RT Journal Article
SR Electronic
T1 A heuristic method to identify runs of homozygosity associated with reduced performance in livestock
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 131706
DO 10.1101/131706
A1 J.T. Howard
A1 F. Tiezzi
A1 Y. Huang
A1 K.A. Gray
A1 C. Maltecca
YR 2017
UL http://biorxiv.org/content/early/2017/04/27/131706.abstract
AB While for the most part genome-wide metrics are currently employed in managing livestock inbreeding, genomic data offer, in principle, the ability to identify functional inbreeding. Here we present a heuristic method to identify haplotypes contained within a run of homozygosity (ROH) associated with reduced performance. Results are presented for simulated and swine data. The algorithm comprises 3 steps. Step 1 scans the genome based on marker windows of decreasing size and identifies ROH genotypes associated with an unfavorable phenotype. Within this stage, multiple aggregation steps reduce the haplotype to the smallest possible length. In step 2, the resulting regions are formally tested for significance with the use of a linear mixed model. Lastly, step 3 removes nested windows. The effect of the unfavorable haplotypes identified and their associated haplotype probabilities for a progeny of a given mating pair or an individual can be used to generate an inbreeding load matrix (ILM). Diagonals of ILM characterize the functional inbreeding load of individual (IIL). We estimated the accuracy of predicting the phenotype based on ILL. We further compared the significance of the regression coefficient for IIL on phenotypes to genome-wide inbreeding metrics. We tested the algorithm using simulated scenarios (n =12) combining different levels of linkage disequilibrium (LD) and number of loci impacting a quantitative trait. Additionally, we investigated 9 traits from two maternal purebred swine lines. In simulated data, as the LD in the population increased the algorithm identified a greater proportion of the true unfavorable ROH effects. For example, the proportion of highly unfavorable true ROH effects identified raised from 32 to 41 % for the low to the high LD scenario. In both simulated and real data the haplotypes identified were contained within a much larger ROH (9.12-12.1 Mb). The IIL prediction accuracy was greater than zero across all scenarios for simulated data (high LD scenario mean (95% confidence interval): 0.49 (0.47-0.52)) and for nearly all swine traits (mean ± SD: 0.17±0.10). On average across simulated and swine datasets the IIL regression coefficient was more closely related to progeny performance than any genome-wide inbreeding metric. A heuristic method was developed that identified ROH genotypes with reduced performance and characterized the combined effects of ROH genotypes within and across individuals.