DOI: 10.1534/genetics.104.031666

**Quantifying the Relationship Between Gene Expressions and**

**Trait Values in General Pedigrees**

**Yan Lu,* Peng-Yuan Liu,* Yong-Jun Liu,* Fu-Hua Xu* and Hong-Wen Deng***

**,†,‡,1**

*Osteoporosis Research Center, Creighton University, Omaha, Nebraska 68131,†_{Key Laboratory of Biomedical Information Engineering of}*Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi’an Jiaotong University,*

*Xi’an 710049, People’s Republic of China and*‡_{Laboratory of Molecular and Statistical Genetics, College of Life Sciences,}*Hunan Normal University, Changsha, Hunan 410081, People’s Republic of China*

Manuscript received May 25, 2004 Accepted for publication September 13, 2004

ABSTRACT

Treating mRNA transcript abundances as quantitative traits and examining their relationships with
clinical traits have been pursued by using an analytical approach of quantitative genetics. Recently, Kraft
*et al. presented a family expression association test (FEXAT) for correlation between gene expressions and*
trait values with a family-based (sibships) design. This statistic did not account for biological relationships of
related subjects, which may inflate type I error rate and/or decrease power of statistical tests. In this
article, we propose two new test statistics based on a variance-components approach for analyses of
microarray data obtained from general pedigrees. Our methods accommodate covariance between relatives
for unmeasured genetic effects and directly model covariates of clinical importance. The efficacy and
validity of our methods are investigated by using simulated data under different sample sizes, family sizes,
and family structures. The proposed LR method has correct type I error rate with moderate to large
sample sizes regardless of family structure and family sizes. It has higher power with complex pedigrees
and similar power to the FEXAT with sibships. The other proposed FEXAT(R) method is favorable with
large family sizes, regardless of sample sizes and family structure. Our methods, robust to population
stratification, are complementary to the FEXAT in expression-trait association studies.

## I

N the past few years, there has been increasing inter- proach to mapping the determinants of variation inest in genetic studies of complex diseases by combin- gene expression. Their results suggested that the

expres-ing information on clinical traits, marker genotypes, sion of most genes is affected by more than one locus

and comprehensive gene expressions. It was proposed (Brem *et al.*2002). Most complex human phenotypes

that standard methods of quantitative genetics can be are also influenced by multiple genes (Chakravarti

applied to microarray data analyses (Wolfinger*et al.* 1999). In developing analytical tools, it is important to

2001;Kraft*et al.*2003). Treating mRNA transcript lev- consider genetic structure of/between gene expressions

els as quantitative traits, efforts have been pursued in or of/between gene expression and a complex trait.

examining their relationships with clinical traits and _{Replication is important in microarray experiments}

mapping gene expression quantitative trait loci (eQTL) _{(}_{Kerr}_{and}_{Churchill}_{2001;}_{Nguyen}_{et al.}_{2002;}_{Yang}

for these traits by using an analytical approach of quanti- _{and}_{Speed}_{2002). Technical replication, such as spotting}

tative genetics (Kraft *et al.*2003;Schadt*et al.*2003). _{genes multiple times per array and hybridizing multiple}

Gene-expression levels are represented in populations _{arrays to the same samples, can address only the }

mea-by continuous variation. The studies in model organisms _{surement error of an experiment.} _{Bakay}_{et al.}_{(2002)}

illustrated the genome-wide view of gene-expression lev- _{found that major unwanted variability in expression }

pro-els as heritable phenotypes (Cheung and Spielman _{filing experiments was from a substantial interindividual}

2002). Li*et al.*compared transcript levels in lymphoblas- _{variability. Such variability can be addressed by using}

toid cell lines from twins by cDNA microarray and RT- _{multiple individuals randomly sampled from a }

popula-PCR to study the heritability of gene expression in hu- _{tion. However, as in genetic epidemiology studies, }

ran-mans. The distribution of the heritability among all _{dom sampling of individuals is liable to population }

strat-genes showed a moderate and homogeneous positive _{ification that may not only inflate type I error but also}

shift that affects the majority of genes (Li*et al.*2003).

mask real genetic effects (Deng2001).

Brem *et al.* used a genome-wide genetic linkage

ap-Families generally contain more information about
inherited traits than do random unrelated individuals,
and family-based studies usually rely on variation and
1_{Corresponding author:}_{Osteoporosis Research Center, Creighton}

covariation among relatives. RecentlyKraft*et al.*(2003)

University, 601 N. 30th St., Suite 6787, Omaha, NE 68131.

E-mail: deng@creighton.edu made pioneering efforts to adopt a family-based design

**FEXAT(R) statistic:**To account for complete family

struc-in microarray studies to mstruc-inimize systematic biases such

ture—the relationships between all subjects within a

pedi-as those from stratification. Stratification wpedi-as recognized

gree—we propose here a statistic FEXAT(R), a revised version

as a confounding factor contributing to spurious associa- _{of the FEXAT. Denote}_{X}

*i* ⫽(x*i*1, . . . ,*x _{i n i}*)Tand

**Y**

*i*⫽ (y

*i*1,

tion between transcript abundance and disease status _{. . . ,} _{y}

*i n i*)Tas gene-expression levels and trait values within
family*i*(with*ni*measured subjects), respectively. They can be

in the sample (Gibson2003;Kraft*et al.*2003).Kraft

expressed, respectively, as

*et al.* (2003) presented a stratified family expression

association test (FEXAT) to examine the correlation _{X}

**i**⫽**WXi****X**⫹**UXiFXi**⫹**zXi**⫹**eXi** (2)

between gene expressions and traits, which was claimed

and

to account for family structure. The FEXAT has a

smaller false-discovery rate than the standard Pearson’s _{Y}_{i}_{⫽}_{W}_{Y}

**i****Y**⫹**UYiFYi**⫹**zYi**⫹**eYi**, (3)

correlation coefficient test when within-family

correla-where**X**and**Y**, respectively, are the vectors of fixed effects of
tion is of interest. However, it considers only sibship

gene-expression levels and trait values, which may incorporate

means and does not account for biological relationships

effects of any observable covariates (e.g., sex and age) as well

between subjects within families. In addition, for large _{as overall means of gene-expression levels and trait values.}_{W}

**Xi**

and complex pedigrees, the FEXAT uses only data from and**W _{Yi}**are the incidence matrices of

**X**and

**Y**, respectively.

**F _{Xi}** and

**F**, respectively, are the family-mean differences of

_{Yi}sibships extracted from large and complex pedigrees

gene-expression levels and trait values, which may be due to

and does not utilize the information from all the

pedi-confounding factors such as population stratification (Kraft

gree members fully and simultaneously.

