Abstract
BackgroundWhole-genome sequencing and imputation methodologies have enabled the study of the effects of genomic variants with low to very low minor allele frequency (MAF) on variation in complex traits. Our objective was to estimate the proportion of variance explained by imputed sequence variants classified according to their MAF compared with the variance explained by the pedigree-based additive genetic relationship matrix for 17 traits in Nordic Holstein dairy cattle.ResultsImputed sequence variants were grouped into seven classes according to their MAF (0.001–0.01, 0.01–0.05, 0.05–0.1, 0.1–0.2, 0.2–0.3, 0.3–0.4 and 0.4–0.5). The total contribution of all imputed sequence variants to variance in deregressed estimated breeding values or proofs (DRP) for different traits ranged from 0.41 [standard error (SE) = 0.026] for temperament to 0.87 (SE = 0.011) for milk yield. The contribution of rare variants (MAF < 0.01) to the total DRP variance explained by all imputed sequence variants was relatively small (a maximum of 12.5% for the health index). Rare and low-frequency variants (MAF < 0.05) contributed a larger proportion of the explained DRP variances (>13%) for health-related traits than for production traits (<11%). However, a substantial proportion of these variance estimates across different MAF classes had large SE, especially when the variance explained by a MAF class was small. The proportion of DRP variance that was explained by all imputed whole-genome sequence variants improved slightly compared with variance explained by the 50 k Illumina markers, which are routinely used in bovine genomic prediction. However, the proportion of DRP variance explained by imputed sequence variants was lower than that explained by pedigree relationships, ranging from 1.5% for milk yield to 37.9% for the health index.ConclusionsImputed sequence variants explained more of the variance in DRP than the 50 k markers for most traits, but explained less variance than that captured by pedigree-based relationships. Although in humans partitioning variants into groups based on MAF and linkage disequilibrium was used to estimate heritability without bias, many of our bovine estimates had a high SE. For a reliable estimate of the explained DRP variance for different MAF classes, larger sample sizes are needed.
Highlights
Whole-genome sequencing and imputation methodologies have enabled the study of the effects of genomic variants with low to very low minor allele frequency (MAF) on variation in complex traits
The objectives of this study were to: (1) estimate the proportion of variance explained by whole-genome sequence variants for 17 traits in Nordic Holstein cattle; (2) estimate the proportion of variance explained by partitioning variants according to MAF, and with or without taking linkage disequilibrium (LD) heterogeneity into consideration; and (3) compare estimates of the proportions of genetic variance explained by relationships based on pedigree, 50 k single nucleotide polymorphisms (SNP), and imputed whole-genome sequence variants
Contribution of different classes of genetic variants based on MAF to deregressed estimated breeding values or proofs (DRP) variance Additional file 1: Table S1 shows the proportion of DRP variance explained and standard error (SE) for variants partitioned into seven MAF groups for 17 traits and Additional file 2: Table S2 presents the same for variants partitioned into seven MAF groups and four LD groups for 17 traits
Summary
Whole-genome sequencing and imputation methodologies have enabled the study of the effects of genomic variants with low to very low minor allele frequency (MAF) on variation in complex traits. Previous studies showed that a wide gap remains between the proportion of variance explained using genomic relationships constructed from 50 k SNP chips and the genetic variance explained by pedigree-based relationships [7, 8, 10, 11]. This “missing” proportion of the genetic variance may affect the maximum accuracy that genomic prediction could achieve in livestock breeding [12]. By using imputed sequence data, rare and low-frequency variants can be identified and studied for much larger numbers of individuals
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