Human Evolutionary History Has Increased The Role Of Rare Variants In Complex Phenotypes

Ryan D. Hernandezryan.hernandez@ucsf.edu

@rdhernand

Human Phenotypic Diversity

http://www.finca.org

Human Colonization of the World

NileRiver

Red Sea

AndamanIslands

Meadowcroft19,000-12,000 years ago

Kennewick9,500 years ago

Spirit Cave9,500-9,400

years ago

Monte Verde14,800 years ago

Niah Cave40,000 years ago

Qafzeh100,000years ago

Lake Mungo45,000 years ago

Malakunanja50,000 years ago

Omo KibishOldest modern human195,000 years ago

Pestera cu Oase35,000 years ago

Yana River30,000 years ago

Zhoukoudian(Shandingdong)

11,000 years ago

Minatogawa18,000 years ago

Clovis13,500years ago

Klasies River Mouth120,000 years ago

EQUATOR

40,000-30,000years ago

20,000-15,000years ago

50,000years ago

15,000-12,000years ago

200,000 years ago

70,000-50,000 years ago

40,000years ago

AUSTRALIA

ASIA

AFRICA

EUROPE

NORTHAMERICA

SOUTHAMERICA

1

2

3

4

5

6

Migration date Generalized route

Human MigrationFossil or artifact site

40,000years ago

SOURCES: SUSAN ANTON, NEW YORK UNIVERSITY; ALISON BROOKS, GEORGE WASHINGTON UNIVERSITY; PETER FORSTER, UNIVERSITY OF CAMBRIDGE; JAMES F. O'CONNELL, UNIVERSITY OF UTAH; STEPHEN OPPENHEIMER, OXFORD UNIVERSITY; SPENCER WELLS, NATIONAL GEOGRAPHIC SOCIETY; OFER BAR-YOSEF, HARVARD UNIVERSITY

NGM MAPS

© 2006 National Geographic Society. All rights reserved.

http://ngm.nationalgeographic.com

Majority of human genetic variation is rare

Class Fraction of variants < 1%Missense 92.6%

Synonymous 88.5%Non-coding 82.3%

Variants with frequency <1%

1 2 5 10 20 50 100 500 2000 5000

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Number of non−reference alleles (log scale)

Frac

tion

of v

aria

nts

missensesynonymousnoncodingneutral model

Neutral model: Essentially all variance explained by common alleles

Prop

ortio

n of

var

ianc

e ex

plai

ned

by a

llele

s w

ith fr

eq ≤

ω

5e-04 1e-03 5e-03 1e-02 5e-02 1e-01 5e-01 1e+00

0.0

0.2

0.4

0.6

0.8

1.0

Standard Neutral Model

derived allele frequency, ω

Pro

porti

on o

f var

ianc

e ex

plai

ned,

Vω/V1

Ultr

a ra

re

Inte

rmed

iate

rare

Common

5e-04 1e-03 5e-03 1e-02 5e-02 1e-01 5e-01 1e+00

0.0

0.2

0.4

0.6

0.8

1.0

Standard Neutral Model

derived allele frequency, ω

Pro

porti

on o

f var

ianc

e ex

plai

ned,

Vω/V1

Ultr

a ra

re

Inte

rmed

iate

rare

Common

5e-04 1e-03 5e-03 1e-02 5e-02 1e-01 5e-01 1e+00

0.0

0.2

0.4

0.6

0.8

1.0

Standard Neutral Model

derived allele frequency, ω

Pro

porti

on o

f var

ianc

e ex

plai

ned,

Vω/V1

Ultr

a ra

re

Inte

rmed

iate

rare

Common

Rare Variants are more deleterious

Ryan et al Nat Gen Rev (2013)Zach

Szpiech

with genetic interactions, they can be either negative, in which the combined effect of perturbing a gene in the presence of a drug is more severe than expected, or positive, in which the combined effect is less severe than expected. Similarly to genetic interactions, these can be interpreted as perturbing parallel or linear pathways respectively (FIG. 1c). However, owing to both off-target and nonspecific effects, such interpretations may be an oversimplification. It is important to note that a drug–gene interaction does not imply that the drug physically binds to the protein product of that gene. Rather, the phenotype that is associated with the perturbation of the gene is modified by the presence of the drug. This can be because the drug directly binds to the encoded pro-tein, but it is more frequently because the drug induces a cellular state in which the requirement for the protein is altered. For example, many genes that are involved in DNA damage repair show negative interactions with the DNA-damaging agent methyl methanesulphonate (MMS)57, not because they directly bind to MMS but because their functionality becomes more important in the presence of the induced DNA damage. Gene function can be either directly inferred from interac-tions with a specific drug — for example, interaction with MMS could indicate that the gene functions in the DNA damage response — or indirectly inferred through profile similarity, as genes in the same pathway tend to interact with the same drugs (FIG. 1d).

Network integration. Each of the three interaction types discussed above provides mostly orthogonal informa-tion of the same cellular components. Consequently, by integrating multiple network types it is possible to obtain insights that are not obvious from analysing a single net-work in isolation. As these integrative approaches have been reviewed elsewhere58–60, we only mention a single example of the use of integrating each pair of networks here. Protein–protein and genetic interaction data have been integrated by various groups to identify functional modules; that is, sets of proteins that are physically con-nected and that show similar genetic interaction profiles. In addition to improving the identification of known complexes, this approach has revealed pairs of com-plexes that are linked by either all negative or all positive genetic interactions, which suggest parallel and linear dependencies, respectively58,61. Similarly, others have integrated protein complexes with chemogenetic inter-actions to identify conditionally essential complexes62 (the members of which all show negative interactions with a particular drug), which suggests that the func-tion of the entire complex is required in the presence of that drug. Finally, genetic and chemogenetic interaction profiles have been successfully integrated to improve the identification of drug targets63,64.

