Using the False Discovery Rate Approach in the Genetic Dissection of Complex Traits: A Response to Weller et al.

Abstract Saturated genetic marker maps are being used to map individual genes affecting quantitative traits. Controlling the “experimentwise” type-I error severely lowers power to detect segregating loci. For preliminary genome scans, we propose controlling the “false discovery rate,” that is, the expected proportion of true null hypotheses within the class of rejected null hypotheses. Examples are given based on a granddaughter design analysis of dairy cattle and simulated backcross populations. By controlling the false discovery rate, power to detect true effects is not dependent on the number of tests performed. If no detectable genes are segregating, controlling the false discovery rate is equivalent to controlling the experimentwise error rate. If quantitative loci are segregating in the population, statistical power is increased as compared to control of the experimentwise type-I error. The difference between the two criteria increases with the increase in the number of false null hypotheses. The false discovery rate can be controlled at the same level whether the complete genome or only part of it has been analyzed. Additional levels of contrasts, such as multiple traits or pedigrees, can be handled without the necessity of a proportional decrease in the critical test probability.

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A practical false discovery rate approach to identifying patterns of differential expression in microarray data

Bioinformatics ◽

10.1093/bioinformatics/bti407 ◽

2005 ◽

Vol 21 (11) ◽

pp. 2684-2690 ◽

Cited By ~ 61

Author(s):

G. R. Grant ◽

J. Liu ◽

C. J. Stoeckert

Keyword(s):

False Discovery Rate ◽

Differential Expression ◽

Microarray Data ◽

False Discovery ◽

False Discovery Rate Approach

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Selective inference in complex research

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2009.0127 ◽

2009 ◽

Vol 367 (1906) ◽

pp. 4255-4271 ◽

Cited By ~ 31

Author(s):

Yoav Benjamini ◽

Ruth Heller ◽

Daniel Yekutieli

Keyword(s):

Type 2 Diabetes ◽

False Discovery Rate ◽

Confidence Intervals ◽

False Discovery ◽

False Discovery Rate Approach ◽

Selective Inference

We explain the problem of selective inference in complex research using a recently published study: a replicability study of the associations in order to reveal and establish risk loci for type 2 diabetes. The false discovery rate approach to such problems will be reviewed, and we further address two problems: (i) setting confidence intervals on the size of the risk at the selected locations and (ii) selecting the replicable results.

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Gene Prioritization for Imaging Genetics Studies Using Gene Ontology and a Stratified False Discovery Rate Approach

Frontiers in Neuroinformatics ◽

10.3389/fninf.2016.00014 ◽

2016 ◽

Vol 10 ◽

Cited By ~ 5

Author(s):

Sejal Patel ◽

Min Tae M. Park ◽

M. Mallar Chakravarty ◽

Jo Knight

Keyword(s):

Gene Ontology ◽

False Discovery Rate ◽

Gene Prioritization ◽

Imaging Genetics ◽

False Discovery ◽

False Discovery Rate Approach

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SEMI-PARAMETRIC COVARIATE-MODULATED LOCAL FALSE DISCOVERY RATE FOR GENOME-WIDE ASSOCIATION STUDIES

10.1101/183384 ◽

2017 ◽

Author(s):

Rong W. Zablocki ◽

Richard A. Levine ◽

Andrew J. Schork ◽

Shujing Xu ◽

Yunpeng Wang ◽

...

Keyword(s):

False Discovery Rate ◽

Complex Traits ◽

Association Studies ◽

Logistic Function ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Local False Discovery Rate ◽

B Spline ◽

False Discovery ◽

Genome Wide

While genome-wide association studies (GWAS) have discovered thousands of risk loci for heritable disorders, so far even very large meta-analyses have recovered only a fraction of the heritability of most complex traits. Recent work utilizing variance components models has demonstrated that a larger fraction of the heritability of complex phenotypes is captured by the additive effects of SNPs than is evident only in loci surpassing genome-wide significance thresholds, typically set at a Bonferroni-inspired p ≤ 5 x 10-8. Procedures that control false discovery rate can be more powerful, yet these are still under-powered to detect the majority of non-null effects from GWAS. The current work proposes a novel Bayesian semi-parametric two-group mixture model and develops a Markov Chain Monte Carlo (MCMC) algorithm for a covariate-modulated local false discovery rate (cmfdr). The probability of being non-null depends on a set of covariates via a logistic function, and the non-null distribution is approximated as a linear combination of B-spline densities, where the weight of each B-spline density depends on a multinomial function of the covariates. The proposed methods were motivated by work on a large meta-analysis of schizophrenia GWAS performed by the Psychiatric Genetics Consortium (PGC). We show that the new cmfdr model fits the PGC schizophrenia GWAS test statistics well, performing better than our previously proposed parametric gamma model for estimating the non-null density and substantially improving power over usual fdr. Using loci declared significant at cmfdr ≤ 0.20, we perform follow-up pathway analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) homo sapiens pathways database. We demonstrate that the increased yield from the cmfdr model results in an improved ability to test for pathways associated with schizophrenia compared to using those SNPs selected according to usual fdr.

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Identification of novel common variants associated with chronic pain using conditional false discovery rate analysis with major depressive disorder and assessment of pleiotropic effects of LRFN5

10.1101/495846 ◽

2018 ◽

Author(s):

Keira J.A. Johnston ◽

Mark J. Adams ◽

Barbara I. Nicholl ◽

Joey Ward ◽

Rona J Strawbridge ◽

...

Keyword(s):

Chronic Pain ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

False Discovery Rate ◽

Complex Traits ◽

Pleiotropic Effects ◽

Chromosome 1 ◽

Chromosome 14 ◽

Major Depressive ◽

False Discovery

AbstractChronic pain is highly prevalent worldwide, with a significant socioeconomic burden, and also contributes to excess mortality. Chronic pain is a complex trait that is moderately heritable and genetically, as well as phenotypically, correlated with major depressive disorder (MDD). Use of the Conditional False Discovery Rate (cFDR) approach, which leverages pleiotropy identified from existing GWAS outputs, has been successful in discovering novel associated variants in related phenotypes. Here, genome-wide association study outputs for both von Korff chronic pain grade as a quasi-quantitative trait and for MDD were used to identify variants meeting a cFDR threshold for each outcome phenotype separately, as well as a conjunctional cFDR (ccFDR) threshold for both phenotypes together. Using a moderately conservative threshold, we identified a total of 11 novel single nucleotide polymorphisms (SNPs), six of which were associated with chronic pain grade and nine of which were associated with MDD. Four SNPs on chromosome 14 were associated with both chronic pain grade and MDD. SNPs associated only with chronic pain grade were located within SLC16A7 on chromosome 12. SNPs associated only with MDD were located either in a gene-dense region on chromosome 1 harbouring LINC01360, LRRIQ3, FPGT and FPGT-TNNI3K, or within/close to LRFN5 on chromosome 14. The SNPs associated with both outcomes were also located within LRFN5. Several of the SNPs on chromosomes 1 and 14 were identified as being associated with expression levels of nearby genes in the brain and central nervous system. Overall, using the cFDR approach, we identified several novel genetic loci associated with chronic pain and we describe likely pleiotropic effects of a recently identified MDD locus on chronic pain.Author SummaryGenetic variants explaining variation in complex traits can often be associated with more than one trait at once (‘pleiotropy’). Taking account of this pleiotropy in genetic studies can increase power to find sites in the genome harbouring trait-associated variants. In this study we used the suspected underlying pleiotropy between chronic pain and major depressive disorder to discover novel variants associated with chronic pain, and to investigate genetic variation that may be shared between the two disorders.

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