Volume 1 Supplement 1
Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci
A mixture model approach to multiple testing for the genetic analysis of gene expression
- Cyril Dalmasso^{1}Email author,
- Joseph Pickrell^{2, 3},
- Marianne Tuefferd^{1},
- Emmanuelle Génin^{2},
- Catherine Bourgain^{2} and
- Philippe Broët^{1}
DOI: 10.1186/1753-6561-1-S1-S141
© Dalmasso et al; licensee BioMed Central Ltd. 2007
Published: 18 December 2007
Abstract
With the availability of very dense genome-wide maps of markers, multiple testing has become a major difficulty for genetic studies. In this context, the false-discovery rate (FDR) and related criteria are widely used. Here, we propose a finite mixture model to estimate the local FDR (lFDR), the FDR, and the false non-discovery rate (FNR) in variance-component linkage analysis. Our parametric approach allows empirical estimation of an appropriate null distribution. The contribution of our model to estimation of FDR and related criteria is illustrated on the microarray expression profiles data set provided by the Genetic Analysis Workshop 15 Problem 1.
Background
In the context of genetic studies for which high-density genetic maps are now widely available, a major multiple testing problem arises due to the large number of statistical tests that are performed simultaneously. In a recent study, Morley et al. [1] analysed microarray gene-expression data together with genome-wide single nucleotide-polymorphism (SNP) genotyping in 14 three-generation families to localize the genetic determinants underlying gene-expression variability (data provided for Genetic Analysis Workshop 15 (GAW 15) Problem 1). For the genome-wide linkage analysis, the authors calculated a non-parametric Haseman-Elston statistic and used the genome-wide significance thresholds proposed by Lander and Kruglyak [2] to identify linked loci. Thus, they controlled the classical family-wise error rate (FWER), i.e., the probability of falsely rejecting at least one null hypothesis.
Although the FWER is the oldest extension of the classical type I error rate, FWER-based procedures are often too conservative, particularly when numerous hypotheses are being tested [3]. As an alternative and less stringent error criterion, Benjamini and Hochberg introduced, in their seminal paper published in 1995 [4], the false-discovery rate (FDR), which is defined as the expected proportion of false discoveries among all discoveries (here, a discovery refers to a rejected null hypothesis). The opposing criterion, the false non-discovery rate (FNR), corresponds to the expected proportion of false non-discoveries among all the non-rejected null hypotheses [5].
More recently, Efron et al. introduced the local FDR (lFDR) [6], which can be interpreted as a variant of the Benjamini and Hochberg's FDR that gives each tested null hypothesis its own "measure of significance". While the FDR is defined for a whole rejection region, the lFDR is defined as the probability that a null hypothesis is true conditional on a particular value of the test statistic. As discussed by Efron [7], the local nature of the lFDR is advantageous for interpreting results from individual test statistics. Moreover, the FDR can be estimated directly from the lFDR [6].
Efron proposed an empirical Bayes' procedure [7, 8] to estimate the lFDR without any assumption about the distribution under the alternative hypothesis. From this procedure, only an upper bound estimate can be obtained for the lFDR and, indirectly, a lower bound for the FNR. One important feature of this approach is that it considers an empirical rather than theoretical null distribution. Indeed, as noted by Efron, these distributions may be very different and strong arguments support using the empirical one in genetic studies for which extensive data are available [5].
In this work, and for variance-component linkage analysis, we introduced a two-component mixture model based approach that allows estimation of lFDR, FDR, and FNR. We illustrate the contribution of our model to the analysis of real GAW15 data. Our results highlight the importance of correctly estimating the null distribution through the proposed mixture model based approach.
Methods
Consider the variance-component linkage analysis between a particular phenotype (here, the expression level of a defined gene) and a specific marker. The null hypothesis of no linkage (additive genetic variance due to the studied quantitative trait locus (QTL) equals zero) is tested by comparing the likelihood of this restricted model with that of a model in which the variance is estimated. Under the null hypothesis, the theoretical asymptotic distribution of the likelihood-ratio statistic X is a 50:50 mixture of a χ^{2} and a point mass at 0 [9]. When testing n markers, n likelihood-ratio statistics X_{ i }, (i = 1,...,n) are available, with each X_{ i }following either the null or the alternative distribution.
For modeling of the marginal distribution of X, we consider the following two-component mixture model, in which the marginal cumulative distribution F_{ X }of X is:
F_{ X }(x) = ω_{1} × {θ × 1_{{X=0} }+ (1 - θ) ×
F_{1}(x|α_{1}, β_{1})} + ω_{2} × F_{2}(x|α_{2}, β_{2}),
where ω_{ c }is the mixing proportions for the c components (c = 1, 2; ω_{ c }∈ [0, 1]; ω_{1} + ω_{2} = 1). Here, c = 1 corresponds to the null hypothesis component and c = 2 to the alternative hypothesis component, respectively. The parameter θ ∈ [0, 1] is the weight of the point mass at 0 for the null hypothesis component.
