Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects

Abstract Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is popular for addressing the challenge...

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Autores principales:	Zobaer Akond, Md. Asif Ahsan, Munirul Alam, Md. Nurul Haque Mollah
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0659ca023008466d995971d2618c2c86
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spelling	oai:doaj.org-article:0659ca023008466d995971d2618c2c862021-12-02T17:14:30ZRobustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects10.1038/s41598-021-90774-72045-2322https://doaj.org/article/0659ca023008466d995971d2618c2c862021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90774-7https://doaj.org/toc/2045-2322Abstract Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is popular for addressing the challenges of hidden population stratification and polygenic effects. However, most of these methods including LMM are sensitive to phenotypic outliers that may lead the misleading results. To overcome this problem, in this paper, we proposed a way to robustify the LMM approach for reducing the influence of outlying observations using the β-divergence method. The performance of the proposed method was investigated using both synthetic and real data analysis. Simulation results showed that the proposed method performs better than both linear regression model (LRM) and LMM approaches in terms of powers and false discovery rates in presence of phenotypic outliers. On the other hand, the proposed method performed almost similar to LMM approach but much better than LRM approach in absence of outliers. In the case of real data analysis, our proposed method identified 11 SNPs that are significantly associated with the rice flowering time. Among the identified candidate SNPs, some were involved in seed development and flowering time pathways, and some were connected with flower and other developmental processes. These identified candidate SNPs could assist rice breeding programs effectively. Thus, our findings highlighted the importance of robust GWAS in identifying candidate genes.Zobaer AkondMd. Asif AhsanMunirul AlamMd. Nurul Haque MollahNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zobaer Akond Md. Asif Ahsan Munirul Alam Md. Nurul Haque Mollah Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects
description	Abstract Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is popular for addressing the challenges of hidden population stratification and polygenic effects. However, most of these methods including LMM are sensitive to phenotypic outliers that may lead the misleading results. To overcome this problem, in this paper, we proposed a way to robustify the LMM approach for reducing the influence of outlying observations using the β-divergence method. The performance of the proposed method was investigated using both synthetic and real data analysis. Simulation results showed that the proposed method performs better than both linear regression model (LRM) and LMM approaches in terms of powers and false discovery rates in presence of phenotypic outliers. On the other hand, the proposed method performed almost similar to LMM approach but much better than LRM approach in absence of outliers. In the case of real data analysis, our proposed method identified 11 SNPs that are significantly associated with the rice flowering time. Among the identified candidate SNPs, some were involved in seed development and flowering time pathways, and some were connected with flower and other developmental processes. These identified candidate SNPs could assist rice breeding programs effectively. Thus, our findings highlighted the importance of robust GWAS in identifying candidate genes.
format	article
author	Zobaer Akond Md. Asif Ahsan Munirul Alam Md. Nurul Haque Mollah
author_facet	Zobaer Akond Md. Asif Ahsan Munirul Alam Md. Nurul Haque Mollah
author_sort	Zobaer Akond
title	Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects
title_short	Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects
title_full	Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects
title_fullStr	Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects
title_full_unstemmed	Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects
title_sort	robustification of gwas to explore effective snps addressing the challenges of hidden population stratification and polygenic effects
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0659ca023008466d995971d2618c2c86
work_keys_str_mv	AT zobaerakond robustificationofgwastoexploreeffectivesnpsaddressingthechallengesofhiddenpopulationstratificationandpolygeniceffects AT mdasifahsan robustificationofgwastoexploreeffectivesnpsaddressingthechallengesofhiddenpopulationstratificationandpolygeniceffects AT munirulalam robustificationofgwastoexploreeffectivesnpsaddressingthechallengesofhiddenpopulationstratificationandpolygeniceffects AT mdnurulhaquemollah robustificationofgwastoexploreeffectivesnpsaddressingthechallengesofhiddenpopulationstratificationandpolygeniceffects
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Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects

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