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.
Highlights
The significance thresholds found with permutation testing for the medium‐ density single nucleotide polymorphism (SNP) chip were 4.1 and increased to 5.5 when the marker density increased to imputed WGS (iWGS)
For iWGS, the number of QTL regions was equal to 36 with a significance threshold of 8.3, to 264 with a signification threshold of 5.4 and to 977 with a significance threshold of 3.6
Even though using the number of independent chromosome fragments to represent the linkage disequilibrium (LD) structure of the data better, the significance thresholds did not increase with increasing SNP density and the thresholds were quite low compared to those ones established van den BERG et al FIGURE 7 Number of QTL regions with a significance level above a range of significance thresholds for medium density, high density and iWGS with permutation testing, for example, 5.4 with permutation testing and 3.4 with the Bonferroni correction for iWGS
Summary
The data set for a Large White (LW) line was provided by Topigs Norsvin. The data set included precorrected phenotypes for number of teats of 4,964 Large White (LW line) pigs (Lopes et al, 2017), medium‐density genotypes (34,588 SNPs) and high‐density genotypes (491,169 SNPs). The random polygenetic effect followed a normal distribution u ~ N(0,Gσg2), where G is the genomic relationship matrix (Yang, Manolio, et al, 2011) for which the chromosome of the SNP tested is ignored and σg is the genetic variance. Three approaches to establish significance thresholds were evaluated: (a) permutation testing, (b) the Bonferroni correction and (c) the FDR. Using either medium‐density genotypes, high‐density genotypes or iWGS, the permutation test was performed for only 3 chromosomes to limit the computational van den BERG et al. With α = 0.05, and m being the total number of SNPs. Third, the p‐value at rank k was defined as the threshold and all SNPs with a rank smaller than k are declared significant. Both false discovery rates were estimated using the R package “mutoss” (Blanchard et al., 2010)
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