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Genetical genomics in humans and model organisms
Dirk-Jan de Koning and Chris S. Haley
The Roslin Institute, Roslin, Midlothian, UK, EH25 9PS
Genetical genomics has been proposed to map loci
controlling gene-expression differences (eQTLs) that
might underlie functional trait variation. We briefly
review the studies in model species and conclude that,
although they successfully demonstrate the utility of
genetical genomics, they are too limited to unlock the
full potential of this approach and some results should
be interpreted with caution. We subsequently elaborate
on two recent studies that use this approach in humans.
The many differences between these studies complicate
meaningful comparisons between them. A joint analysis
of the two experiments offers some scope for morepowerful genetical genomics.
Introduction
Genetical genomics describes the combined study of
gene expression and marker genotypes in a segregat-
ing population [1,2]. It aims to detect the genomic loci
that control gene-expression differences, these loci are
referred to as expression quantitative trait loci
(eQTLs; see Glossary).
To date, most of these studies have used model species
such as mice [3–5], maize [3], rats [6] and yeast [7,8]. The
experimental designs include recombinant inbred lines(RI; in rodents) [4–6], F2 or F3 crosses (in mice and maize)
[3] and haploid lines (in yeast) [7–9]. The common feature
of these designs is that, compared with ‘traditional’
phenotype-based QTL experiments, the sizes of the exper-
iments are modest to small. We have compared the
statistical power to detect different QTL effects among the
different eQTLs studies to date and comment on potential
shortcomings (Box 1). The limited size of experiments can be
attributed to the expense of gene-expression analyses.
However, this should encourage collaborative efforts to
perform more powerful eQTL studies rather than multiple
studies that each lack sufficient power.
Cis and trans eQTL
eQTL can be classified as cis or trans acting based on the
location of the transcript compared with that of the eQTL
influencing the expression of that transcript. There is
variation between studies in exactly how cis and trans are
defined, but generally the genome is divided into segments
(bins; to allow for inherent inaccuracy in the mapping of
eQTL) based on physical or mapping distance {e.g. 20kb in
yeast [7], 5MB [4,5] or 2 cM (w3.6 MB) in mice [3] and
20 MB in rats [6]}. A QTL is cis acting if it is located in the
same bin as the transcript it influences, otherwise it is
termed trans acting.
Differences in microarray platform and their effect on
eQTL studies
Differences in performance between microarray platforms
have been discussed in detail elsewhere [10]. Because
genetical genomics combines sequence polymorphisms
with variation in expression levels, it is important to
establish how robust the RNA measurement is against
sequence variation [e.g. single nucleotide polymorphisms
(SNPs)] in the transcript. The robustness of Affymetrixchips (http://www.affymetrix.com ) against spurious cis-
effects resulting from SNPs in the transcripts has been
evaluated by re-sequencing some of the genes with cis-
effects in rats [6] and by using available SNP data in mice
[5]. Both studies concluded that the effect of SNP variation
on the detection of cis-acting eQTLs was limited. An
alternative approach for Affymetrix chips would be to
study probe–eQTL interactions for cis-acting eQTL
because Affymetrix chips use multiple probes to inter-
rogate each transcript (Ritsert Jansen, personal com-
munication). Agilent 60-mer oligonucleotide arrays were
shown to be robust against four SNPs or less in the probe
region [11].
Major hubs of genes regulation: fact or artefact?
A common feature of eQTL studies is the detection of
‘hotspots’ or hubs of trans-acting eQTL: chromosomal
regions that affect the expression of a much larger number
Glossary
Bonferroni correction: a statistical adjustment for multiple comparisons. The
Bonferroni correction is simple: if a number ( n ) of outcomes are being tested
instead of a single outcome, the desired threshold level (P ) is divided by n .
False discovery rate: the proportion of false-positive test results among all
significant tests (note that the FDR is conceptually different to the significance
level).
Haploid line: a line that is derived by crossing two strains and subsequently
manipulating the F1 gametes to develop into fully homozygous individuals.Heritability: a statistic that estimates the proportion of variation in a trait that is
attributable to genetic factors.
Phenotypic standarddeviations: a statisticthat describes thedispersionof data
about the mean.
Quantitative trait locus: genetic locior chromosomalregionsthat contribute to
variability in complex quantitative traits, as identified by statistical analysis.
Quantitative traits are typically affected by several genes and by the
environment.
Recombinant inbred lines: a strain that is formed by crossing two strains,
followed by 20 or more consecutive generations of brother–sister mating or
selfing. The resulting lines are homozygous (and therefore fixed) at each locus,
enabling repeated replicates of genetically homogeneous lines to be assayed.
