identifying genetic variants for complex disorders - duth disorder phenylketonuria ... disorder...

Identifying genetic variants for complex disorders

Peristera Paschou, PhD

Associate Professor of Population Genetics

Dept. of Molecular Biology and Genetics

Democritus University of Thrace

Marianthi Georgitsi, PhD

Assistant Professor of Medical Biology-Genetics

Dept. of Medicine

Aristotle University of Thessaloniki

The average genome (2x3 billion bp) contains:

– 3-4 million single nucleotide variations, compared to the reference sequence (Single Nucleotide Polymorphisms – SNPs)

– ~0.5 million small insertions or deletions ‘indels’ (1-100bp)

– ~5,000 larger insertions or deletions (>100bp)

We are quite similar, but we are different…

Variation across all (~23,000) genes - the ‘exome’~18,000 variants

~8-9,000 functional variants

~95% of variants are common

~500-1000 genes with new mutations

~100-200 knock-out mutations

Monogenic Disorders

vsComplex Disorders

Single gene disorders

Low impact on public health cost

One gene Mendelian inheritance Rare mutations Classical genetics approaches Examples: Huntington’s disease Cystic fibrosis

Multifactorial disorders(Complex traits)

Serious impact on public health cost

Multiple genes Complex pattern of inheritance Common and rare genetic

variants Genome scans – new

technologies Examples: Stroke Diabetes Schizophrenia

GENETICS ENVIRONMENT

Duchenne muscular dystrophy

Hemophilia

Gastric ulcer

Diabetes Schizophrenia

Bipolar disorder

PhenylketonuriaGalactosemia

Cardiovascular diseaseAnkylosing spondylitis

Car accident

Interaction of genetic and environmental factors

Rare disordersSimple genetic basis

Unifactorial

Common disordersComplex genetic basisMultifactorial

λ = Relative risk of symptom development in relatives (sibs) of affected individuals

Disorder λ

Schizophrenia 12

Autism spectrum disorder 150

Bipolar disorder 7

Type I diabetes 35

Crohn’s disease 25

Multiple sclerosis 24

λ=1 risk in general population

Nussbaum et al: Thompson and Thompson’s Genetics in Medicine, 2007, p 153.

Gene-hunting strategies for multifactorial disorders

Candidate gene approachDisorder → Etiological pathway → Gene → Mapping

Positional cloningDisorder → Mapping → Gene → Etiological pathway

Gene identification

-Size of the human genome≈ 3 x 109 bp

-23,000 genes

-Large portion of the genome function is not known.

Claude Maunet 1891. Haystack at the Sunset near Giverny

Looking for genes in 23 pairs of chromosomes…

SNPs can be used to create dense marker maps

microsatellites SNPs

Recent genome-wide association studies use millions of SNPs.

Clinical Phenotype

Indications for a genetic basis?

Study design

families Sib pairs Single patients

Sample and data collection

Analysis

Linkage analysis Association studies

Define candidate regions

Physical mapping/ gene identification

Positional cloning…

Linkage analysis

Large families Sib-pair studies

1989 – The cystic fibrosis gene is identifiedFirst successful positional cloning study

Linkage analysis

• What is the probability that the disease-causing mutation and the polymorphism we are studying are linked (at a specific genetic distance) vs non-linked?

gametes

Recombination and linkage•Two loci are linked when little or no recombination occurs among them.

•Recombination: Exchange of DNA segments among homologous regions during meiosis and gamete formation.

Recombination and linkage

• Two loci at different chromosomes cannot be linked.

• Linkage increases as physical distance decreases.

• 1cM distance among two genetic loci, means that 1% of produced gametes will be recombined.

Logarithm of the odds LOD score

Logarithm of the probability of linkage Ratio of the probability that the two loci are linked

vs the probability that they are not linked. likelihood of obtaining the test data if the two loci

are indeed linked, to the likelihood of observing the same data purely by chance.

LOD = 3.0 = odds of 1000/1 for linkage Corresponds to error of 5%

Linkage analysis Great power for gene identification for mendelian

disorders

Limited success when used for multifactorial disorders

1,2 1,2

1,1 1,1 2,2 1,1

1,2 1,2

2,2 2,2 1,1 2,2

1 1

1 2

1 1

1 1

2 2

1 1 2 2

2 2

1 2

1 1 2 22 2 1 2

2 22 2

2 21 2 1 2 1 2 1 1

1 2 1 2 1 2 2 2

trios

Population studies

Association studies

Genetic Association Studies

Aim: To unravel associations between genetic data (ie alleles or genotypes) of

commonly occurring genetic variants with information regarding a trait or a

medical phenotype (ie disease) under study, using statistical analyses and a large

enough sample size (typically cases versus controls), in order to support the

statistics that these variants contribute to trait/disease risk.

