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Prioritized candidate causal haplotype blocks in plant genome-wide association studies

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and...

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Autores principales: Wu, Xing, Jiang, Wei, Fragoso, Christopher, Huang, Jing, Zhou, Geyu, Zhao, Hongyu, Dellaporta, Stephen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612827/
https://www.ncbi.nlm.nih.gov/pubmed/36251695
http://dx.doi.org/10.1371/journal.pgen.1010437
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author Wu, Xing
Jiang, Wei
Fragoso, Christopher
Huang, Jing
Zhou, Geyu
Zhao, Hongyu
Dellaporta, Stephen
author_facet Wu, Xing
Jiang, Wei
Fragoso, Christopher
Huang, Jing
Zhou, Geyu
Zhao, Hongyu
Dellaporta, Stephen
author_sort Wu, Xing
collection PubMed
description Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA’s results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement.
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spelling pubmed-96128272022-10-28 Prioritized candidate causal haplotype blocks in plant genome-wide association studies Wu, Xing Jiang, Wei Fragoso, Christopher Huang, Jing Zhou, Geyu Zhao, Hongyu Dellaporta, Stephen PLoS Genet Research Article Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA’s results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement. Public Library of Science 2022-10-17 /pmc/articles/PMC9612827/ /pubmed/36251695 http://dx.doi.org/10.1371/journal.pgen.1010437 Text en © 2022 Wu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wu, Xing
Jiang, Wei
Fragoso, Christopher
Huang, Jing
Zhou, Geyu
Zhao, Hongyu
Dellaporta, Stephen
Prioritized candidate causal haplotype blocks in plant genome-wide association studies
title Prioritized candidate causal haplotype blocks in plant genome-wide association studies
title_full Prioritized candidate causal haplotype blocks in plant genome-wide association studies
title_fullStr Prioritized candidate causal haplotype blocks in plant genome-wide association studies
title_full_unstemmed Prioritized candidate causal haplotype blocks in plant genome-wide association studies
title_short Prioritized candidate causal haplotype blocks in plant genome-wide association studies
title_sort prioritized candidate causal haplotype blocks in plant genome-wide association studies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612827/
https://www.ncbi.nlm.nih.gov/pubmed/36251695
http://dx.doi.org/10.1371/journal.pgen.1010437
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