Statistical significance for genomewide studies

Abstract

With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.false discovery rates ͉ genomics ͉ multiple hypothesis testing ͉ q values

Genetic effects on gene expression across human tissues

“…The patterns of sharing were also supported by replication between single-tissue cis -eQTLs, estimated by π 1 (the proportion of true positives 16 ) among the eQTLs identified in one tissue and then tested for replication in a second tissue (Extended Data Fig. 7a, median π 1 = 0.740).…”

“…The significance of the most highly associated variant per gene was determined from empirical P values, extrapolated from a Beta distribution fitted to adaptive permutations with the setting –permute 1000 10000. These empirical P values were subsequently corrected for multiple testing across genes using Storey’s q value method 16 . To identify the list of all significant variant–gene pairs associated with eGenes, variants with a nominal P value below the gene-level threshold were considered significant and included in the final list of variant–gene pairs.…”

“…2b, Supplementary Information 7, 8, Supplementary Figs 12–15 and Supplementary Table 11). An alternative approach to quantifying FDR at the gene level (Supplementary Information 8 and Supplementary Table 12) identified 46 genes at 10% FDR, with estimated q values of less than 0.4 for all 94 gene associations identified using the genome-wide FDR 16 . By investigating long-range intra-chromosomal eQTLs (≥5 Mb from the TSS), we discovered an additional 33 eGenes (10% FDR; Extended Data Table 2 and Supplementary Information 9).…”

The first complete mitogenome of the South China deep‐sea giant isopod Bathynomus sp. (Crustacea: Isopoda: Cirolanidae) allows insights into the early mitogenomic evolution of isopods

“…To estimate the evolutionary rate, standard branch models calculated with the CODEML program of PAML 4.6 (Yang, 2007) were used, and an adjusted Chi‐square test (Storey & Tibshirani, 2003) was applied for testing p values.…”

Identification of loci affecting teat number by genome-wide association studies on three pig populations

“…Q-values were computed by the q-value package of R language and were then used to estimate the false discovery rate (FDR) threshold for the significant p-value of 0.05 [13, 14]. Confidence regions were determined by log of odds ratio (LOD) dropoff 2 from the peak SNPs.…”