*et al.*2003);**FXi**ⵑ*N(0,*
2

*FX _{i}*

**J**

*i*) and

**F**

*Yi*ⵑ

*N(0,* 2

*FY _{i}*

**J**

*i*), where

**J**

*i*

Factors such as age, genotype, sex, and habitual physi- _{is a matrix with all elements being 1.} _{U}

**Xi** and **UYi** are the

cal activity are important sources of variation in microar- incidence vectors of**F _{X i}**and

**F**with elements of 0 or 1,

_{Yi}respec-tively.**z _{Xi}**and

**z**are the vectors of additive genetic effects of

_{Yi}ray experiments (Jin*et al.*2001;Nogalskaand

Swierc-gene-expression levels and trait values, respectively;**z _{Xi}**ⵑ

*N(0,*zynski 2001; Roth

*et al.*2002; Yang

*et al.*2003) and

2

*ZXi***G***i*) and **zYi**ⵑ*N(0,*
2

*ZYi***G***i*), where **G***i* is the relationship

for complex traits (Deng*et al.*2002). These factors may _{matrix for the} _{n}

*i* observed individuals within family *i. The*

affect statistical analyses if they are not accounted for. _{elements of}_{G}

*i*are twice the coefficients of kinship.**e _{Xi}**and

Hence, covariate effects should be considered simulta- **e _{Yi}**are the vectors of residual effects of gene-expression levels

and trait values within family *i, respectively;* **e _{Xi}**ⵑ

*N(0,*2

*eXi***I**)

neously and explicitly for gene expression and

quantita-tive trait analyses, which is done in the FEXAT (Kraft and**e _{Yi}**ⵑ

*N(0,*2

*eYi***I**), where**I**is an*ni*⫻*ni*identity matrix. The

*et al.*2003) and new analyses that are being developed. variances of *Xi* and *Yi* are thus **V***Xi*⫽

2

*FXi***J***i*⫹
2

*ZXi* **G***i*⫹
2

*eXi***I**

Stimulated largely by the pioneering work ofKraft*et* and**V***Yi*⫽

2

*FYi***J***i*⫹
2

*ZYi***G***i*⫹
2

*eYi***I**, respectively. The FEXAT(R)

*al.*(2003), to account for family structure and potential can be expressed as

covariate effects, we propose a modified FEXAT statistic _{FEXAT(R)}

and a bivariate analysis based on a variance-components

approach to quantify the correlation between gene-expres- ⫽ {兺*i*[(**X***i*⫺**WX _{i}**

**X**)T

_{i}**V**⫺

**X**

*1/2*

**i****V**

⫺1/2

**Y*** i* (

**Y**

*i*⫺

**WYi**

**Yi**)]}

2

兺*i*(1/(*ni*⫺1))[兺*i*(**X***i*⫺**WX _{i}**

**X**)T

_{i}**VX**⫺

*1(*

**i****X**

*i*⫺

**WXi**

**Xi**)兺

*i*(

**Y**

*i*⫺

**WYi**

**Yi**) T

**⫺1**

_{V}**Y*** i*(

**Yi**⫺

**WYi**

**Yi**)]

.

sion levels and complex clinical traits. The properties

(4)

of these two methods in terms of power and type I error

Similar to the FEXAT, in practical data analyses,*P-values for*

rate are explored in a range of situations, in comparison

this statistic can be obtained by2

1test or permutation test.

with the FEXAT.

**Likelihood-ratio statistic:**We present a new statistic
[likeli-hood ratio (LR)] by conducting analyses in a bivariate
variance-components framework (Lange and Boehnke 1983; Lange

MATERIALS AND METHODS

1997). Define**X** ⫽ (**X1**, . . . , **Xi**, . . . , **Xn**)T and**Y** ⫽ (**Y1**,

. . . ,**Yi**, . . . ,**Yn**)Tas gene-expression levels and trait values from

**FEXAT statistic:**As a starting point, we briefly introduce

the whole sample where**Xi**and**Yi**were defined in Equations 2

the statistic FEXAT proposed byKraft*et al.*(2003),

and 3.**X**and**Y**can be written in a matrix form as
FEXAT⫽ [兺*i*兺*jxi j*(y*i j*⫺*yi*.)]

2

兺*i*(1/n*i*⫺1)兺*i*(x*i j*⫺*xi*.)2兺*j*(y*i j*⫺*yi*.)2

, _{(1)}

### 冢

**X**

**Y**

### 冣

⫽### 冢

**WX****X**

**WY****Y**

### 冣

⫹

### 冢

**UXFX**

**UYFY**

### 冣

⫹

### 冢

**zX**

**zY**

### 冣

⫹

### 冢

**eX**

**eY**

### 冣

, (5)

where*i*⫽1, . . . ,*n*index the sibships in the study (extended

pedigrees may contribute multiple sibships) and*j*⫽1, . . . , where **WX**⫽(**WX1**, . . . ,**WXi**, . . . ,**WXn**)
T_{,}_{W}

**Y**⫽(**WY1**, . . . ,**WYi**,
*ni*index the subjects in a sibship.*xi j*is the expression level for _{. . . ,} _{W}_{Y}

**n**)
T_{,} _{F}

**X**⫽(**FX1**, . . . , **FXi**, . . . , **FXn**)
T_{,} _{F}

**Y**⫽(**FY1**,

subject*j*in family*i, which is measured as the (log of the) fold* _{. . . ,}_{F}

**Y1**, . . . ,**FYn**)
T_{,}
change of the expression for the gene under study in the

subject’s RNA relative to a reference sample as in cDNA arrays
or as match-mismatch score as in oligonucleotide arrays. *yi j*
is the trait value. This FEXAT statistic is compared with its
asymptotic2

1distribution or an empirical permutation
distri-bution to derive*P-values in practical data analyses (*Kraft*et*
*al.*2003). It accounts only for sibship means for gene
expres-sion levels and trait values and does not consider family
struc-ture—the biological relationship between subjects within a
family. If the family structures are pedigrees, some subjects
will need to be excluded to calculate the statistic, since it

.

Figure1.—Two types of pedigree structures in simulations.