Examples of complex

diseases

Type II Diabetes Mellitus

Obesity

Cardiovascular diseases

Cancer (non-hereditary)

Osteoarthritis

Autoimmune disorders

Alzheimer’s disease

….

Schizophrenia

Autism

Bipolar Disorder

Obsessive Compulsive Disorder

Learning disabilities (Dyslexia)

….13/04/2016

Linkage disequilibrium (LD)

The non random association of alleles at different loci

Essential tool for genetic association studies

Linkage DisequilibriumExample

SNP1: A 50% C 50%

SNP2: A 50% G 50%

SNP1 SNP2 expected frequency of haplotypes

A A 0,5 x 0,5

A G 0,5 x 0,5

C A 0,5 x 0,5

C G 0,5 x 0,5

If total LD exists – only 2 haplotypes will be observed (eg)

A G

C A

LD around a mutationgenerations

01 1 1 1 1 1 1 1 1 1 1

1 1

k2 2 2 1 1 1 1 1 1 2 2

1 1

2 2 1 1 1 1 1 1 2 2 2

1 1

2 2 2 2 1 1 1 1 1 2 2 2

1 1

.

.

.

11 1 1 1 1 1 1 1 1 2 2

1 1

2 2 1 1 1 1 1 1 1 1 1

1 1

2 2 2 1 1 1 1 1 1 1 1 1

1 1

g2 2 2 2 2 1 1 2 2 2 2 2

1 1

2 2 2 2 2 1 2 2 2 2 2

1

.

.

.2 2 2 2 2 2 1 1 2 2 2 2

1 1

Ancestral chromosome

Ex

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oso

me

s

Ancestral DNA sequence

Novel DNA sequence due to recombination

mutation

Jobling, Hurles & Tyler-Smith. Human Evolutionary Genetics

mutation


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polymorphism mutation


Ex

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mutation


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SNPs

Genome-wide Association Studies (GWAS)

Ideally…

Identify all SNPs(eg 15.000.000)

Collect a very large sample(eg 1,000 patients and 1,000 control individuals)

Genotype all individuals for all SNPs

30 billion genotypes!

Cost in 2002: 50 cents/genotype.

$15 billion for each disorder!

Genome structure allows the selection of tagging SNPs

Hirschhorn & Daly, Nat Rev Genet 2005

Candidate gene or GWAS signal

300.000 SNPs suffice for a rough scan of the human genome

Paschou et al. Genome Research 2007De Bakker et al. Nature Genetics 2005

mutationmutation

SNP

13/04/2016 30

Genetic Association Studies versus Linkage studies

Appropriate for complex phenotypes

Increased genetic (locus) heterogeneity (ie

multigenic variance) – many genes, many variants

Common variance (“common disease – common

variants” concept) – modest disease risk per

variant

Inadequate power to detect associations

Large numbers of cases and controls (healthy

individuals), or family-based associations (trios, ie

affected child and both parents) or extreme

phenotypes

Mostly SNPs (taggingSNPs): A single or a few

SNPs within a chromosomal region that

capture(s) (ie “tags”) most of the common DNA

variation in this particular region, owing to the

effect of Linkage Disequilibrium (LD).

Associated SNPs most often not coding (they can

be in LD with the causal variant or have a

regulatory effect)

Appropriate for Mendelian traits

Reduced genetic heterogeneity (one or a few

genes)

Typically rare variants (“rare disease – rare

variants” concept) in all affected individuals

Large, multigenerational pedigrees –

detection power (parametric, non-parametric

linkage analyses)

Typically microsatellite markers, SNPs, or

long stretches of chromosomal

homozygosity (for recessive traits only)

Note: LD is defined as the phenomenon of co-

inheritance (non-random association) of studied

genetic marker (SNPs) alleles, unlikely to be

separated by homologous recombination (“linked”

markers) within a population.

Candidate genes

1$ per SNP

1 -100 SNPs

2007

Technology reduces the cost

Genome genotyping

(GWAS)

250$

1 millionSNPs

Candidate genes

1$ per SNP

1 -100 SNPs

2007


Genome-wide association studies (GWAS)

Patients Controls

Genotypingeg 600,000 SNPs

Comparison of alleles – Statistical

analysis

Thousands of samples – The more

the better !

