Bias in estimates of variance components in populations undergoing genomic selection: a simulation study

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Bias in estimates of variance components in populations undergoing genomic selection : a simulation study. / Gao, Hongding; Madsen, Per; Aamand, Gert Pedersen; Thomasen, Jørn Rind; Sørensen, Anders Christian; Jensen, Just.

I: BMC Genomics, Bind 20, Nr. 1, 956, 09.12.2019.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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@article{70ba3474d63d4c2fa724e376cdf98f2e,
title = "Bias in estimates of variance components in populations undergoing genomic selection: a simulation study",
abstract = "BACKGROUND: After the extensive implementation of genomic selection (GS), the choice of the statistical model and data used to estimate variance components (VCs) remains unclear. A primary concern is that VCs estimated from a traditional pedigree-based animal model (P-AM) will be biased due to ignoring the impact of GS. The objectives of this study were to examine the effects of GS on estimates of VC in the analysis of different sets of phenotypes and to investigate VC estimation using different methods. Data were simulated to resemble the Danish Jersey population. The simulation included three phases: (1) a historical phase; (2) 20 years of conventional breeding; and (3) 15 years of GS. The three scenarios based on different sets of phenotypes for VC estimation were as follows: (1) Pheno1: phenotypes from only the conventional phase (1-20 years); (2) Pheno1 + 2: phenotypes from both the conventional phase and GS phase (1-35 years); (3) Pheno2: phenotypes from only the GS phase (21-35 years). Single-step genomic BLUP (ssGBLUP), a single-step Bayesian regression model (ssBR), and P-AM were applied. Two base populations were defined: the first was the founder population referred to by the pedigree-based relationship (P-base); the second was the base population referred to by the current genotyped population (G-base).RESULTS: In general, both the ssGBLUP and ssBR models with all the phenotypic and genotypic information (Pheno1 + 2) yielded biased estimates of additive genetic variance compared to the P-base model. When the phenotypes from the conventional breeding phase were excluded (Pheno2), P-AM led to underestimation of the genetic variance of P-base. Compared to the VCs of G-base, when phenotypes from the conventional breeding phase (Pheno2) were ignored, the ssBR model yielded unbiased estimates of the total genetic variance and marker-based genetic variance, whereas the residual variance was overestimated.CONCLUSIONS: The results show that neither of the single-step models (ssGBLUP and ssBR) can precisely estimate the VCs for populations undergoing GS. Overall, the best solution for obtaining unbiased estimates of VCs is to use P-AM with phenotypes from the conventional phase or phenotypes from both the conventional and GS phases.",
author = "Hongding Gao and Per Madsen and Aamand, {Gert Pedersen} and Thomasen, {J{\o}rn Rind} and S{\o}rensen, {Anders Christian} and Just Jensen",
year = "2019",
month = dec,
day = "9",
doi = "10.1186/s12864-019-6323-8",
language = "English",
volume = "20",
journal = "B M C Genomics",
issn = "1471-2164",
publisher = "BioMed Central Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Bias in estimates of variance components in populations undergoing genomic selection

T2 - a simulation study

AU - Gao, Hongding

AU - Madsen, Per

AU - Aamand, Gert Pedersen

AU - Thomasen, Jørn Rind

AU - Sørensen, Anders Christian

AU - Jensen, Just

PY - 2019/12/9

Y1 - 2019/12/9

N2 - BACKGROUND: After the extensive implementation of genomic selection (GS), the choice of the statistical model and data used to estimate variance components (VCs) remains unclear. A primary concern is that VCs estimated from a traditional pedigree-based animal model (P-AM) will be biased due to ignoring the impact of GS. The objectives of this study were to examine the effects of GS on estimates of VC in the analysis of different sets of phenotypes and to investigate VC estimation using different methods. Data were simulated to resemble the Danish Jersey population. The simulation included three phases: (1) a historical phase; (2) 20 years of conventional breeding; and (3) 15 years of GS. The three scenarios based on different sets of phenotypes for VC estimation were as follows: (1) Pheno1: phenotypes from only the conventional phase (1-20 years); (2) Pheno1 + 2: phenotypes from both the conventional phase and GS phase (1-35 years); (3) Pheno2: phenotypes from only the GS phase (21-35 years). Single-step genomic BLUP (ssGBLUP), a single-step Bayesian regression model (ssBR), and P-AM were applied. Two base populations were defined: the first was the founder population referred to by the pedigree-based relationship (P-base); the second was the base population referred to by the current genotyped population (G-base).RESULTS: In general, both the ssGBLUP and ssBR models with all the phenotypic and genotypic information (Pheno1 + 2) yielded biased estimates of additive genetic variance compared to the P-base model. When the phenotypes from the conventional breeding phase were excluded (Pheno2), P-AM led to underestimation of the genetic variance of P-base. Compared to the VCs of G-base, when phenotypes from the conventional breeding phase (Pheno2) were ignored, the ssBR model yielded unbiased estimates of the total genetic variance and marker-based genetic variance, whereas the residual variance was overestimated.CONCLUSIONS: The results show that neither of the single-step models (ssGBLUP and ssBR) can precisely estimate the VCs for populations undergoing GS. Overall, the best solution for obtaining unbiased estimates of VCs is to use P-AM with phenotypes from the conventional phase or phenotypes from both the conventional and GS phases.

AB - BACKGROUND: After the extensive implementation of genomic selection (GS), the choice of the statistical model and data used to estimate variance components (VCs) remains unclear. A primary concern is that VCs estimated from a traditional pedigree-based animal model (P-AM) will be biased due to ignoring the impact of GS. The objectives of this study were to examine the effects of GS on estimates of VC in the analysis of different sets of phenotypes and to investigate VC estimation using different methods. Data were simulated to resemble the Danish Jersey population. The simulation included three phases: (1) a historical phase; (2) 20 years of conventional breeding; and (3) 15 years of GS. The three scenarios based on different sets of phenotypes for VC estimation were as follows: (1) Pheno1: phenotypes from only the conventional phase (1-20 years); (2) Pheno1 + 2: phenotypes from both the conventional phase and GS phase (1-35 years); (3) Pheno2: phenotypes from only the GS phase (21-35 years). Single-step genomic BLUP (ssGBLUP), a single-step Bayesian regression model (ssBR), and P-AM were applied. Two base populations were defined: the first was the founder population referred to by the pedigree-based relationship (P-base); the second was the base population referred to by the current genotyped population (G-base).RESULTS: In general, both the ssGBLUP and ssBR models with all the phenotypic and genotypic information (Pheno1 + 2) yielded biased estimates of additive genetic variance compared to the P-base model. When the phenotypes from the conventional breeding phase were excluded (Pheno2), P-AM led to underestimation of the genetic variance of P-base. Compared to the VCs of G-base, when phenotypes from the conventional breeding phase (Pheno2) were ignored, the ssBR model yielded unbiased estimates of the total genetic variance and marker-based genetic variance, whereas the residual variance was overestimated.CONCLUSIONS: The results show that neither of the single-step models (ssGBLUP and ssBR) can precisely estimate the VCs for populations undergoing GS. Overall, the best solution for obtaining unbiased estimates of VCs is to use P-AM with phenotypes from the conventional phase or phenotypes from both the conventional and GS phases.

U2 - 10.1186/s12864-019-6323-8

DO - 10.1186/s12864-019-6323-8

M3 - Journal article

C2 - 31818251

VL - 20

JO - B M C Genomics

JF - B M C Genomics

SN - 1471-2164

IS - 1

M1 - 956

ER -