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A model of compound heterozygous, loss-of-function alleles is broadly consistent with observations from complex-disease GWAS datasets

08 Oct 2016-bioRxiv (Cold Spring Harbor Laboratory)-pp 048819

TL;DR: It is demonstrated that the model of gene action, relating genotype to phenotype, has a qualitative effect on several relevant aspects of the population genetic architecture of a complex trait, and the genetic model impacts genetic variance component partitioning across the allele frequency spectrum and the power of statistical tests.

AbstractThe genetic component of complex disease risk in humans remains largely unexplained. A corollary is that the allelic spectrum of genetic variants contributing to complex disease risk is unknown. Theoretical models that relate population genetic processes to the maintenance of genetic variation for quantitative traits may suggest profitable avenues for future experimental design. Here we use forward simulation to model a genomic region evolving under a balance between recurrent deleterious mutation and Gaussian stabilizing selection. We consider multiple genetic and demographic models, and several different methods for identifying genomic regions harboring variants associated with complex disease risk. We demonstrate that the model of gene action, relating genotype to phenotype, has a qualitative effect on several relevant aspects of the population genetic architecture of a complex trait. In particular, the genetic model impacts genetic variance component partitioning across the allele frequency spectrum and the power of statistical tests. Models with partial recessivity closely match the minor allele frequency distribution of significant hits from empirical genome-wide association studies without requiring homozygous effect-sizes to be small. We highlight a particular gene-based model of incomplete recessivity that is appealing from first principles. Under that model, deleterious mutations in a genomic region partially fail to complement one another. This model of gene-based recessivity predicts the empirically observed inconsistency between twin and SNP based estimated of dominance heritability. Furthermore, this model predicts considerable levels of unexplained variance associated with intralocus epistasis. Our results suggest a need for improved statistical tools for region based genetic association and heritability estimation.

Topics: Genetic model (66%), Genetic architecture (60%), Genome-wide association study (60%), Genetic variation (55%), Allele frequency (55%)

Summary (2 min read)

Introduction

  • Risk for complex diseases in humans, such as diabetes and hypertension, is highly heritable yet the causal DNA sequence variants responsible for that risk remain largely unknown.
  • The authors have declared that no competing interests exist.
  • This model assigns an effect size to a mutant allele, but formally makes no concrete statement regarding the molecular nature of the allele.
  • When applied to molecular data, such as SNP genotypes in a GWAS, these models treat the SNPs themselves as the loci of interest.
  • Further, the model of gene-based recessivity best explains the differences between estimates of additive and dominance variance components from SNP-based methods [27] and from twin studies [28] and is consistent with the distribution of frequencies of significant associations in GWAS [4, 26].

Results and Discussion

  • The models As in [36],the authors simulate a 100 kilobase region of human genome, contributing to a complex disease phenotype and fitness.
  • The expected fitness effect of a mutation is always deleterious because trait effects are sampled from an exponential distribution.
  • Specifically, the authors studied three different genetic models and two different demographic models, holding the fitness model as a constant.
  • Parameters are briefly described in Table 1.

Parameter Description

  • This reflects the competing forces of increasing average genetic effect and decreasing average allele frequency which occurs as λ increases (S5 Fig).
  • Yet, when simulating iMR model, the authors find that an intermediate degree of dominance, h = 0.25, results in distribution of significant hits which is similar to the GBR results (Fig 4).
  • The reason the authors emphasize this feature of the data is to demonstrate that models with rare alleles of large effect do not necessarily imply a visual excess of rare significant GWAS hits.
  • Of the models the authors explored, only the gene-based recessive model with intermediate to large effects is consistent with the difference between twin and SNP based estimates of dominance variance (Fig 2).

Materials and Methods

  • Forward simulation Using the fwdpp template library v0.2.8 [87], the authors implemented a forward in time individualbased simulation of a Wright-Fisher population with mutation under the infinitely many sites model [88], recombination, and selection occurring each generation.
  • For comparison, the authors calculated the true total heritability in the sample as H2sample ¼ ðVG;sampleÞ=ðVP;sampleÞ.
  • Under MSGREMLd, many replicates resulted in numerical errors in GCTA.
  • For dizygotic (DZ) twins the authors used two child gamete pairs, each with a unique environmental deviate.