**X**,**Y**,**WXi**,**WYi**,**FXi**, and**FYi**were defined for Equations 2 and
3. Thus, the variance-covariance matrix is

article, the first number is for families and the second number for subjects within each family); and the second sample

**con-V**⫽**J**丢

### 冤

2

*fx* *fxy*

*fxy* 2*fy*

### 冥

⫹**G**丢

### 冤

2

*ax* *axy*

*axy* 2*ay*

### 冥

⫹**I**丢

### 冤

2

*ex* *exy*

*exy* 2*ey*

### 冥

, sisted of 8 families of size 12 (8⫻12); and the third sample consisted of 16 families of size 12 (16⫻ 12). To study the (6)effects of family structures on these three methods, we simu-where

lated three family structures: two of them were pedigrees and another was sibship. Sibship structure is a group of full-sibs without parents. The pedigree structures are illustrated in Figure 1. According to the definition of the FEXAT (Kraft

*et al.*2003), a large sibship of size 8 in each pedigree was used
to calculate the statistic FEXAT in pedigree type A, whereas
in pedigree type B, the complex pedigree should be broken
.

into three sibships to calculate the statistic FEXAT. To study the effects of family sizes on these three statistical methods,

**Ji**and**Gi**were defined for Equations 2 and 3.**I**is an identity

we simulated another sample with 24 sibships of size 4 (24⫻ matrix.2

*fx*and2*fy*denote the family mean variances of the

4) to compare it with the situations of 8 sibships of size 12 (8⫻ studied gene-expression levels and trait values in the study

12) under the same sample size. For simplicity, we considered a
population, respectively.*fxy*denotes the corresponding

covari-case in the simulation studies ofKraft*et al.*(2003);*i.e.,*the
ance between them.2

*ax*and2*ay*denote the variances of

addi-ratio of variance in family means to variance in within-family tive effects of the studied gene-expression levels and trait values

differences is 1:1. Our simulation results (not shown)
indi-in the population, respectively.*axy*denotes the corresponding

cated that this ratio has little effect on the comparison of the covariance between them.2

*ex*and2*ey*denote the variances of

three methods. Total variation of gene-expression levels and residual effects of the studied gene-expression levels and trait

trait values were fixed at 1. We studied four levels of genetic
values in the population, respectively.*exy*denotes the

corre-correlation between gene-expression levels and trait values sponding covariance between them. The genetic correlation

(0.0, 0.3, 0.5, and 0.7) and three heritability levels for both between gene-expression levels and trait values can be

calcu-the gene-expression levels and trait values (0.2, 0.4, and 0.6),
lated as*axy*⫽ *axy*/√2*ax*2*ay*.

respectively. In addition, we considered two situations about Under the assumption of the multivariate normality of

gene-covariates: (1) there were no covariate effects;*i.e.,**cX*⫽ *cY*⫽
expression levels and trait values, the ln-likelihood of**V**,**X**,

0, where*cX*and*cY*are the regression coefficients of the
gene-and**Y**given the observed data (**X**,**Y**,**WX**,**WY**) is

expression levels and trait values on a specific covariate *c,*
respectively; and (2) there were covariate effects on the

gene-ln*L*(**V**,**X**,**Y**|**X**,**Y**,**WX**,**WY**)⫽ ⫺

*t*

2ln(2)⫺

1

2|**V**|⫺ ⌬

T** _{V}**⫺1⌬

_{,}

expression levels and trait values. We assume that*cX*⫽ *cY*⫽
0.2 and covariate effects were drawn from a standardized
nor-(7) _{mal distribution. When there were covariate effects on the}

where gene-expression levels and trait values, the computation of

the FEXAT statistic used the adjusted (for the covariate)
gene-expression levels and trait values that were implemented in
*t*⫽

### 兺

*n*

*i*

*ni* and ⌬ ⫽

### 冢

**X**⫺**WX****X**

**Y**⫺**WY****Y**

### 冣

.

the standard regression analyses. We also assumed that the family-mean correlation between gene-expression levels and Maximum-likelihood estimates can be obtained via a

Fisher-trait values is 0.3, which may be due to confounding factors scoring algorithm implemented in each sibship and/or

pedi-such as population stratification (Kraft*et al.*2003). Although
gree (LangeandBoehnke1983). Once maximum-likelihood

our simulation results are presented for the above assumed estimates are available, one can test hypotheses of interest by

parameter values, our results for other parameter values (not the LR statistic,

shown here) indicated similar conclusions obtained with those presented here.

LR⫽ ⫺2 ln*L(**axy*⫽0)
*L(*ˆ*axy*)

. (8) _{For each scenario, we generated 10,000 replicate studies}
and calculated the FEXAT, FEXAT(R), and LR test statistics
for two nominal significance levels of␣ ⫽0.05 and␣⫽0.01,
This test statistic is approximately chi-square distributed with

1 d.f. We can test the null hypothesis *axy* ⫽ 0. If*axy* ⫽ 0 respectively. The statistical power and type I error rate are
used as criteria to compare the relative performance of these
is accepted, one can conclude that there is no significant

relationship between the gene-expression level **X** and trait three statistics in quantifying the correlation between
gene-expression levels and trait values. Power refers to the
probabil-value**Y**.

**Simulation study:**A series of simulations were carried out ity of declaring a statistical significance when a true correlation
exists. Type I error rate is the probability of declaring that
to examine the effects of different factors on the performance

of these two methods, in comparison with the FEXAT. To gene-expression levels are correlated with trait values when
there is no relationship between them, *i.e.,* *axy* ⫽ 0. More
investigate the effects of sample sizes, we simulated three

sam-ples of 48, 96, and 192 subjects, respectively. The first sample detailed descriptions of these test statistics examined are pro-vided inmaterials and methods.

