Steven Gazal

Alkes Price lab - Harvard School of Public Health / Broad Institute

Genomics of common diseases - 09/25/2016

The linkage disequilibrium dependent architecture of human complex traits

http://www.ebi.ac.uk/birney-srv/medaka-ref-panel/viz.htmlhttps://www.google.com/imgres?imgurl=http://cdn2.hubspot.net/hubfs/435333/world-architecture-festival-01.jpg?t=1463423388041&imgrefurl=http://www.stonerbunting.com/blog/topic/architecture&docid=POYfIncxMcpkPM&tbnid=Rk2Gs8mpnq6W-M:&w=3200&h=2133&bih=606&biw=1029&ved=0ahUKEwjGh47g8ZjNAhVBOyYKHfq0C2kQMwhhKA4wDg&iact=mrc&uact=8

2

What does “architecture” mean?

• How heritable is the trait (h2)?

• How many causal variants are there?

• How are causal variants distributed across: - common vs. rare variants (hg

2 vs. h2) - MAF spectrum: MAF-dependent architecture - functional annotations (DHS, H3K27ac, conserved, etc.) …

https://www.google.com/imgres?imgurl=http://cdn2.hubspot.net/hubfs/435333/world-architecture-festival-01.jpg?t=1463423388041&imgrefurl=http://www.stonerbunting.com/blog/topic/architecture&docid=POYfIncxMcpkPM&tbnid=Rk2Gs8mpnq6W-M:&w=3200&h=2133&bih=606&biw=1029&ved=0ahUKEwjGh47g8ZjNAhVBOyYKHfq0C2kQMwhhKA4wDg&iact=mrc&uact=8

3

What does “LD-dependent architecture” mean?

• LD-dependent architecture: dependence of causal effect sizes on the level of LD of a SNP after conditioning on MAF

http://www.ebi.ac.uk/birney-srv/medaka-ref-panel/viz.htmlhttps://www.google.com/imgres?imgurl=http://cdn2.hubspot.net/hubfs/435333/world-architecture-festival-01.jpg?t=1463423388041&imgrefurl=http://www.stonerbunting.com/blog/topic/architecture&docid=POYfIncxMcpkPM&tbnid=Rk2Gs8mpnq6W-M:&w=3200&h=2133&bih=606&biw=1029&ved=0ahUKEwjGh47g8ZjNAhVBOyYKHfq0C2kQMwhhKA4wDg&iact=mrc&uact=8

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Evidence of LD-dependent architecture

SNPs in regions with low level of LD have higher effect sizes

• DNase I hypersensitive site (DHS) regions (Gusev et al. 2014 AJHG)

• Functional elements such histone marks (Finucane et al. 2015 Nat Genet)

• Regions with high GC-content (Loh et al. 2015 Nat Genet)

5

Evidence of LD-dependent architecture

SNPs in regions with low level of LD have higher effect sizes

• DNase I hypersensitive site (DHS) regions (Gusev et al. 2014 AJHG)

• Functional elements such histone marks (Finucane et al. 2015 Nat Genet)

• Regions with high GC-content (Loh et al. 2015 Nat Genet)

More disease variants in coding regions with lower recombination rate (where high level of LD is expected) (Hussin et al. 2015 Nat Genet)

Evidence of LD-dependent architecture

SNPs in regions with low level of LD have higher effect sizes

• DNase I hypersensitive site (DHS) regions (Gusev et al. 2014 AJHG)

• Functional elements such histone marks (Finucane et al. 2015 Nat Genet)

• Regions with high GC-content (Loh et al. 2015 Nat Genet)

More disease variants in coding regions with lower recombination rate (where high level of LD is expected) (Hussin et al. 2015 Nat Genet)

Fundamental as this architecture biases heritability estimations (Speed et al. 2012 AJHG, Gusev et al. Plos Genet 2013, Yang et al. Nat Genet 2015)