S10 Fig. Distribution of significant hits under site based recessive models with incomplete

  • Horizontal violin plots depict the distribution of minor allele frequencies (MAF) of the most strongly associated single marker in a GWAS.
  • Moments were calculated using the boost C++ statistical accumulators library.
  • Data are plotted as the mean across model replicates ± the standard error of the mean.
  • Shown are the additive co-dominant (AC), gene-based (GBR) and complete multiplicative recessive (Mult. recessive (h = 0); cMR) models.
  • (TIFF) S14 Fig. Regression Based Estimates of Genetic Variance.

S17 Fig. Additive genetic variance explained over allele frequency under the Tennessen

  • Et al. [40] model for European demography.
  • The left column of panels shows how VG changes over time under this model.
  • The burden ratio [91] is calculated as the ratio of genetic load between simulations with only ancient growth and those with an additional recent bottleneck and growth.
  • Here load is calculated as the average deviation from optimum fitness due to (left) fixed mutations, segregating mutations and all mutations.
  • For large effect size models, under which there are relatively more mutations that experience strong selection, the authors see the characteristic drop in the burden ratio following the bottleneck and rebound following re-expansion [91].

S21 Fig. Non-parametric comparison between empirical and simulated GWAS hits. A

  • Non-parametric comparison between distribution of allele frequencies between simulated and empirical GWAS hits.
  • In cases where more than one marker was tied for the lowest p-value, one was chosen at random.
  • Specific information regarding the empirical data can be obtained in S1 Table.
  • The dashed lines show the analytical result and the solid curves are empirical cumulative distribution functions based on a sample of 500 mutation effects from an exponential distribution.

Acknowledgments

  • The authors are thankful to Joseph Farran, Harry Mangalam, Adam Brenner, Garr Updegraff, and Edward Xia for administering the University of California, Irvine High Performance Computing cluster.
  • The authors are thankful to Kirk Lohmueller for helpful detailed comments throughout this project.
  • The authors would like to thank Peter Andolfatto, Bogdan Pasaniuc and Nick Mancuso for helpful discussion.

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RESEARCH ARTICLE
A Model of Compound Heterozygous, Loss-of-
Function Alleles Is Broadly Consistent with
Observations from Complex-Disease GWAS
Datasets
Jaleal S. Sanjak
1,2
*, Anthony D. Long
1,2
, Kevin R. Thornton
1,2
*
1 Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, California, USA,
2 Center for Complex Biological Systems, University of California, Irvine, Irvine, California, USA
* jsanjak@uci.edu (JSS); krthornt@uci.edu (KRT)
Abstract
The genetic component of complex disease risk in humans remains largely unexplained. A
corollary is that the allelic spectrum of genetic variants contributing to complex disease risk
is unknown. Theoretical models that relate population genetic processes to the maintenance
of genetic variation for quantitative traits may suggest profitable avenues for future experi-
mental design. Here we use forward simulation to model a genomic region evolving under a
balance between recurrent deleterious mutation and Gaussian stabilizing selection. We
consider multiple genetic and demographic models, and several different methods for identi-
fying genomic regions harboring variants associated with complex disease risk. We demon-
strate that the model of gene action, relating genotype to phenotype, has a qualitative effect
on several relevant aspects of the population genetic architecture of a complex trait. In par-
ticular, the genetic model impacts genetic variance component partitioning across the allele
frequency spectrum and the power of statistical tests. Models with partial recessivity closely
match the minor allele frequency distribution of significant hits from empirical genome-wide
association studies without requiring homozygous effect sizes to be small. We highlight a
particular gene-based model of incomplete recessivity that is appealing from first principles.
Under that model, deleterious mutations in a genomic region partially fail to complement
one another. This model of gene-based recessivity predicts the empirically observed incon-
sistency between twin and SNP based estimated of dominance heritability. Furthermore,
this model predicts considerable levels of unexplained variance associated with intralocus
epistasis. Our results suggest a need for improved statistical tools for region based genetic
association and heritability estimation.
Author Summary
Gene action determines how mutations affect phenotype. When placed in an evolutionary
context, the details of the genotype-to-phenotype model can impact the maintenance of
genetic variation for complex traits. Likewise, non-equilibrium demographic history may
PLOS Genetics | DOI:10.1371/journal.pgen.1006573 January 19, 2017 1 / 30
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OPEN ACCESS
Citation: Sanjak JS, Long AD, Thornton KR (2017)
A Model of Compound Heterozygous, Loss-of-
Function Alleles Is Broadly Consistent with
Observations from Complex-Disease GWAS
Datasets. PLoS Genet 13(1): e1006573.
doi:10.1371/journal.pgen.1006573
Editor: Simon Gravel, McGill University, CANADA
Received: April 18, 2016
Accepted: January 5, 2017
Published: January 19, 2017
Copyright: © 2017 Sanjak 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.
Data Availability Statement: Our simulation code
and code for downstream analyses are freely
available at: http://github.com/ThorntonLab/
disease_sims, http://github.com/molpopgen/
buRden, http://github.com/molpopgen/fwdpy, and
http://github.com/molpopgen/TennessenEAonly.
Funding: This work was supported by NIH grant
R01-GM115564 to KRT. This work was supported
by NIH grant R01-GM115562 to ADL. This material
is based upon work supported by the National
Science Foundation Graduate Research Fellowship
Program under Grant No. DGE-1321846. Any

