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Genomewide Selection when Major Genes Are Known
Author(s) -
Bernardo Rex
Publication year - 2014
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2013.05.0315
Subject(s) - biology , gene , selection (genetic algorithm) , genetics , heritability , random effects model , fixed effects model , major gene , computational biology , statistics , mathematics , meta analysis , computer science , medicine , artificial intelligence , panel data
Current methods for genomewide selection do not distinguish between known major genes and random genomewide markers. My objectives were to determine if explicitly modeling the effects of known major genes affects the response to genomewide selection, and to identify situations in which considering major genes as having fixed effects is helpful. Simulation experiments showed that having a fixed effect for a major gene became more advantageous as the percentage of genetic variance ( V G ) explained by a major gene ( R 2 ) increased and as the heritability on an entry‐mean basis ( h 2 ) increased. With R 2 = 50% and h 2 = 0.80, the relative efficiency (based on selection gains in Cycle 4) with a major gene having a fixed versus random effect was 112–121%. Specifying a fixed effect for a single major gene was never disadvantageous except with R 2 < 10%. With h 2 ≥ 0.50, specifying a fixed versus random effect for a single major gene had little effect on prediction accuracy in Cycle 0. However, prediction accuracy in later cycles declined more rapidly when a major gene had a random effect instead of a fixed effect. The results with L = 2 or 3 major genes were similar to those with one major gene. In contrast, the usefulness of gene information was low with L = 10 major genes. Overall, major genes should be fitted as having fixed effects in genomewide selection when only a few major genes are present and each major gene accounts for ≥10% of V G .

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