September 2012: 1.416 studies7.688 associated SNPs

http://www.genome.gov/gwastudies/

13/04/2016

http://www.lifesciences.sourcebioscience.com/media/426305/genotyping%20image.png

Current status of available SNP genotyping platforms – A comparison

13/04/2016© Marianthi Georgitsi

Workflow of a typical GWAS study

Sample

collection

DNA extraction

and Quantifica

tion

DNA Plating

Array run

QC

Statistical analysis

(trait-dependent)

Peripheral blood

Buccal cells

…

Manual purification

Kit-based purification

96-well plates

384-well plates

Replication

study

(stage 2)

Validation

(technical,

functional)

Pathway

analyses

Meta-analyse

s

Fluorospectrophotometry

Spectrophotometry

• Other genotyping platform

• In silico analysis (SNPs in

LD)

• Gene expression analysis

• Cell-based analysis

• Animal models

• Resequencing of region

• ANOVA• Chi-square• Fisher’s Exact• Covariate

adjustment

• Multiple

testing

• Sample

size

• Population

• Phenotypic

criteria

T2DM associated genes before 2007

Confirmed susceptibility genes in the pre-GWAS era

Altshuler et al, Nature Genetics 2000Gloyn et al, Diabetes 2003

Grant et al, Nature Genetics 2006

Candidate gene studies

GWAS for T2DM

GWAS for DM associated traits

Meta-analysis studies

T2DM associated genes after 2007

Monda et al. Nature Genetics 2013

>3.2 million SNPs

39,144 men and women of African ancestry

Body Mass Index GWAS and meta-analysis

women of African ancestry

Alleles associated with multifactorial disorders differ in frequency around the world

CORONA ET AL. 12TH INTERNATIONAL CONGRESS OF HUMAN GENETICS, 2011

Adygei

Russian

Sardinian

Basque

Orcadian

French

Italian/Tuscan

Different genetic structure in different populations

Significant differentiation even among European populations.

Paschou et al. Journal of Medical Genetics 2010Paschou et al. PLoS Genetics 2008Paschou et al. PLoS Genetics 2007

650.000 SNPs in different European populations.

CEU Greeks

TSI (Italians)

(North Europe) (South Europe)

Principal Components Analysis

1,200 SNPs

For each population, the average individual as well as standard deviation is shown

Stathias et al. Annals of Human Genetics 2012

aa Aa AA

Population 1

Population 2

cases

controls

Population stratification is a confounding factor in genetic association studies

Paschou et al PLoS Genet 2007 Paschou et al PLoS Genet 2008

AsianAfrican

European

Using genetic markers to correct for population stratification in genome-wide

association studies

How many more genes are we looking for?

Type 2 Diabetes – already 45 genes associated

DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium

34,840 patients – 114,981 controls

10 additional genes

Morris et al. Nature Genetics 2012

Type 2 Diabetes associated genes account for 10-28% of heritability

The case of the missing heritability (Maher, Nature 2008)

What GWAS studies have to offer – Pros and Cons

Pros (+)

• High-throughput analysis (hundreds of

thousands up to million variants)

• Large-scale projects with thousands

participants

• Suitable for complex, non-Mendelian

disorders, quantitative traits, eQTLs, …

• Variety of software tools for computerized

analysis

• Dataset repositories for meta-analyses

continuously curated and updated

• Pathway analyses

• Certified service providers worldwide

• ...

Cons (-)

• They target pre-defined variants (biased)

• Not suitable for identification (de novo) studies

• Not suitable for studying Mendelian disorders

• Alleles confer small effect sizes [small Odds

Ratios (typically OR<1.5)]

• False-positive (population stratification,

genotyping errors, selection bias, etc) or false-

negative results (insensitivity to rare variants,

lack of genetic variants from platforms, lack of

variation in a SNP in the population under

study)

• The richer they are in context, the more

expensive

• Their analysis requires training in

bioinformatics and computationally intensive

analyses and infrastructure

• Functional approaches to interpret the data are

needed (gene expression analysis, cell and animal

model manipulations, etc)

• ...

“Missing heritability” of common

diseases/disorders

© Marianthi Georgitsi

What GWAS studies have to offer

Prof. Scharf J, Massachusetts General

Hospital, Boston, personal communication,

2015

Missing heritability not targeted by GWAS

studies

• Rare variants (MAF<1%)

• Structural variants

• Gene-gene interactions

• Gene-environment interactions

• Population isolates and population extremes

Low-hanging fruit ?