RESULTS _{and pedigree B, respectively. The type I error rate of}

the FEXAT is 10.4% with a sample size of 16 ⫻ 12

The type I error rate and power estimates over 10,000

and heritabilities of 0.4 under the family structure of replicated simulations are summarized in Tables 1–3.

pedigree B. In contrast, the type I error rates of the LR All three methods are not sensitive to the correlation

and FEXAT(R) are reasonably robust to family struc-between the family-mean expression levels and trait

val-tures and their statistical powers vary to a lesser extent ues. That is, they are robust to population stratification

than that of the FEXAT. Taking into account both type (that can generate correlation between family-mean

ex-I error rate and statistical power, we present a summary

pression levels and trait values; Kraft *et al.* 2003) in

of the relative performance of the three methods under association analyses between gene-expression levels and

different situations in terms of sample sizes and family trait values. The estimated type I errors of the FEXAT(R)

structures in Table 4. Table 4 also provides a recommen-are slightly conservative and those of the LR recommen-are slightly

dation for choosing different statistics in practical data inflated. The type I errors of the FEXAT vary depending

analysis. This generalization is made under a prerequi-upon family structures (see below).

site of a large family size (e.g.,⬎12). If the family size

The heritabilities of the gene-expression levels and

is small, the FEXAT(R) is overconservative along with trait values have no significant effect on type I errors

having lower powers (Table 5). of the three methods. As expected, the statistical powers

Table 6 illustrates the results when a covariate had increase with increasing heritabilities for all three

meth-effects on both the gene-expression levels and trait val-ods. The LR statistic has higher power than the other

ues under study. Because both the LR and FEXAT(R) two methods in most cases.

accommodate covariate effects in the mean value struc-Comparisons of results in Tables 1–3 show that type

ture of the models, they can correctly estimate the re-I errors of the LR approach nominal levels with

increas-gression coefficient of the gene-expression levels and ing sample sizes. For example, when the sample size of

trait values on covariates. A comparison of Tables 2 and

sibships increases from 4⫻12 (4 sibships each with 12

6 shows that both the LR and FEXAT(R) yield nearly

sibs) to 16 ⫻ 12 (16 sibships each with 12 sibs) and

identical type I error rates and powers whether or not

heritability *h*2⫽ _{0.4, the estimated type I errors of the}

there are covariate effects. The statistic FEXAT *per se*

LR decrease from 12.7 to 5.3% and from 5.7 to 0.9%

cannot account for covariate effects. When adjusting

under the nominal significance levels of␣ ⫽0.05 and

gene-expression levels and trait values that can be

imple-␣ ⫽0.01, respectively. In contrast, the reverse tendency

mented in the standard regression analyses, the FEXAT

is observed for the FEXAT. When the sample size in- _{can also obtain similar powers to those when covariate}

creases, the estimated type I errors of the FEXAT have _{effects are absent, however, with slightly inflated type I}

a slight inflation especially when␣ ⫽0.01. As expected, _{error rates (Tables 2 and 6). The above results are }

ob-the statistical powers increase as ob-the sample size in- _{tained from samples with large family sizes (Table 6).}

creases for all of the three methods. _{If the family size is small, with the same total sample}

Pedigree structure affects type I error rates and pow- _{size, the FEXAT has relatively larger inflated type I }

er-ers (Tables 1–3). The FEXAT performs relatively unsat- _{rors (Table 5). In comparison, the LR and FEXAT(R)}

isfactorily in multigeneration complex pedigree data. _{are reasonably robust to family size regardless of the}

Compared with the results from the same sample size _{presence of covariate effects (Tables 2, 5, and 6).}

and family size but with different family structures, the estimated powers of the FEXAT are much lower in

com-DISCUSSION

plex pedigrees than in sibships, and the difference is

more pronounced with large sample sizes. In addition, _{In microarray experiments, measures of }

gene-expres-the type I errors of gene-expres-the FEXAT depart far above gene-expres-the _{sion levels are available for members of a set of families}

nominal levels for those pedigrees with small sibships. _{(}_{Shannon}_{et al.}_{2002;}_{Schadt}_{et al.}_{2003). Thus, }

stan-This is largely because (1) the FEXAT uses only the _{dard methods of quantitative genetics can be applied}

data of sibships by breaking a large pedigree into multi- _{to the family-based microarray data analyses. This may}

ple sibs and may lose information, while the FEXAT(R) _{facilitate exploration of the association of gene }

expres-and LR use full data in pedigrees; expres-and (2) the FEXAT sions with multifactorial phenotypes of interest.

Family-does not consider the biological relationship (covari- based designs have the advantages of being robust to

ance due to polygene effects), while the FEXAT(R) and population stratification. Recently, Kraft*et al.*(2003)

LR methods accounted for the biological relationships proposed a stratified FEXAT to quantify the relationship

of family members. For example, with a sample size of between gene-expression levels and clinical traits.

Stimu-16⫻12 (16 pedigrees each with 12 members), heritabil- lated by their work, we presented a modified FEXAT

ities of 0.4 (for both gene expression levels and trait statistic and a bivariate analysis based on a

variance-values), and an expression-trait correlation of 0.7, the components approach for such experimental data

anal-estimated powers of the FEXAT are 89.7, 47.6, and yses.

**TABLE 1**

**Type I error rates and powers under different heritabilities (for both the gene expression levels**
**and the trait values) with a sample size of 4**⫻**12**