affect patterns of genetic variation. Here, we explore the impact of genetic model and pop-
ulation growth on distribution of genetic variance across the allele frequency spectrum
underlying risk for a complex disease. Using forward-in-time population genetic simula-
tions, we show that the genetic model has important impacts on the composition of
variation for complex disease risk in a population. We explicitly simulate genome-wide
association studies (GWAS) and perform heritability estimation on population samples. A
particular model of gene-based partial recessivity, based on allelic non-complementation,
aligns well with empirical results. This model is congruent with the dominance variance
estimates from both SNPs and twins, and the minor allele frequency distribution of
GWAS hits.
Introduction
Risk for complex diseases in humans, such as diabetes and hypertension, is highly heritable yet
the causal DNA sequence variants responsible for that risk remain largely unknown. Genome-
wide association studies (GWAS) have found many genetic markers associated with disease
risk [1]. However, follow-up studies have shown that these markers explain only a small por-
tion of the total heritability for most traits [2, 3].
There are many hypotheses which attempt to explain the ‘missing heritability’ problem [2
5]. Genetic variance due to epistatic or gene-by-environment interactions is difficult to identify
statistically because of, among other reasons, increased multiple hypothesis testing burden [6,
7], and could artificially inflate estimates of broad-sense heritability [8]. Well-tagged interme-
diate frequency variants may not reach genome-wide significance in an association study if
they have smaller effect sizes [9, 10]. One appealing verbal hypothesis for this ‘missing herita-
bility’ is that there are rare causal alleles of large effect that are difficult to detect [4, 11, 12].
These hypotheses are not mutually exclusive, and it is probable that a combination of models
will be needed to explain all heritable disease risk [13].
The standard GWAS attempts to identify genetic polymorphisms that differ in frequency
between cases and controls. A complementary approach is to estimate the heritability
explained by genotyped (and imputed) markers (SNPs) under different population sampling
schemes [14, 15]. Stratifying markers by minor allele frequency (MAF) prior to performing
SNP-based heritability estimation allows the partitioning of genetic variation across the allele
frequency spectrum to be estimated [16], which is an important summary of the genetic archi-
tecture of a complex trait [1623]. This approach has inferred a contribution of rare alleles to
genetic variance in both human height and body mass index (BMI) [16], consistent with theo-
retical work showing that rare alleles will have large effect sizes if fitness effects and trait effects
are correlated [18, 2025]. Yet, simulations of causal loci harboring multiple rare variants with
large additive effects predict an excess of low-frequency significant markers relative to empiri-
cal findings [4, 26].
SNP-based heritability estimates have concluded that there is little missing heritability for
height and BMI, and that the causal loci simply have effect sizes that are too small to reach
genome-wide significance under current GWAS sample sizes [14, 16]. Further, extensions to
these methods decompose genetic variance into additive and dominance components and find
that dominance variance is approximately one fifth of the additive genetic variance on average
across seventy-nine complex traits [27]. When taken into account together with results from
GWAS, these observations can be interpreted as evidence that the genetic architecture of
human traits is best-explained by a model of small additive effects. However, a recent large
Compound Heterozygosity and Complex Traits
PLOS Genetics | DOI:10.1371/journal.pgen.1006573 January 19, 2017 2 / 30
opinions, findings, and conclusions or
recommendations expressed in this material are
those of the authors and do not necessarily reflect
the views of the National Science Foundation. 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.

