Page 1

Using Extended Genealogy to Estimate Components of

Heritability for 23 Quantitative and Dichotomous Traits

Noah Zaitlen1*, Peter Kraft2,3,4, Nick Patterson4, Bogdan Pasaniuc5, Gaurav Bhatia2,3,4,

Samuela Pollack2,3,4, Alkes L. Price2,3,4*

1Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, California, United States of America, 2Department of Epidemiology,

Harvard School of Public Health, Boston, Massachusetts, United States of America, 3Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts,

United States of America, 4Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America,

5Interdepartmental Program in Bioinformatics Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California, United States of America

Abstract

Important knowledge about the determinants of complex human phenotypes can be obtained from the estimation of

heritability, the fraction of phenotypic variation in a population that is determined by genetic factors. Here, we make use of

extensive phenotype data in Iceland, long-range phased genotypes, and a population-wide genealogical database to

examine the heritability of 11 quantitative and 12 dichotomous phenotypes in a sample of 38,167 individuals. Most previous

estimates of heritability are derived from family-based approaches such as twin studies, which may be biased upwards by

epistatic interactions or shared environment. Our estimates of heritability, based on both closely and distantly related pairs

of individuals, are significantly lower than those from previous studies. We examine phenotypic correlations across a range

of relationships, from siblings to first cousins, and find that the excess phenotypic correlation in these related individuals is

predominantly due to shared environment as opposed to dominance or epistasis. We also develop a new method to jointly

estimate narrow-sense heritability and the heritability explained by genotyped SNPs. Unlike existing methods, this approach

permits the use of information from both closely and distantly related pairs of individuals, thereby reducing the variance of

estimates of heritability explained by genotyped SNPs while preventing upward bias. Our results show that common SNPs

explain a larger proportion of the heritability than previously thought, with SNPs present on Illumina 300K genotyping

arrays explaining more than half of the heritability for the 23 phenotypes examined in this study. Much of the remaining

heritability is likely to be due to rare alleles that are not captured by standard genotyping arrays.

Citation: Zaitlen N, Kraft P, Patterson N, Pasaniuc B, Bhatia G, et al. (2013) Using Extended Genealogy to Estimate Components of Heritability for 23 Quantitative

and Dichotomous Traits. PLoS Genet 9(5): e1003520. doi:10.1371/journal.pgen.1003520

Editor: Peter M. Visscher, The University of Queensland, Australia

Received September 27, 2012; Accepted April 6, 2013; Published May 30, 2013

Copyright: ? 2013 Zaitlen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits

unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was funded by NIH grant R03HG005732 (NZ and ALP), NIH fellowship 5T32ES007142-27 (NZ), and the Rose Traveling Fellowship Program in

Chronic Disease Epidemiology and Biostatistics (NZ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of

the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: noah.zaitlen@ucsf.edu (NZ); aprice@hsph.harvard.edu (ALP)

Introduction

Although genome-wide association studies (GWAS) have

resulted in the discovery of thousands of novel associations of loci

to hundreds of phenotypes [1], concerns have been raised about

the finding that these loci appear to explain a relatively small

proportion of the estimated heritability, the fraction of phenotypic

variation in a population that is due to genetic variation [2]. This

has led to considerable speculation by researchers about the

genetic basis of complex human phenotypes and the ‘‘missing

heritability’’, i.e. the fraction of heritability not accounted for by

the associations discovered to date [3,4,5,6,7,8,9]. Among the

proposed explanations for missing heritability is the existence of

many presently unidentified common variants with small effect

sizes, rare variants not captured by current genotyping platforms,

structural variants, epistatic interactions, gene-environment inter-

actions, parent-of-origin effects, or inflated heritability estimates

[3,5,10]. Studies that examine the sources of missing heritability

can help researchers to evaluate the prospects of future studies

focusing on common versus rare variation and thereby devise

effective strategies to discover the remaining sequence variants that

affect disease risk and other aspects of phenotypic variation in

humans.

