36 Exploring the Statistical Nature of Independent Chromosome Segments. (21st September 2022) - Hollifield, Mary Kate; Bermann, Matias; Lourenco, Daniela; Misztal, Ignacy
Abstract: Independent chromosome segments for a population with Ne effective population size and L genome length can be approximately defined as 4Ne L non-overlapping haplotypes of L/2 length derived from any Ne animals. The number of independent chromosome segments (Me ) can be approximated as 4Ne L. The genetic selection with a genomic relationship matrix (GRM) using such haplotypes approaches that with a GRM using the SNP markers. The objective of this study was to investigate the statistical nature of independent chromosome segments. Data were simulated using QMSim and contained a population of ten non-overlapping generations, each including 2, 000 animals with Ne equal to 20, and a polygenic trait with a heritability of 0.6. The last three generations were genotyped, and each genome contained ten 1 M long chromosomes, for a total genome length of 10 M and 50, 000 SNP. Chromosome segments of each animal were organized in non-overlapping haplotypes by the SNP code using in-house software written in Fortran. The effects of the hypothetical independent chromosome segments were estimated using a model that included an overall mean plus the segment effects. To analyze the behavior around Me, the number of segments chosen to estimate segment effects varied around 4Ne L. Accuracies were calculated for animals in the last generation by cor (TBV, Zs ŝ), where Zs ŝ is a vector of breeding values based on segments and TBV is a vector of the true breeding values outputted by QMSim. Accuracies of segment effects were compared with the true accuracy, i.e., cor (TBV, GEBV). The maximum accuracy of segment effects was 0.84, and the true accuracy was 0.96. The results suggest that 4Ne L segments contain most of the additive information in the population. However, the accuracy of GBLUP is greater than that of the chromosome segment effects, suggesting that arranging chromosome segments based solely on the statistical nature is not enough to account for all the genetic variation.