twin study found a substantial contribution of dominance variance for fourteen out of eighteen
traits [28]. The reason for this discrepancy in results remains unclear. One possibility is a sta-
tistical artifact; for example, twin studies may be prone to mistakenly infer non-additive effects
when none exist. Another possibility, which we return to later, is that this apparently contra-
dictory results are expected under a different model of gene action.
The design, analysis, and interpretation of GWAS are heavily influenced by the “standard
model” of quantitative genetics [29]. This model assigns an effect size to a mutant allele, but
formally makes no concrete statement regarding the molecular nature of the allele. Early appli-
cations of this model to the problem of human complex traits include Risch’s work on the
power to detect causal mutations [30, 31] and Pritchard’s work showing that rare alleles under
purifying selection may contribute to heritable variation in complex traits [17]. When applied
to molecular data, such as SNP genotypes in a GWAS, these models treat the SNPs themselves
as the loci of interest. For example, influential power studies informing the design of GWAS
assign effect sizes directly to SNPs and assume Risch’s model of multiplicative epistasis [32].
Similarly, the single-marker logistic regression used as the primary analysis of GWAS data
typically assumes an additive or recessive model at the level of individual SNPs [33]. Finally,
recent methods designed to estimate the heritability of a trait explained by genotyped markers
assigns additive and dominance effects directly to SNPs [14, 16, 27, 34]. Naturally, the results
of such analyses are interpreted in light of the assumed model of gene action.
A weakness of the multiplicative epistasis model [30, 31] when applied to SNPs is that the
concept of a gene, defined as a physical region where loss-of-function mutations have the same
phenotype [35], is lost. Specifically, under the standard model, the genetic concept of a failure
to complement is a property of SNPs and not “gene regions” (see [36] for a detailed discussion
of this issue). We have recently introduced an alternative model of gene action, one in which
risk mutations are unconditionally deleterious and fail to complement at the level of a “gene
region” [36]. This model, influenced by the standard operational definition of a gene [35],
gives rise to the sort of allelic heterogeneity typically observed for human Mendelian diseases
[37], and to a distribution of GWAS “hit” minor allele frequencies [4, 26] consistent with
empirical results [36]. In this article, we explore this “gene-based” model under more complex
demographic scenarios as well as its properties with respect to the estimation of variance com-
ponents using SNP-based approaches [34] and twin studies. We also compare this model to
the standard models of strictly additive co-dominant effects, and multiplicative epistasis with
dominance.
We further explore the power of several association tests to detect a causal gene region
under each genetic and demographic model. We find significant heterogeneity in the perfor-
mance of burden tests [36, 38, 39] across models of the trait and demographic history. We find
that population expansion reduces the power to detect causal gene-regions due to an increase
in rare variation, in agreement with work by [22, 23]. The behavior of the tests under different
models provides us with insight as to the circumstances in which each test is best suited.
In total, our results show that modeling gene action is key to modeling GWAS, and thus
plays an important role in both the design and interpretation of such studies. Further, the
model of gene-based recessivity best explains the differences between estimates of additive and
dominance variance components from SNP-based methods [27] and from twin studies [28]
and is consistent with the distribution of frequencies of significant associations in GWAS [4,
26]. Further, the genetic model plays a much more important role than the demographic
model, which is expected based on previous work on additive models showing that the genetic
load is approximately unaffected by changes in population size over time, [21, 22]. Consistent
with recent work by [23], we find that rapid population growth in the recent past increases the
contribution of rare variants to total genetic variance. However, we show here that different
Compound Heterozygosity and Complex Traits
PLOS Genetics | DOI:10.1371/journal.pgen.1006573 January 19, 2017 3 / 30