High-resolution protein–protein interactionsIdentifying which parts of a protein are responsible for different interactions is an important step towards predicting how its function will be affected by differ-ent mutations, as well as for understanding how a single protein can carry out multiple different functions.

Box 1 | How sequence variation has an effect on proteins

High-throughput sequencing has facilitated the rapid collection of genetic variation

across human genomes. This includes both germline variation that is heritable and

somatic mutations that occur in certain cell lineages (for example, as precursors to

cancer126). Coupled with technologies that enrich DNA samples for protein-coding

regions of the genome, exome sequencing has provided a wealth of information about

genetic variants that potentially affect protein function. For somatic mutations in cancer,

the range of effects is highly diverse and dependent on both cancer type and exposure to

carcinogens (for example, tobacco smoke in lung cancers and ultraviolet radiation in skin

cancers)126. For germline mutations, most functional coding variation is rare127 and is

specific to single populations128. To illustrate the effect of sequence variation on proteins,

we summarize the distribution of single-nucleotide variants across three possible

categories: nonsense, missense and synonymous. Numerous computational techniques

have recently been developed to predict the functional significance of amino acid

substitutions; here, we use the PolyPhen-2 program to categorize missense variants as

‘benign’, ‘possibly damaging’ or ‘probably damaging’ (REF. 118).Large-scale sequencing efforts, such as the 1,000 Genomes Project (TGP)129, have

amassed a tremendous amount of data by sequencing thousands of individuals and have

had an early emphasis on exome sequencing. If mutations were completely random, we

would then expect nonsense and missense mutations to collectively make up ~72% of all

coding variants observed, with a substantial fraction of these probably affecting protein

function (see the figure, part a). This is nearly the case for the rarest of variants in the TGP

(global frequency <0.1%), for which ~63% of variants are either nonsense or missense.

However, purifying selection is an efficient evolutionary force that purges deleterious

variation or that at least restricts them from reaching high frequency. Thus, almost all

common amino acid variation is predicted to have no functional effect.

In addition to nucleotide substitutions, short insertions and deletions (indels) can also

affect protein function. Frameshift indels (that is, indels with lengths that are not

multiples of three) may be particularly deleterious, as they can have downstream effects

during translation. The signature of purifying selection that operates against frameshift

indels shows that the percentage of indels in the TGP129 that alter the reading frame of a

protein decreases as the global allele frequency increases — from 66% for the rarest

indels to 42% for the most common indels (see the figure, part b).

Nature Reviews | Genetics

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<0.001 0.001–0.01 0.01–0.10 >0.10

Benign Probablydamaging

Nonsense

<0.001 0.001–0.01 0.01–0.10 >0.10

TGP exome indels binned by allele frequency

Randomcodingvariants TGP exome variants binned by allele frequency

Perc

enta

ge o

f var

iant

sSynonymous

Non am i am ib

Possiblydamaging

a Missense

REVIEWS

868 | DECEMBER 2013 | VOLUME 14 www.nature.com/reviews/genetics

with genetic interactions, they can be either negative, in which the combined effect of perturbing a gene in the presence of a drug is more severe than expected, or positive, in which the combined effect is less severe than expected. Similarly to genetic interactions, these can be interpreted as perturbing parallel or linear pathways respectively (FIG. 1c). However, owing to both off-target and nonspecific effects, such interpretations may be an oversimplification. It is important to note that a drug–gene interaction does not imply that the drug physically binds to the protein product of that gene. Rather, the phenotype that is associated with the perturbation of the gene is modified by the presence of the drug. This can be because the drug directly binds to the encoded pro-tein, but it is more frequently because the drug induces a cellular state in which the requirement for the protein is altered. For example, many genes that are involved in DNA damage repair show negative interactions with the DNA-damaging agent methyl methanesulphonate (MMS)57, not because they directly bind to MMS but because their functionality becomes more important in the presence of the induced DNA damage. Gene function can be either directly inferred from interac-tions with a specific drug — for example, interaction with MMS could indicate that the gene functions in the DNA damage response — or indirectly inferred through profile similarity, as genes in the same pathway tend to interact with the same drugs (FIG. 1d).

Network integration. Each of the three interaction types discussed above provides mostly orthogonal informa-tion of the same cellular components. Consequently, by integrating multiple network types it is possible to obtain insights that are not obvious from analysing a single net-work in isolation. As these integrative approaches have been reviewed elsewhere58–60, we only mention a single example of the use of integrating each pair of networks here. Protein–protein and genetic interaction data have been integrated by various groups to identify functional modules; that is, sets of proteins that are physically con-nected and that show similar genetic interaction profiles. In addition to improving the identification of known complexes, this approach has revealed pairs of com-plexes that are linked by either all negative or all positive genetic interactions, which suggest parallel and linear dependencies, respectively58,61. Similarly, others have integrated protein complexes with chemogenetic inter-actions to identify conditionally essential complexes62 (the members of which all show negative interactions with a particular drug), which suggests that the func-tion of the entire complex is required in the presence of that drug. Finally, genetic and chemogenetic interaction profiles have been successfully integrated to improve the identification of drug targets63,64.

High-resolution protein–protein interactionsIdentifying which parts of a protein are responsible for different interactions is an important step towards predicting how its function will be affected by differ-ent mutations, as well as for understanding how a single protein can carry out multiple different functions.