Statistical power: a statistic that describes how effective a given experiment is
to detect a certain effect. Statistical power is expressed as the proportion of
teststhat are expectedto be significant given a certainexperimentand a certain
effect.Corresponding author: de Koning, D.-J. ([email protected]).
Available online 23 May 2005
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of genes than expected by chance. These major hubs of gene regulation are most prominent in yeast (eight) [7,8],
followed by mice (approximately seven) [3–5]. Clustering
of eQTL was not reported for maize [3]. The locations of
the trans-acting eQTL show limited overlap among the
three mouse eQTL studies [3–5], which could be due to
tissue-specific trans regulation. Although the most sig-
nificant eQTL are cis-acting, the detection of trans-acting
regulatory hubs is plausible if cis-regulation provides
more direct (i.e. less variable) genetic control than trans
regulation, ensuring that cis-acting effects are larger and
more consistent. Alternatively, it could be that the
proportion of false positive eQTL is greater among trans-
acting effects.The strong clustering in ‘hubs’ of eQTLs reflects the
highly correlated expression levels of many gene tran-
scripts. This is illustrated by a recent simulation study
using real expression data from human pedigrees with a
simulated SNP map that was independent of the
expression levels [12]. As a result, all eQTLs detected
were by default false positives. The eQTL analyses showed
strong clustering of (trans) eQTLs and the five most
populated bins contained 20% of the significant, but
spurious, eQTLs [12]. Thus, although both the high
correlation of expression levels among gene transcripts
and the detection of eQTL hotspots in experimental
studies can be interpreted to support the hypothesis of coordinated trans-regulation of multiple genes, a major
concern is whether the correlation could be due to some
technical or environmental factors that are currently
unaccounted for. For example, the clustering of eQTL for
multiple traits could simply represent the clustering of
spurious QTL for highly correlated traits (i.e. with so
many traits we expect to see many false-positive QTL
effects, and if traits are highly correlated, for whatever
reason, these false-positive QTLs will often locate to the
same region). Because of the limited understanding of
genetic and physiological control of gene expression and
the limited experimental sizes so far, any conclusions with
regard to hotspots for gene regulation should be inter-preted with caution.
eQTL studies in human cell lines
Although the genetic complexity of most eQTL studies is
limited because of the use of inbred resources, two
recent studies report eQTL in analyses of cell lines
derived from human pedigrees [13,14]. These initial
studies both used lymphoblastoid cell lines from the
CEPH pedigrees (http://www.cephb.fr/cephdb/ ) but other-
wise have differences at almost every level of execution
(Table 1). Many of the differences between the two studies
are not unique to genetical genomics: discrepancies
Box 1. The power of eQTL studies to date
Table I summarizes the statistical power to detect QTL for some eQTL
studies to date and compares these with hypothetical F2 designs that
are commonly encountered in QTL detection. For example, an eQTL
with a Heritability of 0.03 (i.e. the eQTL explains 3% of the variation in
RNA abundance among the F2 mice) would be detected in 7% of the
experiments performed with 111 F2 mice [3] a nd 1 6% of the
experiments with 86 haploid yeast lines [8].
Although the experiment using 112 haploid yeast lines [9] is themost powerful of all the studies, most studies have limited power to
detect any QTL with an effect !0.5 phenotypic standard deviations
(SD; equivalent to a QTLheritability of 0.13).As a result, the studies fail
to detect many loci with moderate effects on gene regulation and are
also expected to miss some loci with major effects. The statistical
threshold that we have used for the power calculations is reasonably
stringent for a single trait, but fairly liberal overall, considering that
eQTL studies commonly examine the expression levels of thousands
of genes. This is a major issue in genetical genomics because it uses
multiple testing in two dimensions: hundreds of markers are tested for
their putative effect on O10 000 gene transcripts. Traditional
approaches, such as the Bonferroni correction, that limit the discovery
of spurious effects by increasing the stringency on the statistical
significance threshold are demanding as the thresholds becomeprohibitive for the detection of all but the most extreme effects.
Alternatives such as the false discovery rate have been proposed for
genome scans and gene-expression studies [15], and an overview of
multiple testing issues and alternatives in genetics was recently
presented by Manly et al. [16].
