Genome-wide association analysis to identify chromosomal regions determining components of earliness in wheat

Gouis, Jacques Le; Bordes, Jacques; Ravel, C.; Heumez, Emmanuel; Faure, Sébastien; Praud, Sébastien; Galic, Nathalie; Remoué, Carine; Balfourier, François; Allard, Vincent; Rousset, M.

doi:10.1007/s00122-011-1732-3

Cited by 90 publications

(92 citation statements)

References 77 publications

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“…Within the 57 SNPs under selection, the highest P-value corresponded to Ppd-D1, which is a known candidate gene involved in photoperiod sensitivity (Beales et al 2007) and a key earliness QTL in many genetic studies (Hanocq et al 2007;Le Gouis et al 2012). Using association tests on the evolved population, we confirmed the strong impact of this QTL on flowering time.…”

Section: Discussionsupporting

confidence: 53%

“…This area could match with the location of Vrn2B, a known gene involved in an integrative pathway of vernalization requirement and photoperiod sensitivity but not located in the available map. Three areas, one on chromosome 4A and two on chromosome 5A, could also match previously detected QTL for heading date (Hanocq et al 2007;Le Gouis et al 2012). Finally one additional area on chromosome 5A did not correspond to any reported QTL.…”

Section: Discussionmentioning

confidence: 49%

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Efficiently Tracking Selection in a Multiparental Population: The Case of Earliness in Wheat

Thepot

Restoux

Goldringer³

et al. 2014

Genetics

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Multiparental populations are innovative tools for fine mapping large numbers of loci. Here we explored the application of a wheat Multiparent Advanced Generation Inter-Cross (MAGIC) population for QTL mapping. This population was created by 12 generations of free recombination among 60 founder lines, following modification of the mating system from strict selfing to strict outcrossing using the ms1b nuclear male sterility gene. Available parents and a subset of 380 SSD lines of the resulting MAGIC population were phenotyped for earliness and genotyped with the 9K i-Select SNP array and additional markers in candidate genes controlling heading date. We demonstrated that 12 generations of strict outcrossing rapidly and drastically reduced linkage disequilibrium to very low levels even at short map distances and also greatly reduced the population structure exhibited among the parents. We developed a Bayesian method, based on allelic frequency, to estimate the contribution of each parent in the evolved population. To detect loci under selection and estimate selective pressure, we also developed a new method comparing shifts in allelic frequency between the initial and the evolved populations due to both selection and genetic drift with expectations under drift only. This evolutionary approach allowed us to identify 26 genomic areas under selection. Using association tests between flowering time and polymorphisms, 6 of these genomic areas appeared to carry flowering time QTL, 1 of which corresponds to Ppd-D1, a major gene involved in the photoperiod sensitivity. Frequency shifts at 4 of 6 areas were consistent with earlier flowering of the evolved population relative to the initial population. The use of this new outcrossing wheat population, mixing numerous initial parental lines through multiple generations of panmixia, is discussed in terms of power to detect genes under selection and association mapping. Furthermore we provide new statistical methods for use in future analyses of multiparental populations.KEYWORDS QTL detection; parental contribution; recombinant population; selection detection; experimental evolution; multiparental populations; Multiparent Advanced Generation Inter-Cross (MAGIC); MPPA LTHOUGH the recent development of genome sequencing in crop species has strongly increased capacity for gene discovery, the accurate mapping of genes controlling the genetic variation of complex traits (QTL) remains a key objective. The principal mapping methods rely on the detection of statistical association between polymorphisms at molecular markers and quantitative variation of phenotypic traits and were historically developed for progenies of biparental crosses with parents chosen for their extreme phenotypes at a trait of interest. These populations are powerful resources for QTL detection, but have low precision as few effective meioses (and thus subsequent recombinations) occur during their development. As a result, large parental haplotype blocks are preserved in the segregating population. In a...