Box 1 | How sequence variation has an effect on proteins

High-throughput sequencing has facilitated the rapid collection of genetic variation

across human genomes. This includes both germline variation that is heritable and

somatic mutations that occur in certain cell lineages (for example, as precursors to

cancer126). Coupled with technologies that enrich DNA samples for protein-coding

regions of the genome, exome sequencing has provided a wealth of information about

genetic variants that potentially affect protein function. For somatic mutations in cancer,

the range of effects is highly diverse and dependent on both cancer type and exposure to

carcinogens (for example, tobacco smoke in lung cancers and ultraviolet radiation in skin

cancers)126. For germline mutations, most functional coding variation is rare127 and is

specific to single populations128. To illustrate the effect of sequence variation on proteins,

we summarize the distribution of single-nucleotide variants across three possible

categories: nonsense, missense and synonymous. Numerous computational techniques

have recently been developed to predict the functional significance of amino acid

substitutions; here, we use the PolyPhen-2 program to categorize missense variants as

‘benign’, ‘possibly damaging’ or ‘probably damaging’ (REF. 118).Large-scale sequencing efforts, such as the 1,000 Genomes Project (TGP)129, have

amassed a tremendous amount of data by sequencing thousands of individuals and have

had an early emphasis on exome sequencing. If mutations were completely random, we

would then expect nonsense and missense mutations to collectively make up ~72% of all

coding variants observed, with a substantial fraction of these probably affecting protein

function (see the figure, part a). This is nearly the case for the rarest of variants in the TGP

(global frequency <0.1%), for which ~63% of variants are either nonsense or missense.

However, purifying selection is an efficient evolutionary force that purges deleterious

variation or that at least restricts them from reaching high frequency. Thus, almost all

common amino acid variation is predicted to have no functional effect.

In addition to nucleotide substitutions, short insertions and deletions (indels) can also

affect protein function. Frameshift indels (that is, indels with lengths that are not

multiples of three) may be particularly deleterious, as they can have downstream effects

during translation. The signature of purifying selection that operates against frameshift

indels shows that the percentage of indels in the TGP129 that alter the reading frame of a

protein decreases as the global allele frequency increases — from 66% for the rarest

indels to 42% for the most common indels (see the figure, part b).

Nature Reviews | Genetics

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<0.001 0.001–0.01 0.01–0.10 >0.10

Benign Probablydamaging

Nonsense

<0.001 0.001–0.01 0.01–0.10 >0.10

TGP exome indels binned by allele frequency

Randomcodingvariants TGP exome variants binned by allele frequency

Perc

enta

ge o

f var

iant

sSynonymous

Non am i am ib

Possiblydamaging

a Missense

REVIEWS

868 | DECEMBER 2013 | VOLUME 14 www.nature.com/reviews/genetics

with genetic interactions, they can be either negative, in which the combined effect of perturbing a gene in the presence of a drug is more severe than expected, or positive, in which the combined effect is less severe than expected. Similarly to genetic interactions, these can be interpreted as perturbing parallel or linear pathways respectively (FIG. 1c). However, owing to both off-target and nonspecific effects, such interpretations may be an oversimplification. It is important to note that a drug–gene interaction does not imply that the drug physically binds to the protein product of that gene. Rather, the phenotype that is associated with the perturbation of the gene is modified by the presence of the drug. This can be because the drug directly binds to the encoded pro-tein, but it is more frequently because the drug induces a cellular state in which the requirement for the protein is altered. For example, many genes that are involved in DNA damage repair show negative interactions with the DNA-damaging agent methyl methanesulphonate (MMS)57, not because they directly bind to MMS but because their functionality becomes more important in the presence of the induced DNA damage. Gene function can be either directly inferred from interac-tions with a specific drug — for example, interaction with MMS could indicate that the gene functions in the DNA damage response — or indirectly inferred through profile similarity, as genes in the same pathway tend to interact with the same drugs (FIG. 1d).

Network integration. Each of the three interaction types discussed above provides mostly orthogonal informa-tion of the same cellular components. Consequently, by integrating multiple network types it is possible to obtain insights that are not obvious from analysing a single net-work in isolation. As these integrative approaches have been reviewed elsewhere58–60, we only mention a single example of the use of integrating each pair of networks here. Protein–protein and genetic interaction data have been integrated by various groups to identify functional modules; that is, sets of proteins that are physically con-nected and that show similar genetic interaction profiles. In addition to improving the identification of known complexes, this approach has revealed pairs of com-plexes that are linked by either all negative or all positive genetic interactions, which suggest parallel and linear dependencies, respectively58,61. Similarly, others have integrated protein complexes with chemogenetic inter-actions to identify conditionally essential complexes62 (the members of which all show negative interactions with a particular drug), which suggests that the func-tion of the entire complex is required in the presence of that drug. Finally, genetic and chemogenetic interaction profiles have been successfully integrated to improve the identification of drug targets63,64.

High-resolution protein–protein interactionsIdentifying which parts of a protein are responsible for different interactions is an important step towards predicting how its function will be affected by differ-ent mutations, as well as for understanding how a single protein can carry out multiple different functions.