The narrow-sense heritability of a phenotype (h2) is the fraction

of phenotypic variance that can be described by an additive model

over the set of SNPs that are functionally related to the phenotype

(i.e. the causal SNPs) [11]. It is commonly estimated by comparing

the phenotypic correlation of monozygotic (MZ) to that of

dizygotic (DZ) twins. The difference between h2and the fraction

of phenotypic variance accounted for by variants discovered by

means of GWAS (h2

gwas) is the so-called missing heritability.

Recently, Yang et al [12] developed a method to estimate the

variance explained by all SNPs on a genotyping platform including

those that are not genome-wide significant (h2

limit of h2

gwasfor infinite sample size.

There are two major challenges in comparing h2and h2

g), representing the

gto

quantify missing heritability. First, there is the potential for

inflation of h2estimates based on closely related individuals such as

MZ/DZ twins. It is well known that epistatic interactions can

inflate heritability estimates in studies of related individuals [13].

PLOS Genetics | www.plosgenetics.org1 May 2013 | Volume 9 | Issue 5 | e1003520

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Recent work from Zuk et al [10] has examined this in detail.

Other factors that could also lead to inflated estimates of h2using

closely related pairs of individuals include dominance and shared

environment. Second, there is a tradeoff between inflation and

sampling variance when estimating h2

component approach described by Yang et. al results in inflated

estimatesof

h2

g

inthe presence

[12,14,15,16,17]. However, removing related individuals reduces

the sample size, resulting in a larger standard error around the

estimate [18,19]. Both of these issues can adversely affect estimates

of missing heritability.

Here, we analyze the heritability of 23 complex phenotypes in

an Icelandic cohort of 38,167 individuals, leveraging both a

population-wide genealogical database and genotype data from

over 300,000 SNPs that have been long-range phased across and

between chromosomes (i.e. where not only the phase, but also the

parental origin of alleles has been determined) [20]. Importantly,

we develop an approach that allows h2to be estimated on the basis

of both closely and distantly related pairs of individuals. We find,

for all of the quantitative phenotypes, that our estimates of h2are

smaller than those from the literature that were based on MZ/DZ

twins [21]. Our results indicate that previous estimates were

inflated by the impact of epistasis or shared environment.

We further introduce a new variance components method that

provides simultaneous estimates of h2and h2

principal advantages. First, by adequately taking account of both

closely and distantly related pairs of individuals, it minimizes the

standard error of the estimates, whilst avoiding the upward bias that

canresultfromcalculations based on closely related pairs.Second, it

produces both estimates of heritability for the same population

sample, ensuring that h2and h2

gare directly comparable.

For most of the 23 phenotypes examined here, our results show

that h2

identified many SNPs with large effect sizes (i.e. h2

h2

gwasby a considerable margin, it follows that

g. The recent variance

ofrelated individuals

g. This method has two

gaccounts for more than half of h2. As GWAS have not

gwasis small), and

gis greater than h2

there must be many associated sequence variants that remain to be

discovered, i.e. these phenotypes are highly polygenic. Currently,

only common variants are well captured by the genotyping arrays

used in most GWAS studies. As the difference between h2

is likely due to common and rare variants not captured by the

genotyping array [12], it may be assumed that a fair number of

association signals remain to be identified through more compre-

hensive approaches, such as whole genome-sequencing. However,

our estimates of h2

gshow that GWAS genotyping arrays capture a

greater proportion of h2than indicated by previous twin-based

estimates of h2.

gand h2

Results

Overview of methods

Below, we provide an overview of the approaches we used to

estimate various components of heritability. The details of these

approaches are provided in the Methods section.

We used a linear mixed model approach to estimate compo-

nents of heritability. In this approach, each phenotype is modeled

using a multivariate normal distribution. Each of the components

of heritability that we estimated corresponds to a different model

of the phenotypic covariance.

Narrow-sense heritability (h2) estimates from variance compo-

nent models rely on covariance matrices specifying the genome-

wide genetic relatedness of individuals in the data set. An estimate

of h2can be obtained by using an identity-by-descent (IBD) based

covariance matrix, which is trivial to obtain from long-range

phased genotype data (see below).

