Estimating statistical significance of exome sequencing data for rare mendelian disorders using population-wide linkage analysis

Cornelis AlbersNHS Blood and Transplant, University of Cambridge &

Wellcome Trust Sanger Institute

Cornelis Albers

• Exome sequencing of a small (4-10) number of affected individuals only is a powerful strategy to detect high penetrance disease mutations, especially for recessive disorders

• Looks for independent mutation events in the unrelated individuals, as opposed to family based analysis where the causative mutation is shared between the affected individuals

Introduction

Cornelis Albers

Approach

• Call sequence variants from whole-exome data

• Filter out previously seen variants or variants above some population allele frequency

• Filter out variants predicted to be non-functional (e.g. intronic variants)

• See if there is a functional unit (a gene) where all affected individuals have at least two unfiltered variants (for a recessive disease).

Cornelis Albers

Four unrelated affected individuals

Cornelis Albers

Four unrelated affected individuals

Cornelis Albers

Four unrelated affected individuals

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known nsSNP

Four unrelated affected individuals

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novel nsSNP

known nsSNP

Four unrelated affected individuals

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novel nsSNP

known nsSNP

Four unrelated affected individuals

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one novel homozygous mutation or two novel heterozygous mutations in the same gene

novel nsSNP

known nsSNP

Four unrelated affected individuals

Cornelis Albers

• Only gene with two novel mutations in each GPS case

• All mutations absent from 1094 low-pass 1000 Genomes samples, 460 exomes from the 1000 Genomes project

• Amino acids conserved in most species

Mutations in NBEAL2 cause GPS

Albers, Cvejic et al. Nat Genet 2011

control GPS, paucity of !-granules

GPSGPS

Mi

GPSGPSMC OCS

V

a

A

B

C

D

L388P

I682F

E643V P2100L

I86V splice+S2269L G2553E

29.86 kb 2754 AA forward

beach

WD

ARM-like

ConA-like

InterPro

b

c d

e

!

control control

o-Dianisidine staining 3dpf

nbeal2 MO nbeal2 MO

o-Dianisidine staining 3dpf

control nbeal2 MO

cd41:EGFP

control nbeal2 MOcontrol nbeal2 MO

homozygous

homozygous

heterozygous

heterozygous 7 bp splice site insertion

Cornelis Albers

Motivation

Cornelis Albers

Motivation

• Can the filtering strategy be formalized in a statistical framework?

Cornelis Albers

Motivation

• Can the filtering strategy be formalized in a statistical framework?

• What is the statistical significance for the presence of X novel mutations in Y affected individuals in a gene?

Cornelis Albers

Motivation

• Can the filtering strategy be formalized in a statistical framework?

• What is the statistical significance for the presence of X novel mutations in Y affected individuals in a gene?

• What if we see an enrichment of low-frequency variants in a gene in the patients? When should we follow this up?

Cornelis Albers

Motivation

• Can the filtering strategy be formalized in a statistical framework?

• What is the statistical significance for the presence of X novel mutations in Y affected individuals in a gene?

• What if we see an enrichment of low-frequency variants in a gene in the patients? When should we follow this up?

• In another exome study, we found four 2% SNPs in four patients in a 1 kb window. How significant is that result?

Cornelis Albers

Motivation

• Can the filtering strategy be formalized in a statistical framework?

• What is the statistical significance for the presence of X novel mutations in Y affected individuals in a gene?

• What if we see an enrichment of low-frequency variants in a gene in the patients? When should we follow this up?

• In another exome study, we found four 2% SNPs in four patients in a 1 kb window. How significant is that result?

• Does a result reach genome-wide significance?

Cornelis Albers

Population linkage analysis

Cornelis Albers

Population linkage analysis

• With (whole genome) sequence data, the causal variants can be observed directly.

Cornelis Albers

Population linkage analysis

• With (whole genome) sequence data, the causal variants can be observed directly.