Table I. A comparison of statistical power to detect QTL in eQTL studies
Refs Population Na Statistical power for different QTL effectsb
QTL effect (phenotypic SD)c 0.25 0.40 0.5 0.6 0.75
QTL heritability in F2 (variance explained)d 0.03 0.08 0.13 0.18 0.28
Brem et al. [7] Haploid yeast 40 0.05 0.2 0.51 0.73 0.99
Yvert et al. [8] Haploid yeast 86 0.16 0.67 0.94 0.99 0.99
Brem and Kruglyak [9] Haploid yeast 112 0.25 0.84 0.99 0.99 0.99
Schadt et al. [3] F2 mice 111 0.07 0.37 0.68 0.90 0.99
Schadt et al. [3] F3 maize 76 0.04 0.19 0.41 0.67 0.94Chesler et al. (mice);
Bystryk et al. (mice);
Hubner et al. (rats) [4–6]
Recombinant inbred linese 33 0.05 0.29 0.62 0.91 0.99
Hypothetical F2 200 0.21 0.77 0.96 0.99 0.99
Hypothetical F2 400 0.60 0.99 0.99 0.99 0.99aNumber of individuals with expression data.bThe probability of detecting as significant a QTL using a point-wise significance threshold of P !0.001, which corresponds to a LOD score of 3.0 for an F2 design (slightly
morestringent thanthe proposedthreshold forsuggestive linkagebut muchless stringent thanthe threshold for significant linkage [17]). Thepower calculationsaccount
fordifferent experimentaldesigns but notfor different genome length betweenspecies(the greaternumberof independenttests performed in a larger genome requiresa
more stringent significance threshold).cAdditive effect of the QTL (half of the difference between homozygotes) expressed in units of the phenotypic standard deviation.dThe proportion of the totalvariationin the population explained by the QTL,assuming an F2 populationwhere theQTLallelefrequenciesareboth0.5.In anRI orhaploid
system, the heritability of the QTL is twice the magnitude in an F 2.eAssuming a repeatability of 0.50 for gene transcripts and three replicates for every recombinant inbred (RI) line.
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between gene-expression platforms, different statistical
methods and protocols are common obstacles when
comparing different microarray studies. Although the
studies overlap for about half (eight) of the CEPH families
studied, they use different genetic marker sets and
different methods for expression analysis and eQTL
analysis. Furthermore, they use different criteria for
including genes in their eQTL analysis and apply different
thresholds for QTL detection (Table 1). The results
between the two studies are also remarkably different:
Morley et al. take w42% of the genes (nZ3554) on theirarrays forward to eQTL analysis, whereas Monks et al.
use only w10% (nZ2430; Table 1). At comparable
significance levels (3.7!10K5 and 5.0!10K5, respect-
ively), Morley et al. report eQTL for w28% of the genes
that were taken forward to eQTL analysis compared with
w2% for Monks et al. (Table 1). Figure 1 shows the
theoretical power for detection of QTL for the two studies
using the two methods of QTL analysis. The QTL methods
are briefly explained in Box 2. For the sib-pair analyses,
both studies had similar power. The power calculations
confirm that variance component methods such as
sequential oligogenic linkage analysis routines (SOLAR)
are theoretically slightly more powerful than sib-pair
analyses, because they use all of the genetic relationships
within the pedigree. However, the power difference does
not explain the marked difference in numbers of QTL
detected by the two studies. The greater number of eQTLs
for the Morley et al. study could be due to several factors
including: (i) less technical noise in gene-expression
measurements, resulting in a larger proportion of the
variance attributable to the QTL effect; (ii) environmental
conditions that promote greater genetically controlled
variation in expression; or (iii) less robust gene-expressionmeasurements or analyses, making the results more
prone to bias and false positive results. Given the low
power of both studies to detect eQTLs under the stringent
thresholds that they apply, the results of Monks et al. are
more consistent with prior expectation, unless eQTL
effects are much stronger than those of phenotypic QTL.
Although the low theoretical power does not explain why
Morley et al. detect more QTL than Monks et al., it would
explain differences in genes for which eQTL are detected,
in addition to discrepancies in finding eQTL in different
locations for a particular transcript. When both studies
have limited power to detect a given QTL, they will each
Table 1. A comparison between two eQTL analyses on human CEPH dataa
Morley et al. [14] Monks et al. [13]
CEPH families used 14 (eight in common) 15 (eight in common)
Gene expression
Platform Affymetrix genome focus 25-mer
oligonucleotide arrays
Agilent 60-mer oligonucleotide array
Genes on array w8500 23 499
Design and replicates Direct m easurement with t wo array replicates
per individual
Reference design with at least two arrays per
individual
Criterion for selecting genes for eQTLanalysis
Greater variation between individuals thanwithin
Differentially expressed in at least half of theoffspring
Genes taken forward to eQTL analysis 3554 2430
Marker genotypes 2756 autosomal SNP markers from the SNP
consortium database
346 autosomal markers, selected from CEPH
genotype database
Data availability Genotypes available at http://www.ceph/fr/
cephdb
Genotypes available at http://www.ceph/fr/
cephdb
Expression data at http://www.ncbi.nlm.nih.