The fine-scale estimates of IBD used here rely on long-range

phasing data that are not available in most data sets. An estimate

of h2can also be obtained by using an identity-by-state with

threshold (IBS.t) based covariance matrix with all values below a

threshold t set to 0, i.e. focusing on closely related individuals. An

alternative is to use the full IBS based covariance matrix to obtain

an estimate of the heritability explained by genotyped SNPs (h2

however, this requires removing related individuals [12]. If related

individuals are included, the resulting estimate is neither an

estimate of h2nor an estimate of h2

Previous approaches to estimating the heritability explained by

genotyped SNPs (h2

g) required filtering related individuals, thereby

increasing the standard error of the estimates. However, joint

estimates of h2and h2

gcan be obtained using two covariance

matrices based on IBS.t and IBS. The first component provides

an estimate of h2, and the second provides an estimate of h2

approach removes the need to filter related individuals. Alternate-

ly, joint estimates of h2and h2

covariance matrices based on IBD and IBS, where here IBD

replaces IBS.t to estimate h2.

Broad-sense heritability (H2) is the sum of additive, dominant,

and epistatic components of heritability. The additive, dominant,

environmental (ADE) model can be used to obtain joint estimates

of dominance and additive components of heritability, using two

covariance matrices based on IBD2 (two copies shared IBD) and

IBD [22].

Below, we investigate all of these modeling approaches. Table

S1 contains definitions of all parameters quantifying components

of heritability that are used in the text.

g),

g.

g. This

gcan be obtained using two

Estimates of narrow-sense heritability (h2)

Ideally, estimates of narrow-sense heritability of a particular

phenotype would be based on a genetic relationship matrix

Author Summary

Phenotype is a function of a genome and its environment.

Heritability is the fraction of variation in a phenotype

determined by genetic factors in a population. Current

methods to estimate heritability rely on the phenotypic

correlations of closely related individuals and are poten-

tially upwardly biased, due to the impact of epistasis and

shared environment. We develop new methods to

estimate heritability over both closely and distantly related

individuals. By examining the phenotypic correlation

among different types of related individuals such as

siblings, half-siblings, and first cousins, we show that

shared environment is the primary determinant of inflated

estimates of heritability. For a large number of pheno-

types, it is not known how much of the heritability is

explained by SNPs included on current genotyping

platforms. Existing methods to estimate this component

of heritability are biased in the presence of related

individuals. We develop a method that permits the

inclusion of both closely and distantly related individuals

when estimating heritability explained by genotyped SNPs

and use it to make estimates for 23 medically relevant

phenotypes. These estimates can be used to increase our

understanding of the distribution and frequency of

functionally relevant variants and thereby inform the

design of future studies.

Components of Heritability via Extended Genealogy

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constructed from causal variants, representing the true genetic

contribution to the phenotype [23]. However, as this set of variants

is typically not known for most phenotypes, a proxy must be used

for the pair-wise genetic covariance of individuals at the causal

variants. Traditionally, this proxy has been derived from

genealogical information, representing, for each pair of individuals

in a sample, the expected fraction of their genomes that is

identical-by-descent (IBD) – i.e. identical as a result of being

inherited from a recent common ancestor [23]. The availability of

dense genome-wide data from microarray SNP genotyping

platforms has made it possible to directly estimate the fraction of

the genome shared IBD between each pair of individuals (KIBD).