• Given a disease model (ie, penetrance values for each sequence variant), the probability of the observed phenotype can be computed directly from the sequence data.

Cornelis Albers

Population linkage analysis

• With (whole genome) sequence data, the causal variants can be observed directly.

• Given a disease model (ie, penetrance values for each sequence variant), the probability of the observed phenotype can be computed directly from the sequence data.

• 1000 Genomes individuals are healthy controls

Cornelis Albers

Population linkage analysis

• With (whole genome) sequence data, the causal variants can be observed directly.

• Given a disease model (ie, penetrance values for each sequence variant), the probability of the observed phenotype can be computed directly from the sequence data.

• 1000 Genomes individuals are healthy controls

• For ‘unrelated’ individuals, we use the coalescent to integrate over all possible relationships between affected and unaffected individuals (in pedigree linkage analysis the pedigree is known and fixed).

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Disease model

Affection status: 0,1 for individual i

vector of # derived alleles for each sequence variant segregating in the

population

vector of penetrance values for each sequence variant

• Combine multiple loci to predict phenotype, so that we can model compound heterozygosity in a gene-based analysis

Pr(ai = 1|mi, f) = 1! e!g(P

l flmi,paternall ,

Pl flm

i,maternall )

Cornelis Albers

A linkage statistic

unaffectedaffected

LOD(gene) = log101

P (a!ection status all individuals)

Mendelian full-penetrance disease with novel variants:

LOD(gene) = log10P (a!ection status all individuals|observed sequence variants in gene,disease model)

P (a!ection status all individuals|disease model)

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Algorithm

segregating site

unaffectedʼs chromosomesaffectedʼs chromosomes

affected individual 1 affected individual 2

Cornelis Albers

Algorithm

segregating site

unaffectedʼs chromosomesaffectedʼs chromosomes

affected individual 1 affected individual 2

Prob. of k segregating sites

Cornelis Albers

Algorithm

segregating site

unaffectedʼs chromosomesaffectedʼs chromosomes

affected individual 1 affected individual 2

Prob. of i segregating alleles given that the site is variable

Prob. of k segregating sites

Cornelis Albers

Algorithm

segregating site

unaffectedʼs chromosomesaffectedʼs chromosomes

affected individual 1 affected individual 2

Prob. that the mutation is present only on affected

chromosomes

Prob. of i segregating alleles given that the site is variable

Prob. of k segregating sites

Cornelis Albers

Algorithm

segregating site

unaffectedʼs chromosomesaffectedʼs chromosomes

affected individual 1 affected individual 2

Prob. that the mutation is present only on affected

chromosomes

Prob. of i segregating alleles given that the site is variable

Prob. of k segregating sites

Prob. mutation on next site hits chromosome with (no)

previous mutation(s)

Cornelis Albers

Algorithm

segregating site

unaffectedʼs chromosomesaffectedʼs chromosomes

affected individual 1 affected individual 2

Prob. that the mutation is present only on affected

chromosomes

Prob. of i segregating alleles given that the site is variable

Prob. of k segregating sites

Prob. mutation on next site hits chromosome with (no)

previous mutation(s)

Prob. that all chromosomes have at least one mutation not present on unaffacted

chromosomes

Cornelis Albers

Algorithm

segregating site

unaffectedʼs chromosomesaffectedʼs chromosomes

affected individual 1 affected individual 2

Prob. that the mutation is present only on affected

chromosomes

Prob. of i segregating alleles given that the site is variable

Prob. of k segregating sites

Prob. mutation on next site hits chromosome with (no)

previous mutation(s)

Prob. that all chromosomes have at least one mutation not present on unaffacted

chromosomes

Prob. that each individual has two chromosomes with at

least one mutation

Cornelis Albers

Algorithm

segregating site

unaffectedʼs chromosomesaffectedʼs chromosomes

affected individual 1 affected individual 2

Prob. that the mutation is present only on affected

chromosomes

Prob. of i segregating alleles given that the site is variable

Prob. of k segregating sites

Prob. mutation on next site hits chromosome with (no)

previous mutation(s)

Prob. that all chromosomes have at least one mutation not present on unaffacted

chromosomes

Prob. that each individual has two chromosomes with at

least one mutation

Coalescent is used to predict number of segregating sites and site frequency spectrum.