FEXAT FEXAT(R) LR

*axy* ␣ ⫽0.05 ␣ ⫽0.01 ␣ ⫽0.05 ␣ ⫽0.01 ␣ ⫽0.05 ␣ ⫽0.01

Sibship
*h*2_{⫽}_{0.2}

0.0 0.050 0.010 0.044 0.006 0.124 0.054

0.3 0.056 0.010 0.050 0.008 0.134 0.060

0.5 0.065 0.012 0.063 0.011 0.146 0.069

0.7 0.079 0.017 0.076 0.015 0.163 0.079

*h*2⫽_{0.4}

0.0 0.051 0.009 0.042 0.006 0.127 0.057

0.3 0.076 0.017 0.074 0.014 0.154 0.070

0.5 0.125 0.035 0.125 0.031 0.212 0.101

0.7 0.205 0.064 0.200 0.059 0.300 0.155

*h*2⫽_{0.6}

0.0 0.050 0.008 0.042 0.006 0.117 0.049

0.3 0.134 0.036 0.126 0.032 0.210 0.100

0.5 0.292 0.107 0.274 0.095 0.375 0.199

0.7 0.528 0.257 0.491 0.227 0.581 0.376

Pedigree A
*h*2_{⫽}_{0.2}

0.0 0.050 0.008 0.045 0.007 0.089 0.033

0.3 0.052 0.010 0.050 0.011 0.100 0.039

0.5 0.057 0.011 0.063 0.013 0.110 0.044

0.7 0.066 0.012 0.083 0.018 0.135 0.052

*h*2⫽_{0.4}

0.0 0.052 0.008 0.041 0.008 0.092 0.034

0.3 0.066 0.012 0.078 0.017 0.130 0.056

0.5 0.095 0.022 0.127 0.029 0.204 0.094

0.7 0.139 0.036 0.226 0.070 0.322 0.157

*h*2_{⫽}_{0.6}

0.0 0.051 0.008 0.038 0.006 0.086 0.033

0.3 0.100 0.023 0.140 0.037 0.205 0.084

0.5 0.194 0.055 0.284 0.102 0.382 0.200

0.7 0.347 0.123 0.508 0.240 0.619 0.405

Pedigree B
*h*2_{⫽}_{0.2}

0.0 0.099 0.026 0.040 0.006 0.085 0.030

0.3 0.102 0.027 0.056 0.012 0.101 0.038

0.5 0.110 0.031 0.076 0.015 0.123 0.046

0.7 0.120 0.034 0.092 0.022 0.157 0.062

*h*2⫽_{0.4}

0.0 0.100 0.026 0.046 0.008 0.104 0.037

0.3 0.118 0.033 0.087 0.020 0.149 0.061

0.5 0.149 0.046 0.146 0.039 0.240 0.107

0.7 0.191 0.066 0.255 0.083 0.390 0.206

*h*2_{⫽}_{0.6}

0.0 0.098 0.025 0.044 0.008 0.099 0.037

0.3 0.150 0.048 0.131 0.034 0.215 0.089

0.5 0.243 0.093 0.312 0.110 0.435 0.242

0.7 0.375 0.169 0.559 0.273 0.706 0.494

In Tables 1–3 and 5–7, when*axy*⫽0.0, which indicates no correlation between expression levels and trait
values, the corresponding data are for type I error rates. When *axy* ⬆ 0.0, the corresponding data are for
statistical powers.

I errors for the FEXAT were somewhat conservative to have slightly inflated type I errors, especially under

a large sample size. As indicated inKraft*et al.*(2003),

especially at stringent nominal levels in their

**TABLE 2**

**Type I error rates and powers under different heritabilities with a sample size of 8**⫻**12**

FEXAT FEXAT(R) LR

*axy* ␣ ⫽0.05 ␣ ⫽0.01 ␣ ⫽0.05 ␣ ⫽0.01 ␣ ⫽0.05 ␣ ⫽0.01

Sibship
*h*2⫽_{0.2}

0.0 0.052 0.008 0.042 0.006 0.077 0.023

0.3 0.062 0.011 0.063 0.011 0.089 0.028

0.5 0.081 0.019 0.088 0.019 0.111 0.036

0.7 0.110 0.031 0.119 0.032 0.144 0.053

*h*2_{⫽}_{0.4}

0.0 0.051 0.009 0.041 0.006 0.074 0.021

0.3 0.109 0.029 0.111 0.027 0.138 0.048

0.5 0.213 0.075 0.220 0.075 0.249 0.109

0.7 0.381 0.169 0.390 0.163 0.424 0.216

*h*2⫽_{0.6}

0.0 0.050 0.009 0.041 0.025 0.067 0.020

0.3 0.223 0.081 0.219 0.076 0.252 0.110

0.5 0.537 0.281 0.527 0.261 0.572 0.330

0.7 0.841 0.627 0.824 0.593 0.848 0.669

Pedigree A
*h*2⫽_{0.2}

0.0 0.048 0.007 0.040 0.008 0.066 0.017

0.3 0.055 0.009 0.064 0.014 0.079 0.022

0.5 0.066 0.012 0.097 0.025 0.111 0.035

0.7 0.085 0.018 0.135 0.034 0.159 0.056

*h*2_{⫽}_{0.4}

0.0 0.049 0.009 0.045 0.008 0.070 0.019

0.3 0.082 0.018 0.116 0.028 0.138 0.047

0.5 0.148 0.043 0.238 0.076 0.288 0.127

0.7 0.253 0.087 0.409 0.177 0.500 0.275

*h*2⫽_{0.6}

0.0 0.049 0.008 0.042 0.007 0.065 0.017

0.3 0.155 0.046 0.235 0.078 0.271 0.112

0.5 0.365 0.147 0.556 0.288 0.607 0.372

0.7 0.639 0.368 0.839 0.613 0.885 0.732

Pedigree B
*h*2⫽_{0.2}

0.0 0.100 0.026 0.043 0.008 0.066 0.016

0.3 0.105 0.031 0.066 0.012 0.087 0.026

0.5 0.119 0.037 0.100 0.024 0.137 0.044

0.7 0.135 0.046 0.155 0.042 0.213 0.079

*h*2_{⫽}_{0.4}

0.0 0.100 0.026 0.046 0.007 0.066 0.018

0.3 0.134 0.045 0.130 0.035 0.165 0.057

0.5 0.197 0.075 0.270 0.099 0.359 0.171

0.7 0.293 0.129 0.479 0.223 0.612 0.372

*h*2⫽_{0.6}

0.0 0.101 0.026 0.043 0.008 0.061 0.016

0.3 0.202 0.080 0.245 0.080 0.294 0.125

0.5 0.384 0.191 0.573 0.306 0.660 0.425

0.7 0.619 0.381 0.874 0.665 0.932 0.812

than for small family sizes (seeKraft*et al.*2003, Tables families when simulating phenotypic data. In the study

ofKraft*et al.*(2003), data for family members (siblings)
1 and 2), but the reverse trend was observed in our

study (see Tables 2 and 4). This is presumably due to appeared to be drawn independently from a normal

distribution. Apparently, the former design is closer to different simulation designs between the two studies.