However, fine-scale estimation of KIBDin population samples is

dependent on information about the chromosomal phase of alleles,

which requires long-range phasing of the data [17,24,25]. Previous

studies reporting h2estimates in close relatives based on KIBD

[26,27,28] had very high standard errors based on their study

design and sample size. Recent work has examined IBD-based

heritability estimates from distantly related individuals [29]. Ours

is the first study to provide fine-scale IBD-based estimates of h2

based on pairs of individuals at a range of relationship from

siblings to distant relatives. We refer to these estimates as h2

IBD-based estimates of h2for the 11 quantitative traits (h2

are shown in Table 1. For these and subsequent estimates of h2,

age, sex, and geographic region were included as covariates to

prevent confounding. These estimates range from 0.099 for

recombination rate to 0.691 for height. The only quantitative trait

yielding an estimate not significantly different from 0 was sex-ratio

of offspring. For each of the eight quantitative phenotypes with

published estimates of h2, our estimates were smaller than the

mean published estimate. For example, our estimate of 0.69 for

height was lower than previous estimates of 0.80 [30], but was

consistent with previous estimates in being lower for females (0.724

s.e. 0.019) than males (0.780 s.e. 0.029) [31].

Previous studies, based on either genealogical or direct estimates

of IBD sharing, have been limited to closely related individuals

(first-cousins or closer), and may therefore be upwardly biased due

to the impact of shared environment, dominance, or epistatic

interactions [10]. On average, our estimates of h2were lower than

those from previous studies by a ratio of 0.75 (s.e. 0.067), most

likely because the latter were inflated by one of the three

aforementioned factors. We return to this point below, performing

IBD.

IBD)

a pedigree-based analysis to assess the impact of these factors.

Dichotomous phenotypes in this study were ascertained to increase

the number of available cases, leading inflation in h2. A discussion

of this inflation and the resulting estimates are presented in Text

S1 and Table S2.

In most cases, researchers do not have access to long-range

phased genotypes with which to estimate h2. One suggested

solution to this problem is the use of KIBS, the genome-wide

proportion of alleles shared identical-by-state (IBS) at all geno-

typed loci, as a substitute for KIBD[32], when estimating h2on the

basis of closely-related pairs of individuals (it is assumed that KIBS

provides a poor estimate of KIBDfor distantly related pairs of

individuals). Taking advantage of long-range phase based

estimates of KIBD, we sought to evaluate the use of KIBSfor the

estimation of h2. For this purpose, we computed KIBSas defined in

[12] and found that it produced downwardly biased estimates of h2

for both simulated and real data sets that included many pairs of

distantly related individuals (see Methods). As noted by Vattikuti et

al [19], this bias is due to the fact that, when used to estimate h2,

the KIBSmatrix captures information from two distinct sources,

depending on the degree of relationship between pairs of

individuals. For large values of IBS it estimates genetic covariance

over all SNPs in the genome. For low values of IBS it estimates

genetic covariance over just those SNPs on the genotyping

platform h2

g(see next section). Thus, the resulting heritability

estimates from KIBStherefore tend to lie between the true value,

h2, and the typically lower value of h2

To avoid this bias, we implemented a different approach,

retaining all individuals for the calculation of h2, but setting values

of KIBSless than or equal to a threshold t (KIBS.t) to 0, for t=0.00,

0.025 and 0.05. This threshold defines the separation between

closely and distantly related individuals. We evaluated this

approach using both simulations and real data sets and observed

a significant downward bias of narrow-sense heritability estimated

from tresholded IBS (h2

IBSwt) at t=0. For example, when t=0

h2

(similar results were obtained for the other phenotypes). We

observed no bias at t=0.025 or t=0.05 (see Methods and Table

S3). To err on the side of caution, we present h2values for t=0.05

(h2

IBSw0:05) in Table 1 and Table S2, for the quantitative and

dichotomous traits, respectively. The difference between narrow-

g.

IBSwtfor height is 0.58, while when t=0.05 h2

IBSwt=0.70

Table 1. Narrow-sense heritability estimated from IBD (h2

IBD) and thresholding IBS (h2

IBSw0:05) for 11 quantitative traits.

