Case-Only Design to Measure Gene-Gene Interaction

Yang, Quanhe; Mj, Khoury; Sun, Fengzhu; Flanders, W. Dana

doi:10.1097/00001648-199903000-00014

Cited by 130 publications

(85 citation statements)

References 22 publications

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“…We considered gene-by-gene interaction of all pairs of unlinked genes lying on the same androgen-dependent prostatic growth pathway by constructing a series of genotype-by-genotype tables (three by three contingency tables) and testing the independence. 45 An overall w 2 (d.f. ¼ 4) was calculated for each pair to test the null hypothesis that the joint effects were multiplicative (statistical significance was two-tailed and defined as Po0.05).…”

Section: Discussionmentioning

confidence: 99%

A case-based evaluation of SRD5A1, SRD5A2, AR, and ADRA1A as candidate genes for severity of BPH

et al. 2004

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In men with a clinical diagnosis of benign prostatic hyperplasia (BPH), polytomous logistic regression analysis was conducted to evaluate associations between two silent polymorphisms in SRD5A1 (codon positions 30 and 116), two polymorphisms in SRD5A2 (Val89Leu substitution and C to T transition in intron 1), a trinucleotide (CAG) n repeat in androgen receptor (AR), and an Arg492Cys substitution in ADRA1A and clinical parameters that characterize severity of BPH. Candidate gene selection was based on two mechanistic pathways targeted by pharmacotherapy for BPH: (1) androgen metabolic loci contributing to prostate growth (static obstruction); and (2) factors affecting smooth muscle tone (dynamic obstruction). Polymorphisms in SRD5A2 were not associated with severity of BPH; however, SRD5A1 polymorphisms were associated with severity of BPH. The process(es) in which these silent single-nucleotide polymorphisms (SNPs) influence BPH phenotypes is unknown and additional studies will be needed to assess whether these SNPs have direct functional consequences. The characterization of additional molecular factors that contribute to static and dynamic obstruction may help predict response to pharmacotherapy and serve to identify novel drug targets for the clinical management of BPH.

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Section: Discussionmentioning

confidence: 99%

A case-based evaluation of SRD5A1, SRD5A2, AR, and ADRA1A as candidate genes for severity of BPH

et al. 2004

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“…As for population assumptions that constrain the parameter space for p, two popular and broadly applicable ones are HWE (the independence of maternal and paternal allele values that make up each SNP) and LE (the independence of the two loci that make up a pair) (Yang et al 1999;Chatterjee and Carroll 2005;Zhao et al 2006). Although such assumptions have their limitations (Albert et al 2001;Mukherjee and Chatterjee 2008), HWE can be expected to hold for homogeneous populations, and LE is plausible in the case of distal locus pairs, i.e., almost all pairs considered in a typical GWAS (Chen and Chatterjee 2007;Song and Nicolae 2009 -eliminating an additional parameter.…”

Section: Association Testing With Case-control-population Datamentioning

confidence: 99%

“…This improves power to detect associations that comply with the assumptions, at the often acceptable cost of reduced power to detect implausible associations. Two popular and broadly applicable population assumptions that constrain the parameter space are Hardy-Weinberg equilibrium (HWE) (the independence of maternal and paternal allele values that make up each genotype locus) and linkage equilibrium (LE) (the independence of the two loci that make up a pair) (Yang et al 1999;Chatterjee and Carroll 2005;Zhao et al 2006). Imperfect as they may be (see Discussion), these assumptions are expected to hold for the vast majority of associations considered in a typical GWAS.…”

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confidence: 99%

“…are allelic, dominant, recessive, and additive SNP effects (Sasieni 1997;Freidlin et al 2009), which can be tested with the aforementioned nonparametric approaches similarly to standard ones, i.e., by using transformed genotypes or treating them as continuous (Zheng et al 2012). As for population assumptions that constrain the parameter space for p, two popular and broadly applicable ones are HWE (the independence of maternal and paternal allele values that make up each SNP) and LE (the independence of the two loci that make up a pair) (Yang et al 1999 -eliminating an additional parameter. Assume HWE at x 2 has the same effect (with the minor allele frequency f 2 parameterizing p (2) ).…”

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Exploiting Population Samples to Enhance Genome-Wide Association Studies of Disease

