Significance testing and genomic inflation factor using high‐density genotypes or whole‐genome sequence data

Abstract

Significance testing for genome‐wide association study (GWAS) with increasing SNP density up to whole‐genome sequence data (WGS) is not straightforward, because of strong LD between SNP and population stratification. Therefore, the objective of this study was to investigate genomic control and different significance testing procedures using data from a commercial pig breeding scheme. A GWAS was performed in GCTA with data of 4,964 Large White pigs using medium density, high density or imputed whole‐genome sequence data, fitting a genomic relationship matrix based on a leave‐one–chromosome‐out approach to account for population structure. Subsequently, genomic inflation factors were assessed on whole‐genome level and the chromosome level. To establish a significance threshold, permutation testing, Bonferroni corrections using either the total number of SNPs or the number of independent chromosome fragments, and false discovery rates (FDR) using either the Benjamini–Hochberg procedure or the Benjamini and Yekutieli procedure were evaluated. We found that genomic inflation factors did not differ between different density genotypes but do differ between chromosomes. Also, the leave‐one‐chromosome‐out approach for GWAS or using the pedigree relationships did not account appropriately for population stratification and gave strong genomic inflation. Regarding different procedures for significance testing, when the aim is to find QTL regions that are associated with a trait of interest, we recommend applying the FDR following the Benjamini and Yekutieli approach to establish a significance threshold that is adjusted for multiple testing. When the aim is to pinpoint a specific mutation, the more conservative Bonferroni correction based on the total number of SNPs is more appropriate, till an appropriate method is established to adjust for the number of independent tests.

Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics

“…It has been widely observed that RA and SLE have common pathological and clinical features (Manoussakis et al, 2004;Icen et al, 2009;Orozco et al, 2011;Toro-Domínguez et al, 2014;Márquez et al, 2017;Acosta-Herrera et al, 2019), which are partly attributable to common genetic foundation between the two diseases. However, the genetic overlap underlying RA and SLE remains elusive and a large proportion of genes related to them are yet not discovered (Ramos et al, 2011).…”

“…The objective of our study was to gain insight into genetic mechanism linking between RA and SLE using advanced bioinformatics approaches. By leveraging publicly available GWAS summary statistics, we identified a significant positive genetic correlation between RA and SLE, indicating that genetic variants associated with the risk of SLE would be also related to the risk of RA, or vice versa (Icen et al, 2009;Toro-Domínguez et al, 2014).…”

“…Because both the RA and SLE GWASs were analyzed with pooled samples of male and female individuals; therefore, to avoid the influence of gender heterogeneity in gene expressions on association signals ( Kassam et al, 2019 ; Lopes-Ramos et al, 2020 ; Oliva et al, 2020 ), we removed six gender-specific tissues in GTEx (i.e., breast mammary tissue, ovary, prostate, testis, uterus, and vagina), leaving 42 various gender-combined GTEx tissues for each disease ( Supplementary Table 2 ). Moreover, due to population stratification or cryptic relatedness or overcorrection of test statistics ( Cardon and Palmer, 2003 ; Freedman et al, 2004 ; van den Berg et al, 2019 ), the empirical null distribution in GWAS is sometimes inflated. To correct for such bias, following prior work ( Price et al, 2010 ), we divided by the genomic inflation factor (λ) if it was greater than 1.05.…”

Genome-Wide Association Studies for Methane Production in Dairy Cattle

“…The genomic inflation factor expresses the deviation of the distribution of the observed test statistic compared to the distribution of the expected test statistic. High genomic inflation factors are caused by population stratification, strong linkage disequilibrium (LD) between SNPs, strong association between SNPs and phenotypes, and systematic bias [32].…”

“…For a trait with a low heritability, the threshold value for significance of associating loci with a trait would have low −log10 (p-values) [33]. In published studies in pig populations, threshold values for −log10 (p-value) ranged from 3.3 to 6, using either no multiple testing correction, a Bonferroni correction, the false discovery rate, or genomic control [32].…”