Quantitative traitNa

h2

IBD

s.e.

h2

IBSw0:05

s.e.

h2

Pub

Body Mass Index (kg/m2) 200000.4220.018 0.4330.018 0.4–0.6 [6]

Cholesterol High Density Lipoprotein19977 0.4460.017 0.457 0.018 0.5 [6]

Cholesterol Low_Density Lipoprotein45470.1960.0620.1980.063 0.376 [42]

Height (cm)20000 0.6910.0160.7040.016 0.8 [6]

Menarche Age (years)15150 0.4430.0220.4540.0220.4–0.7 [43]

Menopause Age (years) 55400.400 0.0470.409 0.048 0.4–0.6 [44]

Monocyte White Blood Cell9651 0.343 0.032 0.3510.032 0.378 [42]

Waist-Hip Ratio 55380.181 0.0370.187 0.0380.3–0.6 [45]

Sex Ratio of offspring 150000.026 0.0170.0210.018-

Total Children15000 0.1030.0170.111 0.018-

Recombination Rate10259 0.099 0.0230.1100.030-

aN is the number of individuals used in the analysis of each phenotype. h2

doi:10.1371/journal.pgen.1003520.t001

Pubare previously published estimates of heritability from different populations.

Components of Heritability via Extended Genealogy

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sense heritability estimated from IBD (h2

than 0.015 for all traits and not statistically significant for any of

them. The correlation between the two estimators was 0.9998 and

0.9999 for the quantitative and dichotomous traits, respectively.

Furthermore, in our extensive simulations over real data, the

difference between the estimators was always less than 0.02 and

not statistically significant (see Methods and Tables S3, S4, S5).

These results indicate that when phase information is not available

KIBScan provide unbiased and precise estimates of h2, by means of

h2

IBSw0:05, in data consisting of a mixture of closely and distantly

related pairs of individuals. The choice of threshold t is a function

of the relatedness structure of the individuals in the study as well as

the properties of the population they are drawn from (see

Discussion).

IBD) and h2

IBSw0:05was less

Joint estimation of h2and h2

Recently, Yang et al [12] developed a method for estimating h2

the fraction of narrow sense heritability explained by genotyped

SNPs (and SNPs in LD with genotyped SNPs). The interest in h2

derives from the fact that it is the upper bound on the heritability

that can be described from GWAS (h2

genotyping platform used to estimate h2

is based on a variance component model with a genetic

relationship matrix KIBS estimated from the genotyped SNPs.

To prevent inflation, the method requires that all pairs of

individuals have KIBS,0.025 [12]. In studies where the Yang et

al. [12] approach has been applied [18,19], the removal of related

individuals resulted in a significant decrease in sample size and a

concomitant increase in the standard error of the heritability

estimates (e.g. a standard error of 19% in one study [18]). Filtering

such that KIBS,0.025 for all individuals in our data leaves less

than 3000 individuals, which is not adequate to estimate h2

low standard error(for example, resulting in a standard error for h2

of 10.0% for height).

To enable unbiased calculation of h2

both closely and distantly related pairs of individuals, we have

devised an alternative approach based on a model with two

variance components (see Methods). The first variance compo-

nent, KIBShas a parameter h2

genetic variance due genotyped SNPs. The second variance

component KIBS, has a parameter h2

h2, the total narrow-sense heritability (the subscript+is used for

both parameters to denote that they are estimated simultaneously).

Although we have access to fine-scale estimates of KIBD, based on

long-range phased genotype data, we demonstrate the application

of this approach using KIBS.t, because fine-scale KIBDestimates

are typically not available to most researchers. We note that in the

empirical results and in simulation, the use of KIBDand KIBSin the

model produced results that were similar to those obtained using

KIBS.tand KIBS(see Methods). Extensive testing of this model was

performed to demonstrate that it estimates the appropriate

quantities (see Methods), and estimates of h2

those of narrow-sense heritability estimated from tresholded IBS

and IBD (h2

IBD), both in our data and in simulations.