Box 1 | How sequence variation has an effect on proteins

High-throughput sequencing has facilitated the rapid collection of genetic variation

across human genomes. This includes both germline variation that is heritable and

somatic mutations that occur in certain cell lineages (for example, as precursors to

cancer126). Coupled with technologies that enrich DNA samples for protein-coding

regions of the genome, exome sequencing has provided a wealth of information about

genetic variants that potentially affect protein function. For somatic mutations in cancer,

the range of effects is highly diverse and dependent on both cancer type and exposure to

carcinogens (for example, tobacco smoke in lung cancers and ultraviolet radiation in skin

cancers)126. For germline mutations, most functional coding variation is rare127 and is

specific to single populations128. To illustrate the effect of sequence variation on proteins,

we summarize the distribution of single-nucleotide variants across three possible

categories: nonsense, missense and synonymous. Numerous computational techniques

have recently been developed to predict the functional significance of amino acid

substitutions; here, we use the PolyPhen-2 program to categorize missense variants as

‘benign’, ‘possibly damaging’ or ‘probably damaging’ (REF. 118).Large-scale sequencing efforts, such as the 1,000 Genomes Project (TGP)129, have

amassed a tremendous amount of data by sequencing thousands of individuals and have

had an early emphasis on exome sequencing. If mutations were completely random, we

would then expect nonsense and missense mutations to collectively make up ~72% of all

coding variants observed, with a substantial fraction of these probably affecting protein

function (see the figure, part a). This is nearly the case for the rarest of variants in the TGP

(global frequency <0.1%), for which ~63% of variants are either nonsense or missense.

However, purifying selection is an efficient evolutionary force that purges deleterious

variation or that at least restricts them from reaching high frequency. Thus, almost all

common amino acid variation is predicted to have no functional effect.

In addition to nucleotide substitutions, short insertions and deletions (indels) can also

affect protein function. Frameshift indels (that is, indels with lengths that are not

multiples of three) may be particularly deleterious, as they can have downstream effects

during translation. The signature of purifying selection that operates against frameshift

indels shows that the percentage of indels in the TGP129 that alter the reading frame of a

protein decreases as the global allele frequency increases — from 66% for the rarest

indels to 42% for the most common indels (see the figure, part b).

Nature Reviews | Genetics

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<0.001 0.001–0.01 0.01–0.10 >0.10

Benign Probablydamaging

Nonsense

<0.001 0.001–0.01 0.01–0.10 >0.10

TGP exome indels binned by allele frequency

Randomcodingvariants TGP exome variants binned by allele frequency

Perc

enta

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f var

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sSynonymous

Non am i am ib

Possiblydamaging

a Missense

REVIEWS

868 | DECEMBER 2013 | VOLUME 14 www.nature.com/reviews/genetics

Human-specific demography and Selection

Growth model: Gutenkunst et al (2009) Explosive growth: Tennessen et al (2012)

AFRICA EUROPE ASIA

effect size = f(demography, natural selection)

Uricchio, et al. Genome Res (2016).

with genetic interactions, they can be either negative, in which the combined effect of perturbing a gene in the presence of a drug is more severe than expected, or positive, in which the combined effect is less severe than expected. Similarly to genetic interactions, these can be interpreted as perturbing parallel or linear pathways respectively (FIG. 1c). However, owing to both off-target and nonspecific effects, such interpretations may be an oversimplification. It is important to note that a drug–gene interaction does not imply that the drug physically binds to the protein product of that gene. Rather, the phenotype that is associated with the perturbation of the gene is modified by the presence of the drug. This can be because the drug directly binds to the encoded pro-tein, but it is more frequently because the drug induces a cellular state in which the requirement for the protein is altered. For example, many genes that are involved in DNA damage repair show negative interactions with the DNA-damaging agent methyl methanesulphonate (MMS)57, not because they directly bind to MMS but because their functionality becomes more important in the presence of the induced DNA damage. Gene function can be either directly inferred from interac-tions with a specific drug — for example, interaction with MMS could indicate that the gene functions in the DNA damage response — or indirectly inferred through profile similarity, as genes in the same pathway tend to interact with the same drugs (FIG. 1d).

Network integration. Each of the three interaction types discussed above provides mostly orthogonal informa-tion of the same cellular components. Consequently, by integrating multiple network types it is possible to obtain insights that are not obvious from analysing a single net-work in isolation. As these integrative approaches have been reviewed elsewhere58–60, we only mention a single example of the use of integrating each pair of networks here. Protein–protein and genetic interaction data have been integrated by various groups to identify functional modules; that is, sets of proteins that are physically con-nected and that show similar genetic interaction profiles. In addition to improving the identification of known complexes, this approach has revealed pairs of com-plexes that are linked by either all negative or all positive genetic interactions, which suggest parallel and linear dependencies, respectively58,61. Similarly, others have integrated protein complexes with chemogenetic inter-actions to identify conditionally essential complexes62 (the members of which all show negative interactions with a particular drug), which suggests that the func-tion of the entire complex is required in the presence of that drug. Finally, genetic and chemogenetic interaction profiles have been successfully integrated to improve the identification of drug targets63,64.

High-resolution protein–protein interactionsIdentifying which parts of a protein are responsible for different interactions is an important step towards predicting how its function will be affected by differ-ent mutations, as well as for understanding how a single protein can carry out multiple different functions.

Box 1 | How sequence variation has an effect on proteins

High-throughput sequencing has facilitated the rapid collection of genetic variation

across human genomes. This includes both germline variation that is heritable and

somatic mutations that occur in certain cell lineages (for example, as precursors to

cancer126). Coupled with technologies that enrich DNA samples for protein-coding

regions of the genome, exome sequencing has provided a wealth of information about

genetic variants that potentially affect protein function. For somatic mutations in cancer,

the range of effects is highly diverse and dependent on both cancer type and exposure to

carcinogens (for example, tobacco smoke in lung cancers and ultraviolet radiation in skin

cancers)126. For germline mutations, most functional coding variation is rare127 and is

specific to single populations128. To illustrate the effect of sequence variation on proteins,

we summarize the distribution of single-nucleotide variants across three possible

categories: nonsense, missense and synonymous. Numerous computational techniques

have recently been developed to predict the functional significance of amino acid

substitutions; here, we use the PolyPhen-2 program to categorize missense variants as

‘benign’, ‘possibly damaging’ or ‘probably damaging’ (REF. 118).Large-scale sequencing efforts, such as the 1,000 Genomes Project (TGP)129, have

amassed a tremendous amount of data by sequencing thousands of individuals and have

had an early emphasis on exome sequencing. If mutations were completely random, we

would then expect nonsense and missense mutations to collectively make up ~72% of all

coding variants observed, with a substantial fraction of these probably affecting protein

function (see the figure, part a). This is nearly the case for the rarest of variants in the TGP

(global frequency <0.1%), for which ~63% of variants are either nonsense or missense.

