Association mapping of stigma and spikelet characteristics in rice (Oryza sativa L.)

Abstract

Stigma and spikelet characteristics play an essential role in hybrid seed production. A mini-core of 90 accessions developed from USDA rice core collection was phenotyped in field grown for nine traits of stigma and spikelet and genotyped with 109 DNA markers, 108 SSRs plus an indel. Three major clusters were built upon Rogers’ genetic distance, indicative of indicas, and temperate and tropical japonicas. A mixed linear model combining PC-matrix and K-matrix was adapted for mapping marker-trait associations. Resulting associations were adjusted using false discovery rate technique. We identified 34 marker-trait associations involving 22 SSR markers for eight traits. Four markers were associated with single stigma exsertion (SStgE), six with dual exsertion (DStgE) and five with total exsertion. RM5_Chr1 played major role indicative of high regression with not only DStgE but also SStgE. Four markers were associated with spikelet length, three with width and seven with L/W ratio. Numerous markers were co-associated with multiple traits that were phenotypically correlated, i.e. RM12521_Chr2 associated with all three correlated spikelet traits. The co-association should improve breeding efficiency because single marker could be used to assist breeding for multiple traits. Indica entry 1032 (cultivar 50638) and japonica entry 671 (cultivar Linia 84 Icar) with 80.65 and 75.17% of TStgE, respectively are recommended to breeder for improving stigma exsertion.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-009-9290-y) contains supplementary material, which is available to authorized users.

Genetic diversity, population structure and association analysis in linseed (Linum usitatissimum L.)

“…In case of linseed different types of DNA based markers includingAFLP,RAPD,retrotransposon,ISSR,SSRs,SNPetc. havebeenusedforevaluatingthegenetic diversityandlinkage mapping (Cloutier et al 2010;Oh et al 2000;Spielmeyer et al 1998).OverothertypesofDNAmarkers,SSRsarewidelyused forgeneticdiversityandstructureanalysisinseveralcropplants (Cheng et al 2015;Yan et al 2009;Cane et al 2014;Cui et al 2013). In the present investigation, we applied 50 widely distributed SSRs to evaluate the genetic diversity and identify diverseaccessions/groupofaccessionsamongalargesetof168 accessionsoflinseed.Atotalof337allelesweredetectedfrom50 SSRswithanaverageof6.74allelesperSSRs.Thisaverageallele numberwasfoundtobehigherthanthatreportedearlier (RooseAmsalegetal.2006;Dengetal.2011;Palietal.2014;Soto-Cerda etal.2012Cloutieretal.2012b).Thehigherallelenumber mightbeduetothelargeanddiversesetofaccessionsandwidely distributed SSRs used in the present study.…”

“…Association mapping is widely being utilized for detecting marker-trait association based on the genetic resources routinely used for genetic improvement programs (Font I Forcada et al 2015;Cheng et al 2015;Yan et al 2009;Cai et al 2013). As compared to biparental QTL mapping, association mapping increases the range of natural variation that can be exploited in a single experiment and potential genomic regions could be identified (Jin et al 2010).…”

GS3 participates in stigma exsertion as well as seed length in rice

“…There have been many genetic studies of the frequency of stigma exsertion (Miyata et al 2007, Sidharthan et al 2007, Uga et al 2003a, 2003b, Virmani 1994, Virmani and Athwal 1973, Yan et al 2009). Here, we Superscript letters indicate statistically significant (P < 0.001) differences among the mean values within each column (one-way ANOVA).…”

Association analysis of yield and fiber quality traits in Gossypium barbadense with SSRs and SRAPs

“…Some studies have used principal component analysis (PCA) results to divide groups (Yan et al, 2009;Wen et al, 2012), while others rely on the mean log probability of the data [LnP(D)] obtained from the STRUCTURE software (Abdurakhmonov et al, 2008;Yao et al, 2009). The most commonly used models for analyzing the association are one-way analysis of variance (ANOVA), multiple linear regressions, general linear models (GLM), and mixed linear models (MLM) (Grapes et al, 2006;Wu et al, 2007;Kantartzi and Stewart, 2008;Abdurakhmonov et al, 2008Abdurakhmonov et al, , 2009.…”