Table 2 shows heritability results for quantitative traits using the

joint model where heritability estimated from thresholding IBS

(h2

are fit jointly. We examined the nine quantitative traits where

h2.0. Our results are concordant with the previous estimates of h2

for height, high-density lipoprotein (HDL), WHR, and BMI

[6,19]. For most of the traits, h2

gfor quantitative phenotypes

g,

g

gwas) conducted on the same

g. The Yang et al. method

gwith

g

gin data sets that contain a

g,IBSzand is an estimate of h2

g, the

IBSwtzand is an estimate of

IBSwtzclosely match

IBSwtand h2

IBSwtz) and heritability explained by genotyped SNPs (h2

g,IBSz)

g

gaccounts for more than half of h2,

with a maximum of 0.75 for waist-to-hip ratio (WHR), and a

minimum of 0.33 for age at menopause. For each trait, we tested

for deviation from a h2

traits) and found that only height, with a value of 0.58 was

significantly different (p-value,0.0017, see Text S1). However, as

our estimates of h2were smaller than previous estimates, the

fraction 0.53 (s.e. 0.042) of variance explained by genotyped SNPs

based on our estimates of heritability was larger than the fraction

0.40 (s.e. 0.037) based on published estimates [6].

g/h2ratio of 0.53 (the average across all the

Joint estimation of narrow-sense and heritability

explained by genotyped SNPs for dichotomous

phenotypes

For dichotomous phenotypes, ascertainment in samples with

closely related pairs of individuals induces an upward bias in

narrow-sense heritability jointly estimated from IBS above a

threshold (h2

IBSwtz) when converting to the liability scale (Table S6

and Text S1). Thus, our h2

IBSwtzestimates should be viewed as an

upper bound. However, it is possible to account for case-control

ascertainment amongst distantly related pairs when converting

heritability explained by genotyped SNPs jointly estimated from

IBS below a threshold (h2

g,IBSz) from the observed to the liability

scale [33]. This correction is not possible when affected relatives

are included in the study. For example, a study that ascertains

affected sib pairs will have severely inflated heritability estimates,

and the case-control ascertainment correction does not address

this type of bias (see Text S1). Table 3 shows h2

the liability scale, derived from a model with two variance

components KIBS.tand KIBS, for 11 dichotomous traits. Estimates

of h2

g,IBSzprimarily capture the heritability derived from distantly

related pairs of individuals. Results on the observed scale are given

in Table S7. The inflated narrow-sense heritability estimates of the

dichotomous phenotypes leads to a lower ratio of heritability

explained by genotyped SNPs (h2

Our results are lower than previous estimates of the heritability

explained by genotyped SNPs (h2

g) for rheumatoid arthritis (RA), 2

diabetes (T2D), and coronary artery disease (CAD) [34]. The

differences between our estimates and previous estimates could be

due to the use of population controls in our study rather than non-

affected controls, differences in disease prevalence between

populations, differences in the genotyping platform used, differ-

ences in ascertainment strategies such as age matching in previous

work, or actual differences in the heritability of the phenotype

across populations. If a small number of common variants were

responsible for a large fraction of the phenotypic variation, they

would have been identified by previous GWAS. However, since

most of the loci identified through GWAS have a small effect, our

results suggest a highly polygenic model of disease for the

dichotomous phenotypes, as in the case of the quantitative traits.

This is consistent with previous studies [6].

g,IBSzestimates on

g) to h2.

Estimation of heritability explained by shared

environment, dominance, and epistasis

Shared environment, dominance effects and epistasis (i.e. non-

additive interaction between variants) can upwardly bias estimates

of h2in data sets that contain closely related pairs of individuals

[10]. Phenotypic covariance in siblings can be strongly affected by

dominance effects, and as siblings have correlated phenotypes due

to shared environment, these two factors are strongly confounded

[11]. In addition, Zuk et al [10] showed that epistatic interactions

also lead to inflated estimates of h2and is an additional

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confounding factor. Inflation due to any of these factors affects

interpretation of the relationship between h2

result in overestimates of missing heritability when h2is estimated

using closely related pairs of individuals. We adopted two

approaches to test for evidence of h2inflation and to determine

the extent to which it could be accounted for by shared

environment, epistasis, or dominance effects (referred to collec-

tively hereafter as dominance-like effects).