Kaufman

2014

Genetics

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It is widely acknowledged that genome-wide association studies (GWAS) of complex human disease fail to explain a large portion of heritability, primarily due to lack of statistical power-a problem that is exacerbated when seeking detection of interactions of multiple genomic loci. An untapped source of information that is already widely available, and that is expected to grow in coming years, is population samples. Such samples contain genetic marker data for additional individuals, but not their relevant phenotypes. In this article we develop a highly efficient testing framework based on a constrained maximum-likelihood estimate in a case-controlpopulation setting. We leverage the available population data and optional modeling assumptions, such as Hardy-Weinberg equilibrium (HWE) in the population and linkage equilibrium (LE) between distal loci, to substantially improve power of association and interaction tests. We demonstrate, via simulation and application to actual GWAS data sets, that our approach is substantially more powerful and robust than standard testing approaches that ignore or make naive use of the population sample. We report several novel and credible pairwise interactions, in bipolar disorder, coronary artery disease, Crohn's disease, and rheumatoid arthritis. G ENOME-WIDE association studies (GWAS) have implicated thousands of single-nucleotide polymorphisms (SNPs) in the human genome as associated with hundreds of phenotypes (Johnson and O'Donnell 2009). However, as many researchers have pointed out (Manolio et al. 2009;Eichler et al. 2010), the results from GWAS fail to explain the observed heritability of many phenotypes, including complex human diseases, whose genetic architectures remain largely unknown. One often-cited reason for this problem is that the high multiple-testing burden requires an exceedingly stringent statistical significance level. Furthermore, while most studies have employed univariate (locusby-locus) testing approaches, complex diseases are likely to be affected by interactions between loci (Eichler et al. 2010). Such interactions arise when there is a dependence of genotypic effects of one locus on genotypes at other loci (Cordell 2009).In the case of interactions, due to the overwhelming number of locus subsets, the multiple-testing problem becomes a serious computational and statistical challenge. Even when limiting exploration to pairwise SNP-SNP interactions, a modest study including 300,000 usable loci requires testing 45 billion SNP pairs, and associations must have P-values , 10 212 (the 0.05 Bonferroni-corrected significance level) to be declared statistically significant genome-wide. Recently, several authors have suggested sophisticated approximate and exhaustive methods for detecting pairwise interactions (Brinza et al. 2010;Liu et al. 2011;Prabhu and Pe'er 2012). These methods constitute a major step in dealing with the computational issue of carrying out the large number of tests, but their application to actual studies has led to surprisingly ...

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“…Similar to the SC test, the case-only interaction test proposed by Piegorsch et al (1994) compares the distribution of the product of genotypes at a pair of SNPs to the expectation of that product under the assumption of linkage equilibrium between the markers. Alternatively, the case-only interaction test proposed by Yang et al (1999) tests for departures from additivity under a logit model and the case-only interaction tests proposed by Wu et al (2010) and improved in Ueki and Cordell (2012) also consider a logit model. While easily performed, these case-only tests fail to leverage the information available from the parents in the trios and are susceptible to inflation from population structure.…”

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confidence: 99%

A Novel Test for Detecting SNP–SNP Interactions in Case-Only Trio Studies

Balliu

Zaitlen

2016

Genetics

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Epistasis plays a significant role in the genetic architecture of many complex phenotypes in model organisms. To date, there have been very few interactions replicated in human studies due in part to the multiple-hypothesis burden implicit in genome-wide tests of epistasis. Therefore, it is of paramount importance to develop the most powerful tests possible for detecting interactions. In this work we develop a new SNP-SNP interaction test for use in case-only trio studies called the trio correlation (TC) test. The TC test computes the expected joint distribution of marker pairs in offspring conditional on parental genotypes. This distribution is then incorporated into a standard 1 d.f. correlation test of interaction. We show via extensive simulations under a variety of disease models that our test substantially outperforms existing tests of interaction in case-only trio studies. We also demonstrate a bias in a previous case-only trio interaction test and identify its origin. Finally, we show that a previously proposed permutation scheme in trio studies mitigates the known biases of caseonly tests in the presence of population stratification. We conclude that the TC test shows improved power to identify interactions in existing, as well as emerging, trio association studies. The method is publicly available at www.github.com/BrunildaBalliu/TrioEpi.

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Case-Only Design to Measure Gene-Gene Interaction

Cited by 130 publications

References 22 publications

A case-based evaluation of SRD5A1, SRD5A2, AR, and ADRA1A as candidate genes for severity of BPH

A case-based evaluation of SRD5A1, SRD5A2, AR, and ADRA1A as candidate genes for severity of BPH

Exploiting Population Samples to Enhance Genome-Wide Association Studies of Disease

A Novel Test for Detecting SNP–SNP Interactions in Case-Only Trio Studies

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