In this study, we took into account the biological rela- the nature of quantitative-trait inheritance (Lynchand

Walsh1998). It seems that the FEXAT is favored under tionship (covariances due to polygenes or shared

**relation-TABLE 3**

**Type I error rates and powers under different heritabilities with a sample size of 16**⫻**12**

FEXAT FEXAT(R) LR

*axy* ␣ ⫽0.05 ␣ ⫽0.01 ␣ ⫽0.05 ␣ ⫽0.01 ␣ ⫽0.05 ␣ ⫽0.01

Sibship
*h*2⫽_{0.2}

0.0 0.051 0.009 0.046 0.009 0.049 0.008

0.3 0.096 0.026 0.125 0.033 0.093 0.025

0.5 0.189 0.064 0.228 0.080 0.184 0.063

0.7 0.322 0.137 0.372 0.161 0.315 0.135

*h*2_{⫽}_{0.4}

0.0 0.054 0.010 0.046 0.008 0.053 0.009

0.3 0.273 0.108 0.306 0.125 0.269 0.108

0.5 0.618 0.366 0.655 0.390 0.613 0.366

0.7 0.897 0.733 0.909 0.746 0.896 0.734

*h*2⫽_{0.6}

0.0 0.055 0.010 0.048 0.008 0.055 0.010

0.3 0.571 0.330 0.600 0.340 0.569 0.332

0.5 0.954 0.857 0.957 0.855 0.953 0.857

0.7 1.000 0.997 1.000 0.996 1.000 0.997

Pedigree A
*h*2⫽_{0.2}

0.0 0.050 0.010 0.043 0.008 0.054 0.013

0.3 0.065 0.013 0.088 0.022 0.084 0.024

0.5 0.091 0.022 0.142 0.041 0.151 0.049

0.7 0.134 0.038 0.241 0.087 0.257 0.102

*h*2_{⫽}_{0.4}

0.0 0.051 0.010 0.044 0.009 0.057 0.013

0.3 0.128 0.037 0.201 0.068 0.205 0.072

0.5 0.275 0.103 0.446 0.209 0.484 0.257

0.7 0.476 0.240 0.724 0.473 0.777 0.560

*h*2⫽_{0.6}

0.0 0.052 0.010 0.040 0.008 0.057 0.012

0.3 0.283 0.113 0.434 0.203 0.440 0.219

0.5 0.644 0.392 0.853 0.648 0.866 0.694

0.7 0.917 0.767 0.992 0.949 0.996 0.972

Pedigree B
*h*2⫽_{0.2}

0.0 0.106 0.030 0.044 0.007 0.055 0.012

0.3 0.119 0.037 0.095 0.026 0.107 0.029

0.5 0.144 0.052 0.174 0.052 0.206 0.075

0.7 0.185 0.071 0.287 0.106 0.355 0.163

*h*2_{⫽}_{0.4}

0.0 0.104 0.030 0.046 0.009 0.057 0.012

0.3 0.141 0.046 0.230 0.078 0.255 0.101

0.5 0.206 0.078 0.505 0.254 0.588 0.349

0.7 0.297 0.134 0.815 0.578 0.884 0.713

*h*2⫽_{0.6}

0.0 0.105 0.030 0.044 0.007 0.054 0.012

0.3 0.210 0.081 0.453 0.218 0.488 0.264

0.5 0.390 0.198 0.871 0.667 0.916 0.776

0.7 0.612 0.386 0.903 0.964 0.999 0.989

ships. Dividing extended pedigrees into multiple sib- yield spurious association results (Kraft *et al.* 2003).

The FEXAT(R) seems to be slightly conservative at the

ships, as suggested byKraft*et al.*(2003), and ignoring

covariances among family members greatly decrease the cost of a decrease in the powers, while the type I error

rates of the LR statistic are slightly higher than nominal statistical powers and inflate type I errors of the FEXAT

as shown in our simulations. levels especially with small sample size. The LR has

higher powers than the FEXAT and FEXAT(R) in most All three methods are robust to population

**TABLE 4** statistics are the reverse for moderate to large sample
sizes and/or pedigrees.

**Summary of the performances of the three methods**

It is known that maximum-likelihood estimates of the

**under different situations**

variance and covariance components may be biased

Sample Family when only a small number of observations are

consid-size structure Favorable method(s) _{ered (}_{Hopper} _{and}_{Mathews} _{1982;}_{Amos}_{et al.}_{1996).}

As the sample size increases, the biases in

maximum-Small Sibship FEXAT and FEXAT(R)

likelihood estimates of variance and covariance

compo-Large Sibship FEXAT(R), FEXAT, and LR

Small Pedigree FEXAT(R) nents should be reduced; thus, the type I error rates of

Large Pedigree LR _{the LR statistic should approach their nominal levels}

(Amos*et al.*1996) as reflected in our results for the LR
method. We examined the empirical distribution of the
LR statistic under the null hypothesis with different
FEXAT(R) has the highest power. Both the LR and

sample sizes. We simulated 10,000 data sets using pedi-FEXAT(R) are favored by increasing sample sizes

re-gree A as described in *Simulation study, and in each*

gardless of the family structures. This is particularly

im-data set the LR statistic was calculated and shown in portant given that the decreasing costs of microarray

histograms (Figure 2). The LR statistic follows the chi-experiments make it practical for gene expression study

square distribution with 1 d.f. asymptotically. However, on a large scale in complex pedigrees. As shown in

this statistic has a slightly heavy tail under small sample

results, the FEXAT is preferable to the FEXAT(R) and

sizes. LR statistics under small sample sizes and for sibships,

while the performances and the preference of the three The methods proposed in this study can also be used

