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Bias in heritability estimates from genomic restricted maximum likelihood methods under different genotyping strategies
Author(s) -
Cesarani Alberto,
Pocrnic Ivan,
Macciotta Nicolò P. P.,
Fragomeni Breno O.,
Misztal Ignacy,
Lourenco Daniela A. L.
Publication year - 2019
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12367
Subject(s) - heritability , restricted maximum likelihood , genotyping , biology , selection (genetic algorithm) , trait , population , quantitative trait locus , genetics , snp genotyping , statistics , genotype , maximum likelihood , mathematics , gene , demography , computer science , artificial intelligence , sociology , programming language
We investigated the effects of different strategies for genotyping populations on variance components and heritabilities estimated with an animal model under restricted maximum likelihood (REML), genomic REML (GREML), and single‐step GREML (ssGREML). A population with 10 generations was simulated. Animals from the last one, two or three generations were genotyped with 45,116 SNP evenly distributed on 27 chromosomes. Animals to be genotyped were chosen randomly or based on EBV. Each scenario was replicated five times. A single trait was simulated with three heritability levels (low, moderate, high). Phenotypes were simulated for only females to mimic dairy sheep and also for both sexes to mimic meat sheep. Variance component estimates from genomic data and phenotypes for one or two generations were more biased than from three generations. Estimates in the scenario without selection were the most accurate across heritability levels and methods. When selection was present in the simulations, the best option was to use genotypes of randomly selected animals. For selective genotyping, heritabilities from GREML were more biased compared to those estimated by ssGREML, because ssGREML was less affected by selective or limited genotyping.