gov/geo/ (GEO accession GSE1485)
Expression data at http://www.ncbi.nlm.nih.
gov/geo/ (GEO accession GSE1726)
eQTL analyses (i) Sib-pair analyses using S.A.G.E. for whole
genome analysis
Variance component analyses using SOLAR
for both heritability of transcript level and
eQTL
(ii) QTDT and association study for 17 genes
with cis-acting eQTL
Test for hubs of gene regulation 5 MB genome bins, testing for deviation from
poisson distribution
At 4 cM (w3.2 MB) intervals comparing
number of hits with those obtained by
simulationeQTL results 142 genes with at least one eQTL
(P !4.3!10K7)
33 genes with at least one eQTL
(P !5.0!10K6)
984 genes with at least one eQTL
(P !3.7!10K5)
50 genes with at least one eQTL
(P !5.0!10K5)
135 genes with at least one eQTL
(P !5.0!10K4)
Hubs of gene regulation Two hotspots on chromosomes 14 and 20
affecting seven and six genes, respectively
(using P !4.3!10K7) or 31 and 35 genes,
respectively (using P Z3.7!10K5)b
Six locations with five or six linkage hits on
chromosome 6; according to the authors,
these are attributable to allelic diversity and
non-specificity of gene probes and were
therefore dismissed
Other analyses Hierarchical clustering of genes within 5 MB
window on chromosome 14
Test for enrichment of certain annotations
among differentially expressed genes
RT–PCR for one gene with a large cis effect 574 genes with non-zero heritability; these
were subsequently clustered using genetic
or phenotypic correlationsaAbbreviations: GEO, gene expression omnibus.bA different number of genes are affected by the eQTL, depending on the P value used.
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only detect a small proportion of actual eQTL and are
hence unlikely to detect the same effects.
Both studies agree that the most significant QTL
appear to be cis-acting, whereas the proportion of cis
acting eQTL is smaller in Morley et al. (w22%) than in
Monks et al. (w40%) for the most stringent significance
levels. However, although Morley et al. claim support for
two trans-acting hubs of regulation on chromosomes 14
and 20, Monks et al. claim ‘lack of evidence for linkage
hotspots’, although their permutations show that eQTLare significantly ‘unevenly distributed’. However, Monks
et al. make their statement based on the eQTL with P!
0.000005, whereas Morley et al. use P!0.000037 (7.4
times larger) to claim the larger hubs. Therefore, the
difference in threshold, and the difference in genes that
were analysed, could explain this discrepancy.
An interesting aspect of the Morley et al. article is the
follow-up analyses on cis-acting QTL: they perform a
within family association test [quantitative transmission
disequilibrium test (QTDT); Box 2] with additional SNP
markers for 17 transcripts. Furthermore, they re-estimate
the magnitude of these QTL effects by a regression
analyses on the grandparent data, giving a more realistic
estimate of the actual QTL effect. This provides a solution
to the problem that when QTLs are initially detected in a
study with low power, the effects of those that are detected
can be grossly overestimated. This overestimation of QTL
effects is apparent in the article by Monks et al., who
report genes with two, three and even one gene with 15
eQTLs. Subsequently, they claim that ‘all detectable QTL
accounted for at least 50% of the trait variance with 75% of
the QTL having heritabilities O0.76’. This illustrates thelevel at which QTL effects are overestimated: it is
impossible to have 15 eQTL, each explaining 50% of the
trait variance. This phenomenon is not unique to eQTL,
but it illustrates the issue particularly well.
Morley et al. confirm one of their cis-acting eQTL by
quantitativePCR,which would seemto allay concernsabout
SNP variation in the probe. Only a single gene was
confirmed, therefore, no general conclusion can be drawn
from this result. Monks et al. discuss the potential problem
of SNP variation with the probe sequence and subsequently
question their own results for the human leukocyte antigen
(HLA)area,which harbours substantial sequence variation.
TRENDS in Genetics
Power of eQTL studies in human pedigrees
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
QTL heritability
P o w e r t o
d e
t e c t e Q T L
Sib-pair
VCA (Morley et al.)
