Bias in heritability estimates from genomic restricted maximum likelihood methods under different genotyping strategies.

A Cesarani, Ivan Pocrnic, NPP Macciotta, BO Fragomeni, I Misztal, DAL Lourenco

Research output: Contribution to journalArticlepeer-review

Abstract

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 on27 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.
Original languageEnglish
JournalJournal of Animal Breeding and Genetics
DOIs
Publication statusPublished - 13 Nov 2018

Keywords

  • genotyping scheme
  • restricted maximum likelihood
  • selective genotyping
  • single‐step genomic BLUP
  • variance component

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