Cornelis Albers

Algorithm

segregating site

unaffectedʼs chromosomesaffectedʼs chromosomes

affected individual 1 affected individual 2

Prob. that the mutation is present only on affected

chromosomes

Prob. of i segregating alleles given that the site is variable

Prob. of k segregating sites

Prob. mutation on next site hits chromosome with (no)

previous mutation(s)

Prob. that all chromosomes have at least one mutation not present on unaffacted

chromosomes

Prob. that each individual has two chromosomes with at

least one mutation

Coalescent is used to predict number of segregating sites and site frequency spectrum.

Calculation does not condition on the observed mutations

Cornelis Albers

Mutation absent from resp. 50 or 500 unaffecteds and present in only one affected individual.

Mendelian recessive disorder

●

●

●

●

recessive, maximum allele count 1, 4 affected individuals

Observed number of affected individuals where both chromosomes have at least one mutation with total allele count <= maximum allele count

−log

10 p−v

alue

●

●

●

●

●

●

●

●

●

●

●

●

1 2 3 4

13

57

911

1315

1719

2123

25 50 unaffecteds, 1 kb gene500 unaffecteds, 1 kb gene50 unaffecteds, 10 kb gene500 unaffecteds, 10 kb gene

Dashed line indicates genome-wide significance threshold computed as 0.05/(Number of non-overlapping 1 kb windows in the genome).

Cornelis Albers

Mutation absent from resp. 50 or 500 unaffecteds and present in only one affected individual.

Mendelian recessive disorder

●

●

●

●

recessive, maximum allele count 1, 4 affected individuals

Observed number of affected individuals where both chromosomes have at least one mutation with total allele count <= maximum allele count

−log

10 p−v

alue

●

●

●

●

●

●

●

●

●

●

●

●

1 2 3 4

13

57

911

1315

1719

2123

25 50 unaffecteds, 1 kb gene500 unaffecteds, 1 kb gene50 unaffecteds, 10 kb gene500 unaffecteds, 10 kb gene

Dashed line indicates genome-wide significance threshold computed as 0.05/(Number of non-overlapping 1 kb windows in the genome).

Cornelis Albers

We identified 4 2% SNPs in the 3‘UTR of a gene using exome sequencing of 4 cases

Observed in follow-up in 16 additional cases (including 4 exome cases)

Dominant, reduced penetrance disorder

●

●

●

●

dominant, maximum allele frequency 2%, 500 unaffected individuals, 1 kb region

Observed number of affected individuals with at least one chromosome with at least one mutation with total allele count <= maximum allele count

−log

10 p−v

alue

●

●●

● ● ●●

●●

●

●

●

●

●

●

●

●

●

●

●

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

13

57

911

1315

1719

2123

25 4 exome−sequenced affecteds4 + 16 follow−up affecteds

Cornelis Albers

Population stratification

• Very high number of low-frequency alleles for some individuals, manifests as inflated heterozygosity

• Population stratification has severe effect on rate of low-frequency mutations and may inflate significance estimates.

• Estimate relatedness from low-frequency mutations

Number of common non-ref alleles

Num

ber

of <

5% n

on-r

ef a

llele

s 14000

2000 PCA

PC1PC

2

Cornelis Albers

Acknowledgements

• Richard Durbin

• Willem Ouwehand

• Paquita Nurden, Remi Favier, Marie-Christine Alessi, Evelien Bouwmans & Jonathan Stephens (Gray Platelet Syndrome study)

• Patrick Tarpey, Cancer Genome Project Group @ Sanger