VCA (Monks et al.)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Figure 1. The statistical power to detect the eQTL of given heritability for the two studies using either a sib-pair analysis or a variance component analysis (VCA). Using sib-
pair analyses (red), both studies had similar power; therefore, only a single line is shown. The statistical power is defined as the proportion of analyses in which a QTL with a
given effect willbe detectedunder a defined P value(in this case P !0.0001,which is still lessstringent thanthe proposedgenome-widethreshold [17]) The powerfor thesib-
pair analyses was assessed using the genetic power calculator [20] (http://statgen.iop.kcl.ac.uk/gpc/ ). The power for the VCA (pink and blue) was assessedusing routines thatwere kindly provided by Xijiang Yu (University of Edinburgh) based on Williams and Blangero [21], using the CEPH pedigrees. For all power calculations, the background
heritability was assumed to be 0.30. To restrict the pedigree from the original 210 members to the 167 that were used by Monks et al., 43 individuals were randomly deleted
from the power calculations. For a brief explanation of QTL methods, see Box 2.
Box 2. QTL methods used in the eQTL analyses of human data
Sib-pair analysisMorley et al. [14] applied a sib-pair analysis using the SIBPAL
procedure from S.A.G.E (http://darwin.cwru.edu/sage/index.php). A
sib-pair analysis determines evidence for linkage between a marker
and a quantitative trait by regressing the phenotypic difference
between sibs on the proportion of alleles that are shared identical by
descent (IBD) between the sibs.
Variance component QTL analysisMonks et al. [13] applied a variance component QTL analysis using
SOLAR (http://www.sfbr.org/solar/ ) [18]. In a variance component QTL
analysis, the proportion of phenotypic variation attributable to a QTL
is estimatedacross a population using theIBD proportions between all
related individuals for a putative QTL location.
Quantitative transmission disequilibrium test (QTDT)Morley etal. [13] used a family-based association test to confirm some
of the cis -acting eQTL. Transmission disequilibrium tests (TDT) were
initially proposed for studying mendelian disorders and provide a
combined test for linkage and association by comparing the
transmitted and non-transmitted marker alleles from the parents
with those of the affected offspring.The quantitative TDT (QTDT), used
by Morley et al., extended this methodology to complex traits where
direct classification of offspring is not possible [19].
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Concluding remarks
Both articles present an interesting set of results but only
appear to share a limited theoretical power to detect eQTL
of small to moderate sizes. A first step to compare both
studies would be to analyse the experiment in the first
study with the methods that were applied in the second
study (i.e. re-analyse the data from Morley et al. with
SOLAR and the data from Monks et al. with a sib-pair
analysis). Given that the pedigree details, genotype andgene-expression data for both studies are available online
(Table 1), ongoing exploration of these data sets is
expected to shed further light on the differences and
simalarities between the two studies.
eQTL studies have been successfully linked to variation
in disease phenotype in mice [3] and rats [6]. Although the
current examples of eQTL mapping in humans lack this
important aspect (and motivation) of eQTL mapping, these
authors might have paved the way for future eQTL studies
that will address the complex nature of human disease.
Acknowledgements
We acknowledge financial support from the BBSRC. We are grateful to thetwo referees, and to John Gibson, Ritsert Jansen and Rob Williams for
constructive comments on anearlierdraft ofthis article.We alsothank Ritsert
Jansen and Rob Williams for sharing their manuscripts on BXD data.
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Genome Analysis
A highly unexpected strong correlation betweenfixation probability of nonsynonymous mutations andmutation rate
Gerald J. Wyckoff1,4,*, Christine M. Malcom1,2,*, Eric J. Vallender1,3,* and
Bruce T. Lahn
1
1Howard Hughes Medical Institute, Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA2Department of Anthropology, University of Chicago, Chicago, IL 60637, USA3Committee on Genetics, University of Chicago, Chicago, IL 60637, USA4Department of Molecular Biology and Biochemistry, University of Missouri-Kansas City, Kansas City, MO 64108, USA
Under prevailing theories, the nonsynonymous-to-
synonymous substitution ratio (i.e. K a /K s ), which
measures the fixation probability of nonsynonymous
mutations, is correlated with the strength of selection.
In this article, we report that K a /K s is also strongly
correlated with the mutation rate as measured by K s ,
and that this correlation appears to have a similar
magnitude as the correlation between K a
/K s
and
selective strength. This finding cannot be reconciled
Corresponding author: Lahn, B.T. ([email protected]).
* These authors contributed equally to this work.
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