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 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.

Genetic Ancestry Estimates within Dutch Family Units and Across Genotyping Arrays: Insights from Empirical Analysis Using Two Estimation Methods

“…For many years, PCA has been widely used in genetic association studies to address population stratification confounding. However, recent scrutiny has raised concerns about potential biases associated with this technique in population genetic research [46] and GWASs [47]. In this study, we employed a projection-based PCA approach, and our results highlight the utility of this method.…”

Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS

“…In a robust GWAS analysis, to overcome the problems of outlier observations, the linear mixed model approach was strengthened using the β-divergence method. This method performed better than the linear regression and mixed model approaches in the presence of outlier data and identified new SNPs that can be used in breeding programs 11 . The combination of GWAS and t-tests help identify significant SNPs 85 , confirming CAPS markers 86 , and identifying favorable SNP alleles 87 .…”

“…This multivariate method, in addition to studies of phenotypic and molecular diversity 2 , 3 , has been used in confirming population structure 4 , 5 , genotype selection 6 , understanding the pattern of genotype-by-environment interactions 7 , 8 , and selection of traits for yield modeling 9 . Despite its widespread use, the results of this technique can be highly biased in population genetic research 10 and GWAS 11 , which have been cited as potential pitfalls 12 .…”

“…Statistical methods are the core of GWAS 36 . Although several of these methods have been developed for GWAS, most of them are sensitive to phenotypic outliers, making their results unreliable 11 . Population structure is used as a covariate in GWAS to avoid false positive rates.…”

Genomic prediction of growth in a commercially, recreationally, and culturally important marine resource, the Australian snapper (Chrysophrys auratus)

“…This is additional evidence for the very complex genomic architecture of growth, which is composed by a mixture of several loci with small effects detected by the haplotype approaches on chromosome 6, and a few loci with larger effects detected by single-SNP approaches, especially on chromosome 22. Thus, it is important to combine conceptually different approaches to extract information from all markers, to gain a better understanding of the genetic components of phenotypic variation ( de Maturana et al 2014 ; Akond et al 2021 ; Gong et al 2021 ).…”