However, purifying selection is an efficient evolutionary force that purges deleterious

variation or that at least restricts them from reaching high frequency. Thus, almost all

common amino acid variation is predicted to have no functional effect.

In addition to nucleotide substitutions, short insertions and deletions (indels) can also

affect protein function. Frameshift indels (that is, indels with lengths that are not

multiples of three) may be particularly deleterious, as they can have downstream effects

during translation. The signature of purifying selection that operates against frameshift

indels shows that the percentage of indels in the TGP129 that alter the reading frame of a

protein decreases as the global allele frequency increases — from 66% for the rarest

indels to 42% for the most common indels (see the figure, part b).

Nature Reviews | Genetics

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<0.001 0.001–0.01 0.01–0.10 >0.10

Benign Probablydamaging

Nonsense

<0.001 0.001–0.01 0.01–0.10 >0.10

TGP exome indels binned by allele frequency

Randomcodingvariants TGP exome variants binned by allele frequency

Perc

enta

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s

Synonymous

Non am i am ib

Possiblydamaging

a Missense

REVIEWS

868 | DECEMBER 2013 | VOLUME 14 www.nature.com/reviews/genetics

Prop

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5e-04 1e-03 5e-03 1e-02 5e-02 1e-01 5e-01 1e+00

0.0

0.2

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0.6

0.8

1.0

Standard Neutral Model

derived allele frequency, ω

Pro

porti

on o

f var

ianc

e ex

plai

ned,

Vω/V1

Ultr

a ra

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Inte

rmed

iate

rare

Common

5e-04 1e-03 5e-03 1e-02 5e-02 1e-01 5e-01 1e+00

0.0

0.2

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0.6

0.8

1.0

Standard Neutral Model

derived allele frequency, ω

Pro

porti

on o

f var

ianc

e ex

plai

ned,

Vω/V1

Ultr

a ra

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Inte

rmed

iate

rare

Common

5e-04 1e-03 5e-03 1e-02 5e-02 1e-01 5e-01 1e+00

0.0

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Standard Neutral Model

derived allele frequency, ω

Pro

porti

on o

f var

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Vω/V1

Ultr

a ra

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Inte

rmed

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rare

Common

Neutral model: Essentially all variance explained by common alleles

5e-04 5e-03 5e-02 5e-01

0.0

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AFR, Growth

derived allele frequency, ω

VωV1

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Evolutionary Model: Most Variance explained by Ultra Rare Alleles or common

alleles…

Evolutionary Model: Most Variance explained by Ultra Rare Alleles or common

alleles…

phenotypes (Fig. 4; Supplemental Fig. S3). We examined multipleRVATs and found that power for each test is highly dependent onthe phenotype model (Supplemental Fig. S4). We found that theSKAT framework was consistently among the most powerful, andhence focus our analysis on SKAT-O (Methods).

We find that power is substantially lower when effectsare drawn from our model as opposed to effects given by theWu et al. (2011) model of log10(x). This result holds for all

model parameters and both demographicmodels that we considered. We also findthat power is always substantially higher un-der the growth model than the explosivegrowth model. Under the explosive growthmodel, a larger proportion of the geneticvariance is due to very rare variants (as op-posed to more intermediate frequency rarevariants).

Since RVATs are tuned to detect contri-butions from rare variants, we might expectpower to increase as r increases, and rare var-iants drive a larger fraction of the variance inthe trait. Surprisingly, we find the opposite.In Figure 4 and Supplemental Figures S3and S4, we show that power decreases as r in-creases. This effect is most dramatic underthe explosive growth model (blue lines/bars) and when t = 1.0. When t decreasesto 0.5 (Fig. 4D–F), intermediate frequencyrare variants play a larger role and the reduc-tion in power is less pronounced.

We replicate the same general trendsunder an African demographic model (Supplemental Fig. S3), butpower is higher than under the European demographic model(up to 50% under some conditions) (Supplemental Fig. S5). Thismay reflect both increased trait variance per gene and differencesin genetic architecture due to demography, and suggests that theoveremphasis on European populations in sequencing studies(Rosenberg et al. 2010; Bustamante et al. 2011) may slow the dis-covery of causal loci.

Figure 3. The cumulative proportion of the genetic variance, Vx/V1, explained by variants underallele frequency x for the European “growth” (A,C) and “explosive growth” (B,D) models of humanhistory under two different values of t for a sample of n = 104 chromosomes.

Figure 4. The power of SKAT-O in Europeans as a function of the variance explained (ve) by a gene on a phenotype in a sample of size n = 104 chro-mosomes under various effect size models. The explosive growth model (B,E) of Tennessen et al. (2012) is shown in shades of blue, and the growth model(A,D) of Gravel et al. (2011) is shown in shades of red. The dashed lines show the power when the effect sizes are taken to be proportional to log10(x) foralleles at frequency x, whereas the solid lines (A,B,D,E) and bars (C,F ) show results from our phenotypemodel. Panel C aggregates data from A and B for ve =0.01, while panel F aggregates data from D and E for ve = 0.01.