**TABLE 5**

**Type I error rates and powers under different heritabilities with sibships 24**⫻**4**

FEXAT FEXAT(R) LR

*axy* ␣ ⫽0.05 ␣ ⫽0.01 ␣ ⫽0.05 ␣ ⫽0.01 ␣ ⫽0.05 ␣ ⫽0.01

*cX*⫽ *cY*⫽0.0
*h*2_{⫽}_{0.2}

0.0 0.050 0.009 0.023 0.003 0.075 0.020

0.3 0.056 0.012 0.041 0.006 0.088 0.025

0.5 0.073 0.018 0.061 0.011 0.110 0.033

0.7 0.103 0.025 0.085 0.016 0.146 0.049

*h*2⫽_{0.4}

0.0 0.049 0.010 0.021 0.003 0.070 0.019

0.3 0.096 0.024 0.068 0.012 0.127 0.044

0.5 0.191 0.060 0.147 0.033 0.247 0.100

0.7 0.340 0.132 0.277 0.077 0.412 0.211

*h*2_{⫽}_{0.6}

0.0 0.049 0.010 0.021 0.003 0.064 0.016

0.3 0.197 0.064 0.131 0.028 0.238 0.098

0.5 0.477 0.226 0.362 0.119 0.533 0.305

0.7 0.785 0.529 0.677 0.353 0.823 0.630

*cX*⫽ *cY*⫽0.2
*h*2⫽_{0.2}

0.0 0.106 0.033 0.024 0.004 0.076 0.021

0.3 0.116 0.039 0.042 0.006 0.087 0.025

0.5 0.135 0.046 0.062 0.010 0.111 0.034

0.7 0.161 0.057 0.086 0.016 0.146 0.049

*h*2_{⫽}_{0.4}

0.0 0.104 0.031 0.022 0.003 0.071 0.021

0.3 0.155 0.056 0.068 0.012 0.129 0.042

0.5 0.243 0.100 0.147 0.033 0.246 0.100

0.7 0.372 0.179 0.275 0.078 0.410 0.209

*h*2⫽_{0.6}

0.0 0.102 0.031 0.022 0.003 0.067 0.017

0.3 0.250 0.107 0.132 0.028 0.239 0.097

0.5 0.487 0.270 0.363 0.120 0.534 0.304

**TABLE 6**

**Effects of covariates on type I error rates and powers with a sample size of 8**⫻**12**

FEXAT FEXAT(R) LR

*axy* ␣ ⫽0.05 ␣ ⫽0.01 ␣ ⫽0.05 ␣ ⫽0.01 ␣ ⫽0.05 ␣ ⫽0.01

Sibship
*h*2⫽_{0.2}

0.0 0.061 0.013 0.042 0.005 0.078 0.024

0.3 0.074 0.016 0.063 0.012 0.091 0.026

0.5 0.094 0.024 0.088 0.019 0.112 0.035

0.7 0.124 0.037 0.121 0.032 0.145 0.054

*h*2_{⫽}_{0.4}

0.0 0.062 0.013 0.041 0.005 0.073 0.021

0.3 0.119 0.033 0.113 0.028 0.137 0.048

0.5 0.228 0.087 0.220 0.076 0.247 0.110

0.7 0.383 0.183 0.389 0.162 0.424 0.216

*h*2⫽_{0.6}

0.0 0.062 0.012 0.040 0.006 0.068 0.019

0.3 0.237 0.093 0.218 0.077 0.252 0.111

0.5 0.532 0.293 0.529 0.262 0.571 0.331

0.7 0.827 0.621 0.827 0.596 0.846 0.665

Pedigree A
*h*2⫽_{0.2}

0.0 0.053 0.011 0.051 0.009 0.073 0.021

0.3 0.066 0.015 0.059 0.011 0.079 0.023

0.5 0.082 0.019 0.088 0.021 0.109 0.033

0.7 0.105 0.026 0.145 0.039 0.165 0.056

*h*2_{⫽}_{0.4}

0.0 0.054 0.011 0.041 0.007 0.067 0.017

0.3 0.100 0.023 0.131 0.035 0.144 0.050

0.5 0.167 0.048 0.229 0.077 0.284 0.127

0.7 0.271 0.096 0.423 0.188 0.499 0.277

*h*2⫽_{0.6}

0.0 0.056 0.011 0.044 0.007 0.068 0.016

0.3 0.168 0.048 0.217 0.071 0.262 0.107

0.5 0.357 0.153 0.534 0.272 0.601 0.365

0.7 0.602 0.345 0.834 0.603 0.890 0.732

Pedigree B
*h*2⫽_{0.2}

0.0 0.104 0.032 0.044 0.008 0.062 0.015

0.3 0.118 0.039 0.074 0.014 0.089 0.024

0.5 0.135 0.044 0.108 0.026 0.137 0.046

0.7 0.156 0.055 0.165 0.048 0.215 0.080

*h*2_{⫽}_{0.4}

0.0 0.108 0.030 0.048 0.008 0.068 0.018

0.3 0.151 0.052 0.133 0.039 0.168 0.060

0.5 0.213 0.088 0.286 0.103 0.361 0.174

0.7 0.303 0.142 0.484 0.228 0.614 0.380

*h*2⫽_{0.6}

0.0 0.105 0.031 0.043 0.008 0.061 0.015

0.3 0.211 0.087 0.255 0.087 0.298 0.127

0.5 0.382 0.191 0.571 0.308 0.666 0.429

0.7 0.602 0.373 0.872 0.662 0.934 0.817

*cX*⫽ *cY*⫽0.2;*cX*and*cY*denote the regression coefficients of the gene-expression levels and trait values
on a specific covariate*c, respectively.*

to characterize the genetic correlation between multiple expression and/or expression-expression association

studies. An initial data reduction to focus on those genes gene-expression levels. Pairwise analysis of multiple

gene expression profiles may provide a prediction of whose expressions varied nontrivially across samples is

necessary before the tests are performed. Additionally, joint expression and regulation of these genes. Due to

the high-dimensional gene-expression data from micro- systematic measurement errors may contribute to the

**trait-TABLE 7**

**Comparison the performance of FEXAT(R) by chi-square**
**and permutation procedures**

Chi-square test Permutation test

*axy* ␣ ⫽0.05 ␣ ⫽0.01 ␣ ⫽0.05 ␣ ⫽0.01

0.0 0.045 0.008 0.051 0.010

0.3 0.116 0.028 0.139 0.042

0.5 0.238 0.076 0.290 0.125

0.7 0.409 0.177 0.521 0.296

*h*2⫽_{0.4, pedigree A with a sample size of 8}⫻_{12.}

ate normal distribution (Amos*et al.*1996). Permutation

tests can be performed in situations, particularly for small sample sizes, in which the violation of multivariate normality assumption is difficult to detect. Simulations showed that permutation tests warrant correct type I errors under violations of multivariate normality

as-sumption (Abecasis *et al.*2000). The procedure of

re-shuffling the original data is similar to that proposed
byKraft*et al.*(2003). Empirical*P-values can be *
com-puted by comparing the observed statistic with the
per-muted statistics under the null hypothesis of no
trait-expression association. We simulated 10,000 data sets
using pedigree A and each data set was resampled 10,000

times to obtain the empirical*P-value of the FEXAT(R).*

We found that the FEXAT(R) using a permutation test performs slightly better than that using a chi-square test (Table 7). However, the permutation test is computa-tionally demanding especially for gene-expression data. Typical microarray experiments aim at characterizing differential gene-expression patterns under distinct treatments. Quantifying trait-expression and/or expres-sion-expression associations at the population level may represent another growing trend toward high-through-put microarray data analyses. In the latter analysis, a large number of candidate genes for complex diseases can be reduced by restricting attention to genes whose

Figure2.—Histograms of the LR statistic for the data

pro-duced under the null hypothesis. Simulation was performed expression levels show associations with complex trait

under the null hypothesis as described in*Simulation study*using _{values. In the near future, remarkable advances in }
micro-pedigree A. (A) Sample size 4⫻12,*h*2_{⫽}_{0.2, and}_{}

