Association Analysis of SSR Markers with Phenology, Grain, and Stover-Yield Related Traits in Pearl Millet (Pennisetum glaucum(L.) R. Br.)

Abstract

Pearl millet is a staple food crop for millions of people living in the arid and semi-arid tropics. Molecular markers have been used to identify genomic regions linked to traits of interest by conventional QTL mapping and association analysis. Phenotypic recurrent selection is known to increase frequencies of favorable alleles and decrease those unfavorable for the traits under selection. This study was undertaken (i) to quantify the response to recurrent selection for phenotypic traits during breeding of the pearl millet open-pollinated cultivar “CO (Cu) 9” and its four immediate progenitor populations and (ii) to assess the ability of simple sequence repeat (SSR) marker alleles to identify genomic regions linked to grain and stover yield-related traits in these populations by association analysis. A total of 159 SSR alleles were detected across 34 selected single-copy SSR loci. SSR marker data revealed presence of subpopulations. Association analysis identified genomic regions associated with flowering time located on linkage group (LG) 6 and plant height on LG4, LG6, and LG7. Marker alleles on LG6 were associated with stover yield, and those on LG7 were associated with grain yield. Findings of this study would give an opportunity to develop marker-assisted recurrent selection (MARS) or marker-assisted population improvement (MAPI) strategies to increase the rate of gain for pearl millet populations undergoing recurrent selection.

Response of locally adapted pearl millet populations to s1 progeny recurrent selection for grain yield and resistance to rust

“…The variation in response to selection indicates a possibility to improve the traits through phenotypic S 1 progeny recurrent selection (Bidinger et al, 2006). This observation was also reported by Kannan et al (2014) in their study to quantify response to recurrent selection for grain yield and related traits using SSR markers. They noted that the possibility was due to pearl millet being a highly cross-pollinating crop, with a high level of genetic variability.…”

Molecular characterization of pearl millet [Pennisetum glaucum (L.) R. Br] inbreds using microsatellite markers

“…It is a highly cross-pollinated crop with protogynous flowering nature which fulfils one of the essential biological requirements for hybrid development. Pearl millet is one of the major cereals grown, primarily for grain production, on more than 29 m ha in the arid and semi-arid tropical regions of Africa and Asia (Kannan et al, 2014). India is the largest producer of pearl millet in Asia and occupied an area of 7.95 m ha with production of 8.79 mt and productivity 1106 kg/ha (Anonymous, 2014).…”

Association analysis of low-phosphorus tolerance in West African pearl millet using DArT markers

“…In that case, the random-mated base population and its four sequentially derived populations were each based on a recombination of at least 50 full-sib progenies and hence expected to be in nearly complete linkage disequilibrium. As subsequently demonstrated (Baskaran et al 2014), this means that use of codominant markers with highly heterozygous full-sib progeny sets developed from a truly randommating population can be very effective for detection of marker-trait associations-even without taking into account population structure, as population structure accounts for less than 1 % of the observed genetic variance in such cases. This in turn means that markerassisted population improvement (MAPI) using fullsib progenies can be expected to be very efficientand directly applicable for improvement of highly heterozygous open-pollinated varieties of seed-propagated crops or clonally propagated crops.…”

“…They had suggested that the bottom 0.1 percentile distribution of the p values is considered as significant, which in our analysis resulted in threshold levels of -log(p values) C 2 for individual traits. Based on this and the studies by Hao et al (2012) and Baskaran et al (2014), we passed significant association at -log p [ 2.00, p \ 0.01 threshold as a first step. This resulted in several significant associations for each trait.…”

“…The colocation of QTLs for different traits implies the likely presence of pleiotropy or tight linkage between the QTLs that control the trait (Lebreton et al 1995;Gemenet et al 2010;Baskaran et al 2014). In our case, marker PgPb7101 had significant association with PCS, PBM and PUE.…”