Statistical Analysis in GWAS

Hasina Begum

Abstract: In the past decade, high-density SNP arrays and DNA re-sequencing have elucidated the majority of the genotypic space for a number of organisms, including maize, Arabidopsis and rice. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Association Studies (GWAS) present a powerful tool to reconnect this trait back to its basic genetics. In this review we discuss future perspectives of AM in plants, application in other emerging research area, potential useful of new cultivar development, and the biological and statistical considerations that emphasize a successful association analysis. The relevance of biological factors including sample size, genetic heterogeneity, genomic confounding, linkage mapping, linkage disequilibrium and spurious association, and statistical tools to account for these are presented. GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation.

Keywords: GWAS, Cereal Crop, Population Structure, Sample Size, Statistical Tools

How to Cite?: Hasina Begum, "Statistical Analysis in GWAS", Volume 12 Issue 12, December 2023, International Journal of Science and Research (IJSR), Pages: 1072-1078, https://www.ijsr.net/getabstract.php?paperid=SR231215085226, DOI: https://dx.doi.org/10.21275/SR231215085226

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