These discordant findings have never been formally assessed, quantified or biologically interpreted

1. Assessing the LD-dependent architecture of human complex traits

2. Understanding which processes shaping LD patterns are involved in human complex traits

7

W< < w< Outline The LD-dependent architecture of human complex traits

8

Coding

DHS

0

1

0

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Recomb Rate

Level of LD (LLD)

Let’s consider Q continuous annotations

Stratified LD score regression (Finucane et al. 2015) Extension to continuous valued annotations

βj : Genotype effect size of SNP j aj,q : annotation of SNP j in category Qq τq : Effect size of category Qq N : number of individuals l(j,q) : LD score of SNP j in category Qq a : measure of confounding r2jk : correlation between SNP j and k

9

Coding

DHS

0

1

0

1

Recomb Rate

Level of LD (LLD)

Let’s consider Q continuous annotations

Stratified LD score regression (Finucane et al. 2015) Extension to continuous valued annotations

Per-standardized causal effect size

βj : Genotype effect size of SNP j aj,q : annotation of SNP j in category Qq τq : Effect size of category Qq N : number of individuals l(j,q) : LD score of SNP j in category Qq a : measure of confounding r2jk : correlation between SNP j and k

• Need to take into account the MAF-dependent architecture – Common SNPs have larger causal effect sizes

– Common SNPs have larger LD scores

• Level of LD (LLD) measure: LD score MAF corrected through MAF-stratified quantile normalization

• We started from the following model

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Measuring the level of LD (LLD) of a SNP

• Publicly available summary statistics (29)

• 23andMe, Inc. summary statistics (19)

• UK Biobank data (15)

56 traits in total (average N = 101,420) Meta-analysis over 31 independent traits (average N = 84,719)

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56 traits analyzed (summary statistics only)

12 Meta-analysis: P = 1.88 x 10-94

SNPs with low LLD explain more heritability

Impact of level of LD (LLD) on genetic architecture for 20 highly heritable traits

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1. Assessing the LD-dependent architecture of human complex traits

2. Understanding which processes shaping LD patterns are involved in human complex traits

13

W< < w< Outline The LD-dependent architecture of human complex traits

Many processes correlate with LLD

• Biological mechanisms and background selection – Predicted allele age (ARGweaver, Rasmussen et al. 2014 Plos Genet)

– LLD in Africans (LLD-AFR)

– Recombination rates (HapMap 2 recombination map)

– Nucleotide diversity (AC ≥ 5)

– Background selection statistic (McVicker et al. 2009 Plos Genet)

– CpG-Content

• Baseline model of Finucane et al. (2015 Nat Genet) – 58 annotations – Coding regions, Enhancer, Promoter, Histone marks, Conserved regions …

14 Annotations corrected via MAF-stratified quantile normalization

Many annotations correlated to LD could contribute to LD-dependent architectures

• Functional annotations correlate with low LD

• Recent mutations have low LLD! 15

Many annotations correlated to LD could contribute to LD-dependent architectures

Functional annotations from the baseline model (Finucane et al. 2015 Nat Genet)

LD-related annotations

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Many LD-related annotations impact causal effect sizes

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Meta-analysis of 31 independent traits

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Meta-analysis of 31 independent traits

• Recombination rate has discordant sign of effect (Hill & Robertson 1966 Genet Res)

Heritability is enriched in SNPs with low LLD in low recombination rate regions

r = −0.49

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Meta-analysis of 31 independent traits

19

Many LD-related annotations impact causal effect sizes after conditioning on the baseline model

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cAnnotation + MAF + baseline model

Meta-analysis of 31 independent traits

• LLD effect is 0.37x smaller when including annotations from baseline model

Some, but not all, of LD-dependent architecture due to DHS, enhancers, etc.