models of gene action are qualitatively different with respect to the partitioning of genetic vari-
ance across the allele frequency spectrum. We also show that these conclusions hold under the
more complex demographic models that have been proposed for human populations [21, 40].
Results and Discussion
The models
As in [36],we simulate a 100 kilobase region of human genome, contributing to a complex dis-
ease phenotype and fitness. The region evolves forward in time subject to neutral and deleteri-
ous mutation, recombination, selection, and drift. To perform genetic association and
heritability estimation studies in silico, we need to impose a trait onto simulated individuals. In
doing so, we introduce strong assumptions about the molecular underpinnings of a trait and
its evolutionary context.
How does the molecular genetic basis of a trait under natural selection influence population
genetic signatures in the genome? This question is very broad, and therefore it was necessary
to restrict ourselves to a small subset of molecular and evolutionary scenarios. We analyzed a
set of approaches to modeling a single gene region experiencing recurrent unconditionally-
deleterious mutation contributing to a quantitative trait subject to Gaussian stabilizing selec-
tion. The expected fitness effect of a mutation is always deleterious because trait effects are
sampled from an exponential distribution. Therefore, we do not allow for compensatory muta-
tions that may occur in more general models of stabilizing selection. Specifically, we studied
three different genetic models and two different demographic models, holding the fitness
model as a constant. Parameters are briefly described in Table 1.
We implemented three disease-trait models of the phenotypic form P = G + E. G is the
genetic component, and E ¼ Nð0; s
2
e
Þ is the environmental noise expressed as a Gaussian ran-
dom variable with mean 0 and variance s
2
e
. In this context, s
2
e
should be thought of as both the
contribution from the environment and from the remaining genetic variance at loci in linkage
equilibrium with the simulated 100kb region. The genetic models are named the additive co-
dominant (AC) model, multiplicative recessive (Mult. recessive; MR) model and the gene-
based recessive (GBR) model. The MR model has a parameter, h, that controls the degree of
Table 1. Description of parameters used in the models.
Parameter Description
N Population size
P Phenotype
P
opt
Optimum phenotype
G Genetic contribution to phenotype
E Environmental contribution to phenotype
λ Mean and standard deviation of trait effects
c
i
Specific trait effect of site i
h Dominance coefficient for trait effects
w Fitness, based on Gaussian function
s
2
s
The total inverse selection intensity
s
2
e
Environmental variance
V
A
Additive genetic variance
V
D
Dominance genetic variance
V
G
Genetic variance
V
A;q x
Additive variance explained by variance below frequency q
doi:10.1371/journal.pgen.1006573.t001
Compound Heterozygosity and Complex Traits
PLOS Genetics | DOI:10.1371/journal.pgen.1006573 January 19, 2017 4 / 30

recessivity; we call this model the complete MR (cMR) when h = 0 and the incomplete MR
(iMR) when 0 h 1. Here, h = 1 corresponds to co-dominance, which is different from the
typical formulation used when modeling the fitness effects of mutations directly. It is also
important to note that here recessivity is being defined in terms of phenotypic effects; this may
be unusual for those more accustomed to dealing directly with recessivity for fitness effects.
An idealized relationship between dominance for fitness effects and trait effects of a mutation
on an unaffected genetic background is shown in S15 Fig.
The critical conceptual difference between recessive models is whether dominance is a
property of a locus (nucleotide/SNP) in a gene or the gene overall. Mathematically, this
amounts to whether one first determines diploid genotypes at sites (and then multiplies across
sites to get a total genetic effect) or calculates a score for each haplotype (the maternal and
paternal alleles). For completely co-dominant models, this distinction is irrelevant, however
for a model with arbitrary dominance one needs to be more specific. As an example, imagine a
compound heterozygote for two biallelic loci, i.e. genotype Ab/aB. In the case of traditional
multiplicative recessivity the compound heterozygote is wild type for both loci and therefore
wild-type over all; this implies that these loci are in different genes (or independent functional
units of the same gene) because the mutations are complementary. However, in the case of
gene-based recessivity [36], neither haplotype is wild-type and so the individual is not wild-
type; the failure of mutant alleles to complement defines these loci as being in the same gene
[35].
For a diploid with m
i
causative mutations on the i
th
haplotype, we may define the additive
model as
G
AC
¼
X
2
i¼1
X
m
i
j¼1
c
i;j
; ð1Þ
where c
i,j
is the effect size of the j
th
mutation on the i
th
haplotype. Each c
i,j
is sampled from an
exponential distribution with mean of λ, to reflect unconditionally deleterious mutation. In
other words, when a new mutation arises its effect c is drawn from an exponential distribution,
and remains constant throughout its entire sojourn in the population.
The GBR model is the geometric mean of the sum of effect sizes on each haplotype [36].
We sum the causal mutation effects on each allele (paternal and maternal) to obtain a haplo-
type score. We then take the square root of the product of the haplotype scores to determine
the total genetic value of the diploid.
G
GBR
¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X
m
1
j¼1
c
1;j
X
m
2
j¼1
c
2;j
v
u
u
t
ð2Þ
Finally, the MR model depends on the number of positions for which a diploid is heterozy-
gous (m
Aa
) or homozygous (m
aa
) for causative mutations,
G
MR
¼
Y
m
Aa
j¼1
ð1 þhc
j
Þ
!
Y
m
aa
j¼1
ð1 þ 2c
j
Þ
!
1: ð3Þ
Thus, h = 0 is a model of multiplicative epistasis with complete recessivity (cMR), and h = 1
closely approximates the additive model when effect sizes are small.
Here, phenotypes are subject to Gaussian stabilizing selection with an optimum at zero and
standard deviation of σ
s
= 1 such that the fitness, w, of a diploid is proportional to a Gaussian
Compound Heterozygosity and Complex Traits
PLOS Genetics | DOI:10.1371/journal.pgen.1006573 January 19, 2017 5 / 30

