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Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation
BACKGROUND: Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and herit...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235212/ https://www.ncbi.nlm.nih.gov/pubmed/35761182 http://dx.doi.org/10.1186/s12711-022-00737-3 |
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author | Ren, Duanyang Cai, Xiaodian Lin, Qing Ye, Haoqiang Teng, Jinyan Li, Jiaqi Ding, Xiangdong Zhang, Zhe |
author_facet | Ren, Duanyang Cai, Xiaodian Lin, Qing Ye, Haoqiang Teng, Jinyan Li, Jiaqi Ding, Xiangdong Zhang, Zhe |
author_sort | Ren, Duanyang |
collection | PubMed |
description | BACKGROUND: Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and heritability estimation. One possible reason is the uneven distribution of the linkage disequilibrium (LD) along the genome, i.e., LD heterogeneity among regions. The aim of this study was to effectively use genome-wide SNP data for genomic prediction and heritability estimation by using models that control LD heterogeneity among regions. METHODS: The LD-adjusted kinship (LDAK) and LD-stratified multicomponent (LDS) models were used to control LD heterogeneity among regions and were compared with the classical model that has no such control. Simulated and real traits of 2000 dairy cattle individuals with imputed high-density (770K) SNP data were used. Five types of phenotypes were simulated, which were controlled by very strongly, strongly, moderately, weakly and very weakly tagged causal variants, respectively. The performances of the models with high- and medium-density (50K) panels were compared to verify that the models that controlled LD heterogeneity among regions were more effective with high-density data. RESULTS: Compared to the medium-density panel, the use of the high-density panel did not improve and even decreased prediction accuracies and heritability estimates from the classical model for both simulated and real traits. Compared to the classical model, LDS effectively improved the accuracy of genomic predictions and unbiasedness of heritability estimates, regardless of the genetic architecture of the trait. LDAK applies only to traits that are mainly controlled by weakly tagged causal variants, but is still less effective than LDS for this type of trait. Compared with the classical model, LDS improved prediction accuracy by about 13% for simulated phenotypes and by 0.3 to ~ 10.7% for real traits with the high-density panel, and by ~ 1% for simulated phenotypes and by − 0.1 to ~ 6.9% for real traits with the medium-density panel. CONCLUSIONS: Grouping SNPs based on regional LD to construct the LD-stratified multicomponent model can effectively eliminate the adverse effects of LD heterogeneity among regions, and greatly improve the efficiency of high-density SNP data for genomic prediction and heritability estimation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-022-00737-3. |
format | Online Article Text |
id | pubmed-9235212 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-92352122022-06-28 Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation Ren, Duanyang Cai, Xiaodian Lin, Qing Ye, Haoqiang Teng, Jinyan Li, Jiaqi Ding, Xiangdong Zhang, Zhe Genet Sel Evol Research Article BACKGROUND: Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and heritability estimation. One possible reason is the uneven distribution of the linkage disequilibrium (LD) along the genome, i.e., LD heterogeneity among regions. The aim of this study was to effectively use genome-wide SNP data for genomic prediction and heritability estimation by using models that control LD heterogeneity among regions. METHODS: The LD-adjusted kinship (LDAK) and LD-stratified multicomponent (LDS) models were used to control LD heterogeneity among regions and were compared with the classical model that has no such control. Simulated and real traits of 2000 dairy cattle individuals with imputed high-density (770K) SNP data were used. Five types of phenotypes were simulated, which were controlled by very strongly, strongly, moderately, weakly and very weakly tagged causal variants, respectively. The performances of the models with high- and medium-density (50K) panels were compared to verify that the models that controlled LD heterogeneity among regions were more effective with high-density data. RESULTS: Compared to the medium-density panel, the use of the high-density panel did not improve and even decreased prediction accuracies and heritability estimates from the classical model for both simulated and real traits. Compared to the classical model, LDS effectively improved the accuracy of genomic predictions and unbiasedness of heritability estimates, regardless of the genetic architecture of the trait. LDAK applies only to traits that are mainly controlled by weakly tagged causal variants, but is still less effective than LDS for this type of trait. Compared with the classical model, LDS improved prediction accuracy by about 13% for simulated phenotypes and by 0.3 to ~ 10.7% for real traits with the high-density panel, and by ~ 1% for simulated phenotypes and by − 0.1 to ~ 6.9% for real traits with the medium-density panel. CONCLUSIONS: Grouping SNPs based on regional LD to construct the LD-stratified multicomponent model can effectively eliminate the adverse effects of LD heterogeneity among regions, and greatly improve the efficiency of high-density SNP data for genomic prediction and heritability estimation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-022-00737-3. BioMed Central 2022-06-27 /pmc/articles/PMC9235212/ /pubmed/35761182 http://dx.doi.org/10.1186/s12711-022-00737-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Ren, Duanyang Cai, Xiaodian Lin, Qing Ye, Haoqiang Teng, Jinyan Li, Jiaqi Ding, Xiangdong Zhang, Zhe Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation |
title | Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation |
title_full | Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation |
title_fullStr | Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation |
title_full_unstemmed | Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation |
title_short | Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation |
title_sort | impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235212/ https://www.ncbi.nlm.nih.gov/pubmed/35761182 http://dx.doi.org/10.1186/s12711-022-00737-3 |
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