First, we estimated additive and dominance-like effects simul-

taneously under an ADE (additive, dominant, and environmental)

model with variance components KIBDand KIBD2, where the latter

represents the fraction of the genome with both chromosomes

shared IBD for each pair of individuals [26]. A likelihood ratio test

of the ADE model against the single variance component model of

KIBDwas performed (see Methods) [35], producing two heritability

estimates h2

heritability due to dominance-like effects (the subscript ‘‘.’’denotes

that these two estimates are generated simultaneously from the

same model). The sum of these two estimates is the broad sense

gand h2and is likely to

IBD.the narrow sense heritability, and h2

IBD2.the

heritability H2. The only class of relationship with significant IBD2

is siblings who share an expected J of their genome IBD2. This

analysis will therefore focus overwhelmingly on the difference

between siblings and other classes of relationship. Siblings are also

subject to epistatic interactions and shared environment and so

this analysis will be influenced by all three factors (shared

environment, dominance, and epistasis). We note that this analysis

will not detect shared environment effects that decay exactly in

proportion to genome-wide IBD.

We initially examined a subset of 11 quantitative and

dichotomous traits, viewed as likely candidates for environmental

effects, in a subset of 15,000 genotyped individuals using the ADE

framework. The results for these phenotypes are shown in Table 4,

with heritability estimates for dichotomous traits given on the

observed scale. Six phenotypes exhibited h2

significantly greater than zero, with an average value of 0.37,

indicating the impact of dominance-like effects. Hypertension in

pregnancy, T2D, CAD and osteoarthritis showed the strongest

effects (see Figure S1). While these results give clear evidence of

IBD2.

that was

Table 2. Heritability estimated from thresholding IBS (h2

IBSwtz) and heritability explained by genotyped SNPs (h2

g,IBSz).

Phenotype

h2

IBSwtz

s.e.

h2

g,IBSz

s.e.

h2

h2

IBSwtz

g,IBSz

h2

g,Pub

Body Mass Index0.424 0.0180.2290.017 0.540 0.16(0.03) [46]

Cholesterol High Density Lipoprotein 0.4500.017 0.2390.0170.531 0.12(0.05) [19]

Cholesterol Low_Density Lipoprotein0.199 0.063 0.103 0.0650.518-

Height0.687 0.0160.399 0.0170.581 0.42(0.03) [46]

Menarche Age 0.451 0.0220.225 0.0220.499-

Menopause Age0.4090.048 0.136 0.0530.333-

Monocyte White Blood Cell0.343 0.032 0.1980.0320.577-

Waist Hip Ratio0.188 0.0370.1400.0550.745 0.13(0.05) [19]

Total Children 0.102 0.0280.043 0.0230.422-

h2

doi:10.1371/journal.pgen.1003520.t002

g,Pubare previously reported estimates of h2

gwith standard errors given in ()’s.

Table 3. Narrow-sense heritability explained by genotyped SNPs (h2

g,IBSz) for dichotomous phenotypes on the liability scale.

Phenotype

h2

g,IBSz

s.e.Prevalence

h2

g,Pub

Alcohol Dependence0.2350.0300.07

Asthma0.2640.067 0.13

Autoimmune Systemic RA SLE SSc AS 0.2000.048 0.02

Autoimmune Tcell mediated0.1920.033 0.05

Breast Cancer0.1170.0510.12

Coronary Artery Disease 0.1460.017 0.06 0.39(0.06)

Hypertension in Pregnancy0.0830.0430.03

Osteoarthritis 0.126 0.0260.1

Prostate Cancer0.204 0.0560.09

Rheumatoid Arthritis**

0.261 0.0610.010.63(0.06)

0.32(0.07)*

Type 2 Diabetes0.254 0.0410.08 0.44(0.06)

h2

*RA estimate without the MHC region.

**RA in our study contained a mixture CCP positive and negative cases, while the previously published worked is based on CCP positive cases only [34].

doi:10.1371/journal.pgen.1003520.t003

g,Pubare previously reported estimates of h2

gwith standard errors given in ()’s.

Components of Heritability via Extended Genealogy

PLOS Genetics | www.plosgenetics.org5 May 2013 | Volume 9 | Issue 5 | e1003520