Uricchio et al.

4 Genome Researchwww.genome.org

Cold Spring Harbor Laboratory Press on June 17, 2016 - Published by genome.cshlp.orgDownloaded from

phenotypes (Fig. 4; Supplemental Fig. S3). We examined multipleRVATs and found that power for each test is highly dependent onthe phenotype model (Supplemental Fig. S4). We found that theSKAT framework was consistently among the most powerful, andhence focus our analysis on SKAT-O (Methods).

We find that power is substantially lower when effectsare drawn from our model as opposed to effects given by theWu et al. (2011) model of log10(x). This result holds for all

model parameters and both demographicmodels that we considered. We also findthat power is always substantially higher un-der the growth model than the explosivegrowth model. Under the explosive growthmodel, a larger proportion of the geneticvariance is due to very rare variants (as op-posed to more intermediate frequency rarevariants).

Since RVATs are tuned to detect contri-butions from rare variants, we might expectpower to increase as r increases, and rare var-iants drive a larger fraction of the variance inthe trait. Surprisingly, we find the opposite.In Figure 4 and Supplemental Figures S3and S4, we show that power decreases as r in-creases. This effect is most dramatic underthe explosive growth model (blue lines/bars) and when t = 1.0. When t decreasesto 0.5 (Fig. 4D–F), intermediate frequencyrare variants play a larger role and the reduc-tion in power is less pronounced.

We replicate the same general trendsunder an African demographic model (Supplemental Fig. S3), butpower is higher than under the European demographic model(up to 50% under some conditions) (Supplemental Fig. S5). Thismay reflect both increased trait variance per gene and differencesin genetic architecture due to demography, and suggests that theoveremphasis on European populations in sequencing studies(Rosenberg et al. 2010; Bustamante et al. 2011) may slow the dis-covery of causal loci.

Figure 3. The cumulative proportion of the genetic variance, Vx/V1, explained by variants underallele frequency x for the European “growth” (A,C) and “explosive growth” (B,D) models of humanhistory under two different values of t for a sample of n = 104 chromosomes.

Figure 4. The power of SKAT-O in Europeans as a function of the variance explained (ve) by a gene on a phenotype in a sample of size n = 104 chro-mosomes under various effect size models. The explosive growth model (B,E) of Tennessen et al. (2012) is shown in shades of blue, and the growth model(A,D) of Gravel et al. (2011) is shown in shades of red. The dashed lines show the power when the effect sizes are taken to be proportional to log10(x) foralleles at frequency x, whereas the solid lines (A,B,D,E) and bars (C,F ) show results from our phenotypemodel. Panel C aggregates data from A and B for ve =0.01, while panel F aggregates data from D and E for ve = 0.01.

Uricchio et al.

4 Genome Researchwww.genome.org

Cold Spring Harbor Laboratory Press on June 17, 2016 - Published by genome.cshlp.orgDownloaded from

Prop

ortio

n of

var

ianc

e ex

plai

ned

by a

llele

s w

ith fr

eq ≤

x

Prop

ortio

n of

var

ianc

e ex

plai

ned

by a

llele

s w

ith fr

eq ≤

x

• Large-scale RNA sequencing + WGS

• 4 European populations

• 360 individuals

• low coverage WGS + high coverage exome: Phase 3.

• RNA-seq: median depth 58.3M reads

• Gene expression: log2 transformed, median centered, and quantile normalized.

• 10,175 unique genes.

FIN

GBR

TSI CEU

YRI

• Our sample size is small, but can we learn anything about the genetic basis of complex traits from these 10k genes?

• Let’s analyze heritability of gene expression due to cis variation (within 1Mb of gene)

• Large-scale RNA sequencing + WGS

• 4 European populations

• 360 individuals

• low coverage WGS + high coverage exome: Phase 3.

• RNA-seq: median depth 58.3M reads

• Gene expression: log2 transformed, median centered, and quantile normalized.

• 10,175 unique genes.

Heritability• Several pioneers have developed ways to infer heritability

from unrelated individuals

• Linear Mixed Models (GCTA)

• Requires complex optimization (REML) procedures that often fail with very small sample sizes.

• Biased.

• Haseman-Elston (HE) regression

• Much faster than LMM (and can run in R).

• No sample size issues!

• Unbiased.

Simulations

•Procedure

•Randomly sample 1,000 genes from genome.

•Pull all variants within 1Mb from 1000 Genomes Project Phase 3.

•Assign variants an effect size under the model.

•Perform HE regression on simulated phenotypes with real genotypes.

Simulations

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•Pool all SNVs

•Downward bias

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•Analyze 5 groups:

•Rare VS common

•Looking better!

•Analyze 2 groups:

•Rare VS common

•Common OK!

•Rare variants biased upwards

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• >1000 parameter combinations varying genetic architecture of trait

• REML fails to converge as the number of bins (K) increases

• HE increases precision across all variations of genetic architecture

• REML does not…

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.5

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total h2

Den

sity For 72.3% of all genes,

>50% of all heritability is due to rare variants!

Mean(h2tot)=21.6%Mean(h2rare)=21.0%Mean(h2common)=0.6%

Prop

ortio

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Majority of h2 due to rare variants.

phenotypes (Fig. 4; Supplemental Fig. S3). We examined multipleRVATs and found that power for each test is highly dependent onthe phenotype model (Supplemental Fig. S4). We found that theSKAT framework was consistently among the most powerful, andhence focus our analysis on SKAT-O (Methods).

