Genome Wide Single Locus Single Trait, Multi-Locus and Multi-Trait Association Mapping for Some Important Agronomic Traits in Common Wheat (T. aestivum L.)

Abstract

Genome wide association study (GWAS) was conducted for 14 agronomic traits in wheat following widely used single locus single trait (SLST) approach, and two recent approaches viz. multi locus mixed model (MLMM), and multi-trait mixed model (MTMM). Association panel consisted of 230 diverse Indian bread wheat cultivars (released during 1910–2006 for commercial cultivation in different agro-climatic regions in India). Three years phenotypic data for 14 traits and genotyping data for 250 SSR markers (distributed across all the 21 wheat chromosomes) was utilized for GWAS. Using SLST, as many as 213 MTAs (p ≤ 0.05, 129 SSRs) were identified for 14 traits, however, only 10 MTAs (~9%; 10 out of 123 MTAs) qualified FDR criteria; these MTAs did not show any linkage drag. Interestingly, these genomic regions were coincident with the genomic regions that were already known to harbor QTLs for same or related agronomic traits. Using MLMM and MTMM, many more QTLs and markers were identified; 22 MTAs (19 QTLs, 21 markers) using MLMM, and 58 MTAs (29 QTLs, 40 markers) using MTMM were identified. In addition, 63 epistatic QTLs were also identified for 13 of the 14 traits, flag leaf length (FLL) being the only exception. Clearly, the power of association mapping improved due to MLMM and MTMM analyses. The epistatic interactions detected during the present study also provided better insight into genetic architecture of the 14 traits that were examined during the present study. Following eight wheat genotypes carried desirable alleles of QTLs for one or more traits, WH542, NI345, NI170, Sharbati Sonora, A90, HW1085, HYB11, and DWR39 (Pragati). These genotypes and the markers associated with important QTLs for major traits can be used in wheat improvement programs either using marker-assisted recurrent selection (MARS) or pseudo-backcrossing method.

Estimation of additive and epistatic gene effects of doubled haploid lines of winter oilseed rape (Brassica napus L.)

“…Confounding epistatic effects in models suggested that inheritance of these traits is complex and polygenic. Epistasis has been reported for many traits in a number of crops: barley ; maize (Li et al 2016); rice (Matsubara et al 2015), and wheat (Jaiswal et al 2016).…”

Genotype by environment interaction using AMMI model and estimation of additive and epistasis gene effects for 1000-kernel weight in spring barley (Hordeum vulgare L.)

“…Epistasis effects involved in the models proved that genetic of these traits is complex and polygenic (Lander and Schork 1994 ; Lefebvre and Palloix 1996 ; Hermisson et al 2003 ; Crow 2010 ). Epistasis has been demonstrated for numerous features in a number of cultivars of following species: barley (Kularia and Sharma 2005 ; Bocianowski et al 2016 ), corn (Melchinger et al 1987 ; Li et al 2016 ), sugar beet (Abbasi et al 2015 ), rice (Matsubara et al 2015 ), oilseed rape (Bocianowski et al 2017 ), common wheat (Bnejdi and El Gazzah 2008 ; Jaiswal et al 2016 ), and sorghum (Finkner et al 1981 ).…”

Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using GWAS

“…In the past ten years, several GWAS approaches have been used to identify signi cant SNPs, especially evaluating agronomic traits in common wheat (T. aestivum L.) traits using a single locus and two multi-locus approaches [75]. Conclusively, V Jaiswal, V Gahlaut, PK Meher, RR Mir, JP Jaiswal, AR Rao, HS Balyan and PK Gupta [75] veri ed that ML-GWAS has more detection power than SL-GWAS by revealing ten Marker Trait Associations (MTAs) through SL-GWAS while, 22 MTAs through multi locus mixed model (MLMM) and 58 MTAs through multi-trait mixed model (MTMM). A more recent study of Ward and his co-researchers utilized a conventional mixed linear model and recently developed FarmCPU approach to dissect the genetic architecture of yield related traits in winter wheat.…”