Additional file 4 of Control procedures and estimators of the false discovery rate and their application in low-dimensional settings: an empirical investigation

Simulation – Observed estimations of π0 for Storey’s (qv) and Strimmer’s q-value methods (fdr)for β1 = 1 and π0 = 75% (a), 50% (b), 25% (c). (PNG 209 kb

QQ-plots of the number of observed significant genes under the null hypothesis comparing random draws of gene input lists and simulated draws.

<p>The graph shows that simulated draws based on a binomial experiment approximate the number of significant genes under the null hypothesis derived from iterations of randomly generated input gene lists, while being computationally more efficient. QQ plots were generated across a range of possible significance thresholds. Spearman correlation coefficients were determined for each setting and found to be in the range of 0.90–1.00.</p

Newly implicated genes identified by GenToS in association with bone mineral density phenotypes.

<p>These genes either mapped into known associated GWAS regions but were not previously named as the index gene, or were not replicated at genome-wide significance at the time the GWAS data was published.</p

GenToS: Use of Orthologous Gene Information to Prioritize Signals from Human GWAS

<div><p>Genome-wide association studies (GWAS) evaluate associations between genetic variants and a trait or disease of interest free of prior biological hypotheses. GWAS require stringent correction for multiple testing, with genome-wide significance typically defined as association p-value <5*10⁻⁸. This study presents a new tool that uses external information about genes to prioritize SNP associations (GenToS). For a given list of candidate genes, GenToS calculates an appropriate statistical significance threshold and then searches for trait-associated variants in summary statistics from human GWAS. It thereby allows for identifying trait-associated genetic variants that do not meet genome-wide significance. The program additionally tests for enrichment of significant candidate gene associations in the human GWAS data compared to the number expected by chance. As proof of principle, this report used external information from a comprehensive resource of genetically manipulated and systematically phenotyped mice. Based on selected murine phenotypes for which human GWAS data for corresponding traits were publicly available, several candidate gene input lists were derived. Using GenToS for the investigation of candidate genes underlying murine skeletal phenotypes in data from a large human discovery GWAS meta-analysis of bone mineral density resulted in the identification of significantly associated variants in 29 genes. Index variants in 28 of these loci were subsequently replicated in an independent GWAS replication step, highlighting that they are true positive associations. One signal, <i>COL11A1</i>, has not been discovered through GWAS so far and represents a novel human candidate gene for altered bone mineral density. The number of observed genes that contained significant SNP associations in human GWAS based on murine candidate gene input lists was much greater than the number expected by chance across several complex human traits (enrichment p-value as low as 10⁻¹⁰). GenToS can be used with any candidate gene list, any GWAS summary file, runs on a desktop computer and is freely available.</p></div

GenToS principle.

<p><b>(A)</b> First, GenToS extracts for each gene on a given candidate gene input list the region of the gene including a user-defined flanking region. <b>(B)</b> Next, all independent SNPs within each region are identified from a reference population, and a significance threshold based on the number of independent SNPs is calculated. <b>(C)</b> In the final step, SNPs with an association p-value below the calculated significance threshold are extracted from the human GWAS summary results. <b>(D)</b> Enrichment of the number of observed significant genes (vertical line) can be assessed visually compared to the expected number based on a null distribution derived by resampling from a binomial distribution (histogram).</p

Additional file 3 of Control procedures and estimators of the false discovery rate and their application in low-dimensional settings: an empirical investigation

Simulation – Average power and specificity for β1 = 1 and π0 = 75% (a), 50% (b), 25% (c). Applied procedures controlling the type I error: Bonferroni correction, Hommel’s procedure, Benjamini-Hochberg’s procedure, Two-stage procedure, Benjamini-Yekutieli’s procedure, Storey’s q-value method, Strimmer’s q-value method, Strimmer’s LFDR method. Power is defined as the proportion of correctly rejected hypotheses and specificity as the proportion of correctly maintained hypotheses. Both proportions potentially range from 0 to 1. Simulations for each scenario were repeated 100 times. (PNG 457 kb

Genes identified by GenToS in association with human bone mineral density phenotypes that reached genome-wide significance and were replicated in previous GWAS.

<p>Genes identified by GenToS in association with human bone mineral density phenotypes that reached genome-wide significance and were replicated in previous GWAS.</p

Ontology terms and number of genes in each murine input gene list.

<p>Ontology terms and number of genes in each murine input gene list.</p

Genome-Wide Association of Copy Number Polymorphisms and Kidney Function

<div><p>Genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNPs) have identified more than 50 loci associated with estimated glomerular filtration rate (eGFR), a measure of kidney function. However, significant SNPs account for a small proportion of eGFR variability. Other forms of genetic variation have not been comprehensively evaluated for association with eGFR. In this study, we assess whether changes in germline DNA copy number are associated with GFR estimated from serum creatinine, eGFRcrea. We used hidden Markov models (HMMs) to identify copy number polymorphic regions (CNPs) from high-throughput SNP arrays for 2,514 African (AA) and 8,645 European ancestry (EA) participants in the Atherosclerosis Risk in Communities (ARIC) study. Separately for the EA and AA cohorts, we used Bayesian Gaussian mixture models to estimate copy number at regions identified by the HMM or previously reported in the HapMap Project. We identified 312 and 464 autosomal CNPs among individuals of EA and AA, respectively. Multivariate models adjusted for SNP-derived covariates of population structure identified one CNP in the EA cohort near genome-wide statistical significance (Bonferroni-adjusted p = 0.067) located on chromosome 5 (876–880kb). Overall, our findings suggest a limited role of CNPs in explaining eGFR variability.</p></div

Statistical significance of copy number in linear regression models for eGFRcrea.

<p>(A) Manhattan plot for CNP association analysis in eGFRcrea among 8,645 European ancestry and 2,514 AA participants in the ARIC study. The gray line indicates genome-wide statistical significance. (B) Quantile-quantile plots of the expected–log 10 p-values under the null hypothesis of no association versus the observed–log 10 p-values. The lower and upper bounds of the shaded region indicate 0.025 and 0.975 quantiles, respectively, of the null.</p