Figures (5)
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Additional excerpts

  • ...Importantly, very recent results from simulations appear to favor incomplete recessivity models for complex trait etiologies, demonstrating consistency with both realistic population genetic models, heritability data, and GWAS findings (Sanjak et al., 2016)....

    [...]


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TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Abstract: Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

22,115 citations


Book
01 Jan 1981
TL;DR: The genetic constitution of a population: Hardy-Weinberg equilibrium and changes in gene frequency: migration mutation, changes of variance, and heritability are studied.
Abstract: Part 1 Genetic constitution of a population: Hardy-Weinberg equilibrium. Part 2 Changes in gene frequency: migration mutation. Part 3 Small populations - changes in gene frequency under simplified conditions. Part 4 Small populations - less simplified conditions. Part 5 Small populations - pedigreed populations and close inbreeding. Part 6 Continuous variation. Part 7 Values and means. Part 8 Variance. Part 9 Resemblance between relatives. Part 10 Heritability. Part 11 Selection - the response and its prediction. Part 12 Selection - the results of experiments. Part 13 Selection - information from relatives. Part 14 Inbreeding and crossbreeding - changes of mean value. Part 15 Inbreeding and crossbreeding - changes of variance. Part 16 Inbreeding and crossbreeding - applications. Part 17 Scale. Part 18 Threshold characters. Part 19 Correlated characters. Part 20 Metric characters under natural selection.

20,268 citations


Journal Article
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

9,228 citations


Journal ArticleDOI
Paul Burton1, David Clayton2, Lon R. Cardon, Nicholas John Craddock3  +192 moreInstitutions (4)
07 Jun 2007-Nature
TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.

8,858 citations


"A model of compound heterozygous, l..." refers methods in this paper

  • ...These observations concerning the GBR model are consistent with the finding of [27] that dominance effects of SNPs do not contribute significantly to the heritability for complex traits....

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  • ...For example, influential power studies informing the design of GWAS assign effect sizes directly to SNPs and assume Risch's model of multiplicative epistasis [32]....

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  • ...A weakness of the multiplicative epistasis model [30, 31] when applied to SNPs is that the concept of a gene, defined as a physical region where loss-of-function mutations have the same phenotype [35], is lost....

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  • ...43 Similarly, the single-marker logistic regression used as the primary analysis of GWAS data typically assumes 44 an additive or recessive model at the level of individual SNPs [33]....

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  • ...Instead, the bias shown for large values of λ is likely due to the presence of substantial non-additive heritability, which is not captured by the dominance effects of SNPs....

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Journal ArticleDOI
01 Oct 1930-Nature
TL;DR: Although it is true that most text-books of genetics open with a chapter on biometry, closer inspection will reveal that this has little connexion with the body of the work, and that more often than not it is merely belated homage to a once fashionable study.
Abstract: PROBABLY most geneticists to-day are some-what sceptical as to the value of the mathematical treatment of their problems. With the deepest respect, and even awe, for that association of complex symbols and human genius that can bring a universe to heel, they are nevertheless content to let it stand at that, believing that in their own particular line it is, after all, plodding that does it. Although it is true that most text-books of genetics open with a chapter on biometry, closer inspection will reveal that this has little connexion with the body of the work, and that more often than not it is merely belated homage to a once fashionable study. The Genetical Theory of Natural Selection. Dr. R. A. Fisher. Pp. xiv + 272 + 2 plates. (Oxford: Clarendon Press; London: Oxford University Press, 1930.) 17s. 6d. net.

7,379 citations


"A model of compound heterozygous, l..." refers background in this paper

  • ...Well-tagged intermediate frequency variants may not reach 9 genome-wide significance in an association study if they have smaller effect sizes [9,10]....

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