation

47,072,644 SNPs

11,361,791 MAF>0.01 r2>=0.3

2012

2017

None of the two variants were present or tagged by HapMap variants (one

is common)

Imputation Servers

Phase 1 – 2016

AIM

To bring together as many whole-genome sequencing data sets as

possible. This reference panel consists of 64,976 haplotypes at

39,235,157 SNPs.

The Haplotype reference consortium – Build 37

A reference panel of 64,976 haplotypes for genotype imputation. Nature Genetics 48 10

Phase 1 – 2016

AIM

To bring together as many whole-genome sequencing data sets as

possible. This reference panel consists of 64,976 haplotypes at

39,235,157 SNPs.

The Haplotype reference consortium – Build 37

A reference panel of 64,976 haplotypes for genotype imputation. Nature Genetics 48 10

34% increase r2 at 0.1%

r2 at ~0.4%

http://www.nealelab.is/blog/2017/7/19/rapid-gwas-of-thousands-of-phenotypes-for-337000-samples-in-

the-uk-biobank

The Million Veteran Program

began collecting data in 2011,

and it has the goal of reaching

1 million participants by 2020

or 2021.

Now… imagine if we combine

data

Predictions for next GIANT

freeze 1.5 million

Analytical Issues!

Current challenges

Perception challenge:

Are we using the correct multiple testing threshold, or we should change

it as we are including more rare variants constituting independent test (LD low).

Methodological challenge:

Is it necessary to correct further for population stratification

(implementation of mixed model) to avoid false-positive signals.

Computational challenge:

Can we store and analyze the data with our current computational power.

Follow-up challenge

Can we identify correctly variants/genes for follow-up studies.

The discovery of genetic variants associated with a trait

or disease is determine by different parameters

We are surpassing the 1M barrier

New imputation panels allow to

explore variants with MAF ~0.1%

More variants more opportunities

to tag the causal variant

The new GWAS era is a treasure trove for making

new fundamental discoveries in human genetics.

10 Years of GWAS Discovery: Biology, Function, and Translation. AJHG July. 2017

Resolution magnification

P<=6.6x10-9

Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis.

Nature Genetics.

Original 2x2 scenario of genetic architecture needs to be

redefined under the scope of the 1000G and other projects

Debatable Facts

- Understanding of human genome diversity is key for the design of

genetic studies

- Large and more comprehensive panels panels provide the best

performance and yield in terms of quality and MAF coverage resulting in

greater power (even more so, in combination with MegaBiobanks!)

- Most of the novel variants to be discovered are of “low to rare” allele

frequency, highly population specific & enriched for functional aspects

- Upscaling of technology, either through interfacing with -omic data or

through experimental perturbations are necessary for making new

fundamental discoveries in human genetics

Take home messages

10 Years of GWAS Discovery: Biology, Function, and Translation. AJHG July. 2017