We find that power is substantially lower when effectsare drawn from our model as opposed to effects given by theWu et al. (2011) model of log10(x). This result holds for all

model parameters and both demographicmodels that we considered. We also findthat power is always substantially higher un-der the growth model than the explosivegrowth model. Under the explosive growthmodel, a larger proportion of the geneticvariance is due to very rare variants (as op-posed to more intermediate frequency rarevariants).

Since RVATs are tuned to detect contri-butions from rare variants, we might expectpower to increase as r increases, and rare var-iants drive a larger fraction of the variance inthe trait. Surprisingly, we find the opposite.In Figure 4 and Supplemental Figures S3and S4, we show that power decreases as r in-creases. This effect is most dramatic underthe explosive growth model (blue lines/bars) and when t = 1.0. When t decreasesto 0.5 (Fig. 4D–F), intermediate frequencyrare variants play a larger role and the reduc-tion in power is less pronounced.

We replicate the same general trendsunder an African demographic model (Supplemental Fig. S3), butpower is higher than under the European demographic model(up to 50% under some conditions) (Supplemental Fig. S5). Thismay reflect both increased trait variance per gene and differencesin genetic architecture due to demography, and suggests that theoveremphasis on European populations in sequencing studies(Rosenberg et al. 2010; Bustamante et al. 2011) may slow the dis-covery of causal loci.

Figure 3. The cumulative proportion of the genetic variance, Vx/V1, explained by variants underallele frequency x for the European “growth” (A,C) and “explosive growth” (B,D) models of humanhistory under two different values of t for a sample of n = 104 chromosomes.

Figure 4. The power of SKAT-O in Europeans as a function of the variance explained (ve) by a gene on a phenotype in a sample of size n = 104 chro-mosomes under various effect size models. The explosive growth model (B,E) of Tennessen et al. (2012) is shown in shades of blue, and the growth model(A,D) of Gravel et al. (2011) is shown in shades of red. The dashed lines show the power when the effect sizes are taken to be proportional to log10(x) foralleles at frequency x, whereas the solid lines (A,B,D,E) and bars (C,F ) show results from our phenotypemodel. Panel C aggregates data from A and B for ve =0.01, while panel F aggregates data from D and E for ve = 0.01.

Uricchio et al.

4 Genome Researchwww.genome.org

Cold Spring Harbor Laboratory Press on June 17, 2016 - Published by genome.cshlp.orgDownloaded from

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•We find a tight coupling between evolutionary parameters and effect sizes!

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RECONSTRUCTING THE GENETIC ARCHITECTURE OF 7 MAJOR DISEASES

• WTCCC: ~2000 cases and 3000 shared controls across 7 common diseases

• Joint imputation of case-control panels (HRC)

• HE regression using K=50 bins

• Drop variants with MAF < 0.5% due to quality

RECONSTRUCTING THE GENETIC ARCHITECTURE OF 7 MAJOR DISEASES

0.0 0.1 0.2 0.3 0.4 0.5

0.0

0.2

0.4

0.6

0.8

1.0

MAF

Type 2 DiabetesCoronary Artery DiseaseHypertensionType 1 DiabetesRheumatoid ArthritisCrohn's DiseaseBipolar Disorder

Cum

ulat

ive h

2

●●

●

●

●●

●

Bipolar Disorder

Hypertension

Type 2 Diabetes

Crohn's Disease

Coronary Artery Disease

Rheumatoid Arthritis

Type 1 Diabetes

0.0 0.2 0.4 0.6 0.8 1.0

47.7 %

43.1 %

38.7 %

38.7 %

37.9 %

30.6 %

16.5 %

Fraction of h2 due to MAF<=1%

Bipolar Disorder

Hypertension

Type 2 Diabetes

Crohn's Disease

Coronary Artery Disease

Rheumatoid Arthritis

Type 1 Diabetes

0.0 0.2 0.4 0.6 0.8 1.0

47.7 %

43.1 %

38.7 %

38.7 %

37.9 %

30.6 %

16.5 %

Fraction of h2 due to MAF<=1%

RECONSTRUCTING THE GENETIC ARCHITECTURE OF 7 MAJOR DISEASES

0.0 0.1 0.2 0.3 0.4 0.5

0.0

0.2

0.4

0.6

0.8

1.0

MAF

Type 2 DiabetesCoronary Artery DiseaseHypertensionType 1 DiabetesRheumatoid ArthritisCrohn's DiseaseBipolar Disorder

Cum

ulat

ive h

2

●●

●

●

●●

●

Bipolar Disorder

Hypertension

Type 2 Diabetes

Crohn's Disease

Coronary Artery Disease

Rheumatoid Arthritis

Type 1 Diabetes

0.0 0.2 0.4 0.6 0.8 1.0

47.7 %

43.1 %

38.7 %

38.7 %

37.9 %

30.6 %

16.5 %

Fraction of h2 due to MAF<=1%

Bipolar Disorder

Hypertension

Type 2 Diabetes

Crohn's Disease

Coronary Artery Disease

Rheumatoid Arthritis

Type 1 Diabetes

0.0 0.2 0.4 0.6 0.8 1.0

47.7 %

43.1 %

38.7 %

38.7 %

37.9 %

30.6 %

16.5 %

Fraction of h2 due to MAF<=1%

4 A U G U S T 2 0 1 6 | V O L 5 3 6 | N A T U R E | 4 1

ARTICLEdoi:10.1038/nature18642

The genetic architecture of type 2 diabetesA list of authors and affiliations appears in the online version of the paper

There is compelling evidence that the individual risk of type 2 diabetes (T2D) is strongly influenced by genetic factors1. Progress in charac-terizing the specific T2D-risk alleles responsible has been catalysed by the ability to perform genome-wide association studies (GWAS). Over the past decade, successive waves of T2D GWAS—featuring ever larger samples, progressively denser genotyping arrays supplemented by imputation against more complete reference panels, and richer ethnic diversity—have delivered more than 80 robust association signals2–8. However, in these studies, the alleles interrogated for association were predominantly common (minor allele frequency (MAF) >5%), and with limited exceptions7,9, the variants driving known association signals were also common, with individually modest impacts on T2D risk2–8,10. Variation at known loci explains only a minority of observed T2D heritability2,3,11.