0.37x

20

Many LD-related annotations impact causal effect sizes after conditioning on the baseline model

Annotation + MAF

LD−

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cAnnotation + MAF + baseline model

Meta-analysis of 31 independent traits

Predicted allele age has largest effect. SNPs with smaller (more recent) allele age have larger causal effect sizes Negative effects across 55/56 traits

21

Many LD-related annotations impact causal effect sizes in joint fit after conditioning on baseline model

Annotation + MAF

LD−

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cAnnotations jointly + MAF + baseline model

Annotation + MAF + baseline model

Meta-analysis of 31 independent traits

22

Many LD-related annotations impact causal effect sizes in joint fit after conditioning on baseline model

Annotation + MAF

LD−

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cAnnotations jointly + MAF + baseline model

Annotation + MAF + baseline model

Meta-analysis of 31 independent traits

6 significant annotations in joint fit

23

40%

Quintiles illustrate large effects of LD-related annotations

30%

20%

10%

0%

Pro

po

rtio

n o

f h

eri

tab

ility

Predicted

Allele Age

LLD−AFR Recombination

Rate

Nucleotide

Diversity

Background

SelectionStatistic

CpG−Content MAF

Pro

po

rtio

n o

f H

eri

tab

ilit

y

0.0

0.1

0.2

0.3

0.4

Predicted

Allele Age

LLD−AFR Recombination

Rate

Nucleotide

Diversity

Background

SelectionStatistic

CpG−Content MAF

Pro

po

rtio

n o

f H

eri

tab

ilit

y

0.0

0.1

0.2

0.3

0.4 1st quintile (lowest values)

2nd quintile

3rd quintile

4th quintile

5th quintile (highest values)

24

Youngest 20% explain 3.8x more heritability oldest 20%

1.8x

Predicted allele age is even more informative than MAF

40%

30%

20%

10%

0%

Pro

po

rtio

n o

f h

eri

tab

ility

Predicted

Allele Age

LLD−AFR Recombination

Rate

Nucleotide

Diversity

Background

SelectionStatistic

CpG−Content MAF

Pro

po

rtio

n o

f H

eri

tab

ilit

y

0.0

0.1

0.2

0.3

0.4

Predicted

Allele Age

LLD−AFR Recombination

Rate

Nucleotide

Diversity

Background

SelectionStatistic

CpG−Content MAF

Pro

po

rtio

n o

f H

eri

tab

ilit

y

0.0

0.1

0.2

0.3

0.4 1st quintile (lowest values)

2nd quintile

3rd quintile

4th quintile

5th quintile (highest values)

25

No overall effect for recombination rate

Competing effects of LLD-AFR and RR imply no overall effect for RR

40%

30%

20%

10%

0%

Pro

po

rtio

n o

f h

eri

tab

ility

Predicted

Allele Age

LLD−AFR Recombination

Rate

Nucleotide

Diversity

Background

SelectionStatistic

CpG−Content MAF

Pro

po

rtio

n o

f H

eri

tab

ilit

y

0.0

0.1

0.2

0.3

0.4

Predicted

Allele Age

LLD−AFR Recombination

Rate

Nucleotide

Diversity

Background

SelectionStatistic

CpG−Content MAF

Pro

po

rtio

n o

f H

eri

tab

ilit

y

0.0

0.1

0.2

0.3

0.4 1st quintile (lowest values)

2nd quintile

3rd quintile

4th quintile

5th quintile (highest values)

26

LD-related annotations tag background selection

• Predicted allele age: deleterious variants are younger

• LLD-AFR: information on variant history?

• Recombination rate: low recombination rate => less efficient selection (Hill & Robertson 1966 Genetics)

• Nucleotide diversity: regions under selection have lower diversity

• Background selection statistic: regions under selection are biologically important

• CpG-Content: mutation rate? unknown functional elements?