*cX*⫽ *cY*⫽

array technology may greatly decrease the experimental

0.0; (B) sample size 8⫻ 12,*h*2_{⫽} _{0.2, and}_{}

*cX* ⫽ *cY*⫽0.0;

costs, making it practical to perform microarray

experi-and (C) sample size 16 ⫻ 12, *h*2 _{⫽} _{0.2, and} _{}

*cX* ⫽ *cY* ⫽

ments in large sample sizes with large pedigrees. This

0.0. The histograms of the statistic are shown as bars. The

probability density function of the chi-square distribution with _{has been exemplified by the current burgeoning of }

large-1 d.f. is shown as curves. _{scale whole-genome linkage scans for complex traits,}

which were hampered by the prohibitory costs just about a decade ago. It is anticipated that the methods based on the variance-components approach that is advantageous which may lead to spurious association results. In a

under large sample sizes will be a good way to measure flexible variance-components framework, modeling such

quantitatively the relationship between gene expres-covariation as a covariance component in our analyses

sions and clinical traits in general pedigrees. The pro-is straightforward. Thpro-is may minimize bias in analyses.

gram for implementing the methods investigated here Variance-components methods make a critical

assump-is available, upon request, from Y.L. (yanlu@creighton. tion that the quantitative trait values within a family

Kerr, M. K., andG. A. Churchill, 2001 Statistical design and the This study was partially supported by grants from the Health Future

analysis of gene expression microarray data. Genet. Res.**77:**123–
Foundation, the National Institutes of Health, and the State of

Ne-128. braska (LB595 and LB692). H.W.D. benefited by partial support from

Kraft, P., E. Schadt, J. AtenandS. Horvath, 2003 A family-based Hunan Province, Chinese National Science Foundation, Huo Ying

test for correlation between gene expression and trait values.
Dong Education Foundation, the Cheng Kong scholar program, and _{Am. J. Hum. Genet.}_{72:}_{1323–1330.}

the Ministry of Education of the People’s Republic of China. _{Lange, K}_{., 1997} _{Mathematical and Statistical Methods for Genetic }

*Analy-sis*. Springer-Verlag, New York.

Lange, K., andM. Boehnke, 1983 Extensions to pedigree analysis. IV. Covariance components models for multivariate traits. Am.

LITERATURE CITED _{J. Med. Genet.}_{14:}_{513–524.}

Li, J., N. RischandR. M. Myers, 2003 Heritability of gene

expres-Abecasis, G. R., L. R. CardonandW. O. Cookson, 2000 A general

sion in humans: a study of lymphoblastoid cell lines from twins. test of association for quantitative traits in nuclear families. Am.

Am. J. Hum. Genet.**73:**S207.
J. Hum. Genet.**66:**279–292.

Lynch, M., andB. Walsh, 1998 *Genetics and Analysis of Quantitative*

Amos, C. I., D. K. ZhuandE. Boerwinkle, 1996 Assessing genetic

*Traits*. Sinauer Associates, Sunderland, MA.
linkage and association with robust components of variance

ap-Nguyen, D. V., A. B. Arpat, N. WangandR. J. Carroll, 2002 DNA
proaches. Ann. Hum. Genet.**60:**143–160.

microarray experiments: biological and technological aspects.

Bakay, M., Y. W. Chen, R. Borup, P. Zhao, K. Nagaraju*et al.*, 2002

Biometrics**58:**701–717.
Sources of variability and effect of experimental approach on

Nogalska, A., andJ. Swierczynski, 2001 The age-related
differ-expression profiling data interpretation. BMC Bioinformatics**3:**

ences in obese and fatty acid synthase gene expression in white 4.

adipose tissue of rat. Biochim. Biophys. Acta**1533:**73–80.

Brem, R. B., G. Yvert, R. ClintonandL. Kruglyak, 2002 Genetic

Roth, S. M., R. E. Ferrell, D. G. Peters, E. J. Metter, B. F. Hurley*et*

dissection of transcriptional regulation in budding yeast. Science

*al.*, 2002 Influence of age, sex, and strength training on human

**296:**752–755.

muscle gene expression determined by microarray. Physiol.

Ge-Chakravarti, A., 1999 Population genetics—making sense out of

nomics**10:**181–190.
sequence. Nat. Genet.**21:**56–60.

Schadt, E. E., S. Monks, T. Drake, A. Lusis, N. Che*et al.*, 2003

Cheung, V. G., andR. S. Spielman, 2002 The genetics of variation

The genetics of gene expression surveyed in maize, mouse and
in gene expression. Nat. Genet.**32:**522–525.

man. Nature**422:**297–302.

Deng, H. W., 2001 Population admixture may appear to mask,

Shannon, W. D., M. A. Watson, A. PerryandK. Rich, 2002 Mantel change or reverse genetic effects of genes underlying complex

statistics to correlate gene expression levels from microarrays
traits. Genetics**159:**1319–1323.

with clinical covariates. Genet. Epidemiol.**23:**87–96.

Deng, H. W., F. H. Xu, Q. Y. Huang, H. Shen, H. Deng*et al.*, 2002

Wolfinger, R. D., G. Gibson, E. Wolfinger, L. Bennett, H.

Hama-A whole-genome linkage scan suggests several genomic regions

potentially containing quantitative trait loci for osteoporosis. J. deh*et al.*, 2001 Assessing gene significance from cDNA
micro-Clin. Endocrinol. Metab.**87:**5151–5159. array expression data via mixed models. J. Comput. Biol.**8:**625–

Gibson, G., 2003 Population genomics: celebrating individual ex- 637.

pression. Heredity**90:**1–2. Yang, Y. H., andT. Speed, 2002 Design issues for cDNA microarray

Hopper, J. L., andJ. D. Mathews, 1982 Extensions to multivariate experiments. Nat. Rev.**3:**579–588.

normal models for pedigree analysis. Ann. Hum. Genet.**46:**373– Yang, W. S., W. J. Lee, K. C. Huang, K. C. Lee, C. L. Chao*et al.*,

383. 2003 mRNA levels of the insulin-signaling molecule SORBS1

Jin, W., R. M. Riley, R. D. Wolfinger, K. P. White, G. Passador- in the adipose depots of nondiabetic women. Obesity Res.**11:**

Gurgel *et al.*, 2001 The contributions of sex, genotype and 586–590.
age to transcriptional variance in Drosophila melanogaster. Nat.