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Section: Discussionsupporting

confidence: 53%

Section: Discussionmentioning

confidence: 49%

Efficiently Tracking Selection in a Multiparental Population: The Case of Earliness in Wheat

Thepot

Restoux

Goldringer³

et al. 2014

Genetics

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“…In the last decade, AM has been used for mapping various qualitative and quantitative traits in plants, namely, flowering time in maize , disease resistance in rice (Garris et al 2003;Agrama and Eizenga 2008;Zhao et al 2011), iron deficiency chlorosis and protein content in soybean (Charlson et al 2003(Charlson et al , 2005Wang et al 2008), maysin and chlorogenic acid content in corn (Szalma et al 2005;Sun et al 2010), yield traits in barley (Kraakman et al 2004) and earliness in wheat (Gouis et al 2012). It has been identified as a tool to resolve complex trait variations down to the sequence level (Nordborg and Tavare 2002), and to identify novel mutations causing specific phenotypes (Palaisa et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Population structure and association mapping studies for important agronomic traits in soybean

Kumar

Talukdar

Bala

et al. 2014

J Genet

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The present study was carried out with a set of 96 diverse soybean genotypes with the objectives of analysing the population structure and to identify molecular markers associated with important agronomic traits. Large phenotypic variability was observed for the agronomic traits under study indicating suitability of the genotypes for association studies. The maximum values for plant height, pods per plant, seeds per pod, 100-seed weight and seed yield per plant were approximately two and half to three times more than the minimum values for the genotypes. Seed yield per plant was found to be significantly correlated with pods per plant (r = 0.77), 100-seed weight (r = 0.35) and days to maturity (r = 0.23). The population structure studies depicted the presence of seven subpopulations which nearly corresponded with the source of geographical origin of the genotypes. Linkage disequilibrium (LD) between the linked markers decreased with the increased distance, and a substantial drop in LD decay values was observed between 30 and 35 cM. Genomewide marker-traits association analysis carried out using general linear (GLM) and mixed linear models (MLM) identified six genomic regions (two of them were common in both) on chromosomes 6, 7, 8, 13, 15 and 17, which were found to be significantly associated with various important traits viz., plant height, pods per plant, 100-seed weight, plant growth habit, average number of seeds per pod, days to 50% flowering and days to maturity. The phenotypic variation explained by these loci ranged from 6.09 to 13.18% and 4.25 to 9.01% in the GLM and MLM studies, respectively. In conclusion, association mapping (AM) in soybean could be a viable alternative to conventional QTL mapping approach.

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“…Nevertheless, this approach has been used in various plant species over the past two decades for tagging and cloning of gene (s) (Price 2006;Holland 2007). The last decade has witnessed increasing application of the association mapping approach in mapping of various qualitative and quantitative traits in plants viz., flowering time in maize (Thornsberry et al 2001), disease resistance in rice (Garris et al 2003;Agrama and Eizenga 2008;Zhao et al 2011) iron deficiency chlorosis and protein content in soybean (Charlson et al 2003(Charlson et al , 2005Wang et al 2008), maysin and chlorogenic acid content in corn (Szalma et al 2005;Sun et al 2010), yield traits in barley (Kraakman et al 2004) and earliness in wheat (Gouis et al 2012). It has been identified as the tool having strength to resolve complex trait variation to the sequence level by exploiting historical and evolutionary recombination events at the population level (Nordborg and Tavare 2002).…”

Section: Introductionmentioning

confidence: 98%

Mapping of yellow mosaic virus (YMV) resistance in soybean (Glycine max L. Merr.) through association mapping approach

et al. 2014

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Yellow Mosaic Virus (YMV) is a serious disease of soybean. Resistance to YMV was mapped in 180 soybean genotypes through association mapping approach using 121 simple sequence repeats (SSR) and four resistance gene analogue (RGA)-based markers. The association mapping population (AMP) (96 genotypes) and confirmation population (CP) (84 genotypes) was tested for resistance to YMV at hot-spot consecutively for 3 years (2007-2009). The genotypes exhibited significant variability for YMV resistance (P < 0.01). Molecular genotyping and population structure analysis with 'admixture' co-ancestry model detected seven optimal sub-populations in the AMP. Linkage disequilibrium (LD) between the markers extended up to 35 and 10 cM with r2 > 0.15, and >0.25, respectively. The 4 RGA-based markers showed no association with YMV resistance. Two SSR markers, Satt301 and GMHSP179 on chromosome 17 were found to be in significant LD with YMV resistance. Contingency Chi-square test confirmed the association (P < 0.01) and the utility of the markers was validated in the CP. It would pave the way for marker assisted selection for YMV resistance in soybean. This is the first report of its kind in soybean.

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Genome-wide association analysis to identify chromosomal regions determining components of earliness in wheat

Cited by 90 publications

References 77 publications

Efficiently Tracking Selection in a Multiparental Population: The Case of Earliness in Wheat

Efficiently Tracking Selection in a Multiparental Population: The Case of Earliness in Wheat

Population structure and association mapping studies for important agronomic traits in soybean

Mapping of yellow mosaic virus (YMV) resistance in soybean (Glycine max L. Merr.) through association mapping approach

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