LD−

rela

ted

an

no

tati

on

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ec

t siz

e (

t*)

−0.8

−0.6

−0.4

−0.2

0.0

0.2

0.4

0.6

LD−

rela

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31 traits

Annotations jointly + MAF

+ baseline model

27

Forward simulations confirm impact of many LD-related annotations on selection coefficient s

• Forward simulations using SLiM (Messer 2013 Genetics) under African-European demographic model (Gravel et al. 2011 PNAS)

• Recombination rate and % of deleterious SNPs vary across regions, selection coeff s varies across deleterious SNPs • Jointly regress selection coeff s on 4 LD-related annotations and minor allele frequency

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6

31 traits

Forward simulations: Impact on s

Simulations

1. SNPs with lower LD have larger causal effect sizes (after conditioning on MAF) across all traits analyzed.

2. About half of this effect can be explained by known functional annotations (e.g. DHS, enhancers) with lower LD.

3. The remainder of the effect is explained by multiple LD-related annotations, including predicted allele age.

4. Forward simulations confirm that all of these findings are consistent with the action of negative selection.

28

Conclusions

29

Acknowledgments

Harvard School of Public Health: • Alkes Price • Hilary Finucane • Po-Ru Loh • Pier Palamara • Xuanyao Liu • Armin Schoech • Sasha Gusev 23andMe, Inc. • Nick Furlotte • Research participants of 23andMe

MGH/Broad Institute: • Brendan Bulik-Sullivan • Ben Neale UK Biobank

30

• Publicly available summary statistics (29) Age at Menarche, Age at Menopause, Anorexia, Autism, Bipolar, BMI, Celiac, Coronary Artery Disease, Crohn's Disease, Depressive Symptoms, Ever Smoked, Fasting Glucose, HbA1C, HDL, Height, IBD, LDL, Neuroticism, Cirrhosis, Putamen Volume, Rheumatoid Arthritis, Schizophrenia, Subject well being, Lupus, Triglycerides, Type 2 Diabetes, Ulcerative Colitis, Years of Education

• 23andMe summary statistics (19) Age at Menarche, Age voice deepened, Black hair, Chin dimple, Cup Size, Dimples, Facial stubble, Female hair loss, Hair color, Hair curl, Height, Hypermobility, IQB gray amount, Male pattern, Motion sick, Nose size, Shoe size, Unibrow, Widows peak

• UK Biobank data (15) Age at Menarche, Age at Menopause, Asthma, Blood pressure (Diastolic), Blood pressure (Systolic), BMI, College Education, Eczema, Heel T-Score, Height, Hypertension, Lung FEV1/FVC ratio, Lung Forced Expiratory Volume, Smoking Status, Waist–hip ratio (BMI adjusted)

56 traits in total (average N = 101,420) Meta-analysis over 31 independent traits (average N = 84,719) 31

56 traits analyzed (summary statistics only)

32

SNPs with smaller (more recent) allele age have larger causal effect sizes

Allele

ag

e e

ffe

ct

siz

e (

t*)

−1.0

−0.8

−0.6

−0.4

−0.2

0.0A

ge a

t M

enarc

he

An

ore

xia

Autism

Spectr

um

Blo

od P

ressure

(D

iasto

lic)

BM

I

Celia

c D

isease

Cro

hn's

Dis

ease

Cup

Siz

e

Hair C

url

He

el T−

Score

Heig

ht

IQB

Gra

y A

moun

t

Lung F

EV

1/F

VC

ratio

Lung

FV

C

Male

Pattern

Pri

mary

Bili

ary

Cirrh

osis

Rheu

mato

id A

rthri

tis

Sch

izoph

ren

ia

Shoe

Siz

e

Unib

row

Allele

ag

e e

ffe

ct

siz

e (

t*)

Meta-analysis: P = 1.60 x 10-106

Impact of predicted allele age on genetic architecture for 20 highly heritable traits

Negative effects across 55/56 traits

• Can we infer different distributions of selection coefficients s across different classes?

• Do different traits have significantly different values of selection coefficient s?

33

Futur directions