(high penetrance, high frequency)

Human

Genome


Where is the missing heritability?

McCarthy et al. Nat Rev Genet 2008

Common disease – rare variant hypothesis

Genome genotyping

(GWAS)

250$

1 millionSNPs

Candidate genes

1$ per SNP

1 -100 SNPs

2007


Exome sequencing

(NGS)

1000$

All exons

Whole genome sequencing

(NGS)

< 1000$

The whole genome

Genome genotyping

(GWAS)

250$

1 millionSNPs

Candidate genes

1$ per SNP

1 -100 SNPs

2007 2011 2014?


…1000 genomes project

Nature 526, 68–74 (01 October 2015)

•Four affected individuals in three independent kindreds, •Exome sequencing at 40x coverage.

•Filtering against public SNP databases and eight HapMapexomes identified a single candidate gene

•DHODH (Dihydroorotate dehydrogenase), encodes a key enzyme in the pyrimidine de novo biosynthesis pathway.

•Sanger sequencing confirmed the presence of DHODH mutations in three additional families with Miller syndrome.

Exome sequencing identifies the cause of a Mendelian disorder

Ng et al. Nature Genetics 2010

•Ten unrelated individuals.

•Seven individuals had nonsense or frameshift mutations at MLL2

•MLL2 - encodes a Trithorax-group histonemethyltransferase

•Sanger sequencing revealed MLL2 mutations in 2 of the remaining individuals

•De novo or transmitted according to phenotype

Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome

Ng et al. Nature Genetics 2010

Exome sequencing to identify rare variants in complex disorders?

Bamshad et al. Nature Reviews Genetics, 2011

unrelateds

Large families

trios

Comparing the extremes of a phenotypic distribution

• Sequencing the exomes of >7,000 individuals with extreme phenotypes

• Goal: find genes that underlie common cardiovascular disease (eg early-onset myocardial infarction and stroke) and lung disease (eg chronic obstructive pulmonary disease)

• Funded by the US National Heart, Lung, and Blood Institute

The Exome Sequencing Project

Fu et al. Nature 2013Boileau et al. Nature Genetics 2012Emond et al. Nature Genetics 2012

Tennessen et al. Science 2012

Data analysis of a typical NGS experiment

Illumina site:

http://www.illumina.com/

Workflow to identify disease variants in exomesequencing data

Pabinger et al. Briefings in Bioinformatics, 2013

Making sense of NGS data

~24.000 SNPs (exome-seq)

~20.000 SNPs (exome-seq)

Factors affecting the identification of causal alleles

against a background of common polymorphisms:

What is the mode of inheritance of a trait?

Does population structure affect causal alleles?

Does the phenotype arise de novo or due to inherited

variants?

Locus heterogeneity of a trait (=how many genes

affect the manifestation of a trait)

How large sample size in order to identify trait-

associated alleles?

What analytical framework (software, pipeline) to be

used?

~2% of SNPs identified

per individual by WES is

novel


• Coding?

• Intronic?

• Deleterious?

• Non-functional?

• Amino-acid change?

• Regulatory?

20,000-25,000 exonic variants…What is the effect?

dbSNP- Includes everything!!- SNPs have information aboutorigin-VCF available

ClinSeq- 650 Exomes with phen. (dbGaP)- CCDS and knownGenes- soon in dbSNP, VCF to beavailable

1000 Genomes- 1,094 low coverage genomes (~4x)- >=1% sensitivity- In dbSNP, VCF available- 822 Exomes – CCDS (coming soon)

NHLBI Exome Sequencing Project- 2,500 Exomes with phen. (dbGaP)- in dbSNP, VCF available

Who else has the variant? Human Variation DatabasesGoal: Determine presence of variant in others

Potential frequencies of causal variants in

complex traits

Cirulli and Goldstein, Nature Reviews Genetics 2010

How NGS has advanced our knowledge in disease predisposition and

disease gene identification

Chong et al., AJHG, 97(2):199-215, 2015

Risk of developing a disease Lifestyle changes

Personalized treatment (pharmacogenomics)

Less toxicity & ADR / Better response

Family planning Prenatal screening / Genetic counseling

Presymptomatic or predictive screening

Disease management and follow-up

identifying genetic variants for complex disorders - duth disorder phenylketonuria ... disorder...

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