In this model, the conditional distributions F_{ c }(x|α_{ c }, β_{ c }) are gamma distributions with parameters α_{ c }and β_{ c }, where α_{ c }is the mean and α_{ c }/β_{ c }the variance of the distribution. Here, we impose that α_{1} <α_{2}.
As discussed in the Background, the empirical distribution under the null hypothesis can be very different from the theoretical distribution [8]. Therefore, we decided to not consider theoretical values (θ = 1/2, α_{1} = 1, and β_{1} = 1/2) for the first component distribution parameters but rather to estimate them. For the second component, we used a gamma distribution, which represents a convenient and parsimonious way to model the non-null distribution.
Parameters of interest are inferred by sampling from their joint posterior distributions using Monte Carlo Markov chain (MCMC) samplers implemented in WinBUGS software [9]. All results presented correspond to 25,000 sweeps of MCMC algorithms following a burn-in period of 25,000 sweeps (period required to achieve algorithm stability). Convergence is checked by visual inspection of the curve of the plots for the different parameters of the mixtures.
For each marker, the posterior probabilities of belonging to the null hypothesis can be estimated directly from the algorithm output, using empirical averages. These probabilities are natural estimates of the lFDR for each marker. They can be used to compute model-based estimates of the observed FDR and FNR (conditionally to the data) [10, 11].
Results
We started from the cell intensity files (*.CEL) obtained from the GeneChip^{®} Human Genome Focus Array Hgfocus [12] that provide gene-expression measurements of 8794 probe sets for 276 samples. We normalized and summarized those measurements using the robust multi-array average (RMA) method proposed by Irizarry et al. [13]. A multipoint variance-component linkage analysis was performed with MERLIN [14] on the normalized phenotypes using all 194 individuals from the 14 Centre d'Etude du Polymorphisme Humain (CEPH) families and the 2819 autosomal SNP data. Using the proposed mixture model, we then estimated the lFDR at each marker, and FDR and FNR. Here, we present only the results obtained for the following 10 genes discussed in the article by Morley et al. [1]: CHI3L2, DDX17, PSPHL, IL16, HOMER1, ALG6, CBR1, TNFRSF11A, TGIF, and DSCR2.
Estimated parameters of the two-component mixture model for each of the ten genes analyzed
Gene | θ | α _{1} | β _{1} | α _{2} | β _{2} |
---|---|---|---|---|---|
CHI3L2 | 0.41 | 1.72 | 0.41 | 40.40 | 0.47 |
DDX17 | 0.47 | 2.96 | 0.24 | 33.90 | 0.32 |
PSPHL | 0.39 | 2.76 | 0.25 | 63.04 | 0.73 |
IL16 | 0.50 | 1.08 | 0.90 | 5.14 | 1.85 |
HOMER1 | 0.45 | 1.02 | 0.92 | 3.36 | 1.25 |
ALG6 | 0.53 | 0.79 | 1.58 | 9.71 | 2.77 |
CBR1 | 0.41 | 0.74 | 1.37 | 2.42 | 2.15 |
TNFRSF11A | 0.52 | 1.27 | 0.68 | 7.34 | 2.97 |
TGIF | 0.54 | 0.70 | 1.35 | 3.96 | 1.47 |
DSCR2 | 0.52 | 0.83 | 1.17 | 3.57 | 1.38 |
However, it is difficult to directly compare the two approaches because the selection strategies rely on completely different criteria. Moreover, it is worth noting that while the Bonferroni procedure depends on the order of the p-values, our procedure depends on the order of the posterior probability (lFDR) values, and the two can be completely different.
Conclusion
Herein we described a mixture model based approach to estimate FDR, FNR, and lFDR in the context of variance component linkage analyses. This approach allows the selection process to take into account both false positives and false negatives. Moreover, it provides an estimate of the empirical null distribution, which is a key component for any simultaneous inference procedure.
Indeed, in many situations, the empirical null distribution deviates from the theoretical one [8], leading to incorrect statistical inferences and resulting decisions. Traditional estimating methods in linkage analysis used simulation approaches in which marker alleles were randomly dropped from the genealogies. When markers are numerous or pedigrees are complex, that method can become very burdensome, with computations requiring several days of running time. New genetic studies for which large amounts of data are available open new opportunities by allowing the estimation of appropriate null and alternative densities without resorting to simulations. Hence, our approach is much easier to handle because examination of each of the different phenotypes analysed required less than 1 hour of computer time. It is important to note that this approach can be extended by incorporating different null distribution parameters for a set of phenotypes in a single model. In conclusion, we think that new insights on linkage analysis using genome-wide technologies might emerge from a mixture model-based approach.
Declarations
Acknowledgements
This article has been published as part of BMC Proceedings Volume 1 Supplement 1, 2007: Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci. The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/1?issue=S1.
Authors’ Affiliations
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