Incorporation of causative quantitative trait nucleotides in single-step GBLUP

Abstract

Background Much effort is put into identifying causative quantitative trait nucleotides (QTN) in animal breeding, empowered by the availability of dense single nucleotide polymorphism (SNP) information. Genomic selection using traditional SNP information is easily implemented for any number of genotyped individuals using single-step genomic best linear unbiased predictor (ssGBLUP) with the algorithm for proven and young (APY). Our aim was to investigate whether ssGBLUP is useful for genomic prediction when some or all QTN are known.MethodsSimulations included 180,000 animals across 11 generations. Phenotypes were available for all animals in generations 6 to 10. Genotypes for 60,000 SNPs across 10 chromosomes were available for 29,000 individuals. The genetic variance was fully accounted for by 100 or 1000 biallelic QTN. Raw genomic relationship matrices (GRM) were computed from (a) unweighted SNPs, (b) unweighted SNPs and causative QTN, (c) SNPs and causative QTN weighted with results obtained with genome-wide association studies, (d) unweighted SNPs and causative QTN with simulated weights, (e) only unweighted causative QTN, (f–h) as in (b–d) but using only the top 10% causative QTN, and (i) using only causative QTN with simulated weight. Predictions were computed by pedigree-based BLUP (PBLUP) and ssGBLUP. Raw GRM were blended with 1 or 5% of the numerator relationship matrix, or 1% of the identity matrix. Inverses of GRM were obtained directly or with APY.ResultsAccuracy of breeding values for 5000 genotyped animals in the last generation with PBLUP was 0.32, and for ssGBLUP it increased to 0.49 with an unweighted GRM, 0.53 after adding unweighted QTN, 0.63 when QTN weights were estimated, and 0.89 when QTN weights were based on true effects known from the simulation. When the GRM was constructed from causative QTN only, accuracy was 0.95 and 0.99 with blending at 5 and 1%, respectively. Accuracies simulating 1000 QTN were generally lower, with a similar trend. Accuracies using the APY inverse were equal or higher than those with a regular inverse.ConclusionsSingle-step GBLUP can account for causative QTN via a weighted GRM. Accuracy gains are maximum when variances of causative QTN are known and blending is at 1%.

Mammalian genomic regulatory regions predicted by utilizing human genomics, transcriptomics and epigenetics data

“…Active TSS regions from 88 human tissues 6 in the ROADMAP were mapped to 81,892 putative promoters in cattle [21], with a total length of 135.6 Mb. Noticeably, the average number of Ensembl reference TSSs overlapped to every 1 Mb of predicted promoters based on the ROADMAP database was 37-fold lower 9 than those based on the CAGE database ( Table 2). HPRS using the CAGE dataset can predict many TSSs at single-nucleotide resolution and can accurately predict transcriptional orientation.…”

“…Since the coverage of the reference cattle liver enhancer dataset was not significantly 9 higher with human liver enhancers, than with enhancers from many of the other human ROADMAP tissue enhancer datasets [21], we asked whether combining tissues would increase coverage. By combining the predictions from the 42 ROADMAP datasets, 2 to 4- 1 2 fold higher coverage could be obtained than from one tissue alone (at least 60% total coverage) across a variety of species could be obtained, with coverage lowest for rat and highest for macaque ( Fig.…”

“…Another example of applying the HPRS databases for analysis of non-coding 9 mutations is for the case of the "Celtic mutation", which causes the polled phenotype. The mutation is a 202-bp-indel, where the duplication of a 212 bp region (chr1:1705834-1706045) replaces the 10 bp (chr1:1706051-1706060) [46,47] [48] (Fig.…”

“…9 select suitable human databases using the following suggested criteria: types of regulatory regions (promoters, enhancers, and TFBSs), biochemical assays, computational models for combining data, and data sources (tissues, cell lines, traits). Second, by applying the UCSC4 2 liftOver tool[23], regions that were aligned at genome-scale (by LastZ pair-wise genome…”

Controlling Coancestry and Thereby Future Inbreeding by Optimum-Contribution Selection Using Alternative Genomic-Relationship Matrices

“…Moreover, genomic relationships that incorporate QTL information realize higher accuracies of genomic breeding values (GEBVs) than genomic relationships constructed based on markers only (Nejati-Javaremi et al, 1997;Zhang et al, 2010). In case of few QTL (100) and many records, accuracies of GEBVs close to one have been achieved (Fragomeni et al, 2017). Although, adding QTL information in the construction of genomic relationship matrices improved accuracies of prediction, there is no full understanding on the interaction of the use of alternative genomic relationship matrices in genomic optimum contribution selection (GOCS) schemes.…”