Residual genetic variance is partly explained by a long tail of com-mon variant signals of lesser effect2. However, the contribution to T2D risk that is attributable to lower-frequency variants remains a matter of considerable debate, not least because of the relevance of disease architecture to clinical application11.

Next-generation sequencing enables direct evaluation of the role of lower-frequency variants to disease risk7,12,13. This paper describes the efforts of the coordinated, complementary strategies pursued by the Genetics of Type 2 Diabetes (GoT2D) and Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) consortia. GoT2D collected comprehensive genome-wide sequence data from 2,657 T2D cases and controls; T2D-GENES focused on exome sequence variation, assembling data (after inclu-sion of GoT2D exomes) from a multiethnic sample of 12,940 indi-viduals. Both consortia used genotype data to expand the sample size available for association testing for a subset of the variants exposed by sequencing.

Analysis of genome-wide variationThe GoT2D consortium selected for whole-genome sequencing cases of type 2 diabetes (T2D) and ancestry-matched normogly-caemic controls from northern and central Europe (Methods and Supplementary Table 1). To increase power to identify low-frequency (0.5% < MAF < 5%) and rare (MAF < 0.5%) T2D variants with large effects, we preferentially identified individuals from the extremes of genetic risk (Methods). The genome sequence of 1,326 cases and 1,331 control individuals was determined through joint statistical analy-sis of low-coverage whole-genome sequence (∼5×), deep-coverage

exome sequence (∼82×), and array-based genotypes at 2.5 million single nucleotide variants (SNVs) (Extended Data Fig. 1 and Extended Data Table 1).

We detected, genotyped, and estimated haplotype phase for 26.7 million genetic variants (Extended Data Fig. 1 and Extended Data Table 2), including 1.5 million short insertion-deletion variants (indels) and 8,876 large deletions. Individual diploid genomes carried a mean of 3.30 million variants (range: 3.20 million–3.35 million), including 271,245 indels (262,201–327,077), and 669 (579–747) large deletions. These data include many variants not directly studied by previous GWAS, including all of the indels as well as 420,473 com-mon and 2.4 million low-frequency SNVs that were poorly tagged (r2 ≤ 0.30)3,4 by genotype arrays. We estimate near-complete ascer-tainment (98.2%) of SNVs with minor allele counts of greater than 5 (MAF > 0.1%), and high accuracy (over 99.1%) at heterozygous genotypes (Methods and Fig. 1a). As half of the sequenced individ-uals were T2D cases, ascertainment was enhanced for any rare or low-frequency variants that substantially increase T2D risk (Fig. 1a). Specifically, we estimate ≥80% power to detect (at genome-wide significance, α = 5 × 10−8) T2D risk variants with MAF ≥ 5% and odds ratio (OR) ≥ 1.87, or MAF ≥ 0.5% and OR ≥ 4.70 (Extended Data Fig. 2).

We tested all 26.7 million variants for T2D association by logistic regression assuming an additive genetic model (Supplementary Table 2). Analyses using a mixed-model framework to account for popula-tion structure and relatedness generated almost identical results. At genome-wide significance, 126 variants at four loci were associ-ated with T2D (Fig. 1b). These included two previously reported common-variant loci (TCF7L2 and ADCY5), a previously reported low-frequency variant in CCND2 (ref. 7) (rs76895963, MAF = 2.6%, Pseq = 4.2 × 10−9), and a novel common-variant association near EML4 (MAF = 34.8%, Pseq = 1.0 × 10−8). There was no significant evidence of association with T2D for sets of low-frequency or rare variants within coding regions, nor within specified non-coding regulatory elements (Methods).

Power to detect association with low-frequency and rare variants of modest effect is limited in a sample of 2,657 individuals. To increase power for variants discovered via genome sequencing, we imputed sequence-based genotypes into 44,414 additional individuals of European origin (11,645 T2D cases and 32,769 controls; Methods) from 13 studies (Supplementary Table 3). We estimated power in the combined sequence plus imputed data, adjusting for imputation

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

© 2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

major role in predisposition to type 2 diabetes.large-scale sequencing does not support the idea that lower-frequency variants have a

0.0 0.5 0.8 0.9 0.95 0.99 1.0

τ = 1.0, AFR

ρ

Pow

er, α=2.5e-06

0.0

0.2

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1.0

GrowthAccelerated growth

Power of SKAT-O may be sensitive to demography & selection

Rare variants drive phenotypic variation

Common variants drive phenotypic variation

Summary

•We still know little about the genetic basis of most complex traits

•Our evolutionary modeling gives insights into what the patterns of heritability should look like

•Analyzing heritability patterns across ~10k gene expression patterns demonstrates a major impact of rare variants across most genes.

•Rare variants contribute substantially to some common diseases

•But our power to detect causal loci using existing tests is very poor.

@rdhernand

Thanks!

Ryan D. Hernandez

Noah Zaitlen

Jimmie Ye

John Witte