Maize association population: a high‐resolution platform for quantitative trait locus dissection

Flint-García, Sherry; Thuillet, Anne-Céline; Yu, Jianming; Pressoir, Gaël; Romero, Susan; Mitchell, Sharon E.; Doebley, John; Kresovich, Stephen; Goodman, M. M.; Buckler, Edward S.

doi:10.1111/j.1365-313x.2005.02591.x

Cited by 805 publications

(775 citation statements)

References 34 publications

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“…Population structure analysis is also important for association mapping as it is essential to define the population stratification within the accessions to avoid any false association (Flint-Garcia et al 2005). The current global prospect and health related properties of linseed have dictated the need for the evaluation of its genetic diversity through molecular markers and exploitation by marker assisted breeding (Pali et al 2015(Pali et al , 2014Chandrawati et al 2014;Rajwade et al 2010;Kale et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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

Chandrawati

Singh

Kumar³

et al. 2017

Physiol Mol Biol Plants

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The present investigation aimed to explore the level of genetic diversity, determine the population structure in a larger set of germplasm of linseed using microsatellite marker and identify linked markers through association mapping. A total of 168 accessions of linseed were evaluated for major agro-economic traits and SSRs markers deployed for diversity assessment. A total of 337 alleles were amplified by 50 SSRs ranging from 2 to 13 with an average of 6.74 ± 2.8 alleles per loci. The neighbor joining based clustering grouped all the accessions into three major clusters that were also confirmed by scatter plot of PCoA. While model based clustering determined four sub-populations (K = 4). Further, analysis of molecular variance analysis considering three population showed that maximum variation (79%) was within the population. We identified one putative SSR marker (Lu_3043) linked with days to 50% flowering through both GLM and MLM analysis of association mapping. The results of this preliminary study revealed genetic diversity, population structure in linseed and linked marker which could be utilized in future breeding program.

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Section: Introductionmentioning

confidence: 99%

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

Chandrawati

Singh

Kumar³

et al. 2017

Physiol Mol Biol Plants

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“…Maize exhibits substantial natural variation in leaf number, with a total number of leaves ranging from seven to 19 leaves in a panel with diverse maize inbred lines (Flint‐Garcia et al ., 2005). However, few studies have dissected the genetic basis of natural variation in maize leaf number (Vlăduţu et al ., 1999; Wang et al ., 2008).…”

Section: Introductionmentioning

confidence: 99%

The genetic architecture of leaf number and its genetic relationship to flowering time in maize

Wang

Zhang

et al. 2015

New Phytologist

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Summary The number of leaves and their distributions on plants are critical factors determining plant architecture in maize (Zea mays), and leaf number is frequently used as a measure of flowering time, a trait that is key to local environmental adaptation.Here, using a large set of 866 maize‐teosinte BC 2S3 recombinant inbred lines genotyped by using 19 838 single nucleotide polymorphism markers, we conducted a comprehensive genetic dissection to assess the genetic architecture of leaf number and its genetic relationship to flowering time.We demonstrated that the two components of total leaf number, the number of leaves above (LA) and below (LB) the primary ear, were under relatively independent genetic control and might be subject to differential directional selection during maize domestication and improvement. Furthermore, we revealed that flowering time and leaf number are commonly regulated at a moderate level. The pleiotropy of the genes ZCN8, dlf1 and ZmCCT on leaf number and flowering time were validated by near‐isogenic line analysis. Through fine mapping, qLA1‐1, a major‐effect locus that specifically affects LA, was delimited to a region with severe recombination suppression derived from teosinte.This study provides important insights into the genetic basis of traits affecting plant architecture and adaptation. The genetic independence of LA from LB enables the optimization of leaf number for ideal plant architecture breeding in maize.

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“…Association studies on diversity panels are effective for identifying genetic variants associated with phenological and morphological traits in maize (Thornsberry et al, 2001;Flint-Garcia et al, 2005;CamusKulandaivelu et al, 2006;Ducrocq et al, 2008Ducrocq et al, , 2009Durand et al, 2012;Romay et al, 2013;Bouchet et al, 2013). In the current study, 375 lines of maize originating from the tropics, USA and Europe were successfully phenotyped for 24 ecologically and agronomically important traits related to (i) phenology, (ii) plant architecture, (iii) ear and yield components and (iv) tassel architecture.…”

Section: Introductionmentioning

confidence: 99%

Association mapping for phenology and plant architecture in maize shows higher power for developmental traits compared with growth influenced traits

et al. 2016

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Plant architecture, phenology and yield components of cultivated plants have repeatedly been shaped by selection to meet human needs and adaptation to different environments. Here we assessed the genetic architecture of 24 correlated maize traits that interact during plant cycle. Overall, 336 lines were phenotyped in a network of 9 trials and genotyped with 50K singlenucleotide polymorphisms. Phenology was the main factor of differentiation between genetic groups. Then yield components distinguished dents from lower yielding genetic groups. However, most of trait variation occurred within group and we observed similar overall and within group correlations, suggesting a major effect of pleiotropy and/or linkage. We found 34 quantitative trait loci (QTLs) for individual traits and six for trait combinations corresponding to PCA coordinates. Among them, only five were pleiotropic. We found a cluster of QTLs in a 5 Mb region around Tb1 associated with tiller number, ear row number and the first PCA axis, the latter being positively correlated to flowering time and negatively correlated to yield. Kn1 and ZmNIP1 were candidate genes for tillering, ZCN8 for leaf number and Rubisco Activase 1 for kernel weight. Experimental repeatabilities, numbers of QTLs and proportion of explained variation were higher for traits related to plant development such as tillering, leaf number and flowering time, than for traits affected by growth such as yield components. This suggests a simpler genetic determinism with larger individual QTL effects for the first category. Although technology to support this process has dramatically evolved, especially in recent decades, breeders' objectives have not fundamentally changed over the last 10 000 years. They still have to overcome the difficulty of conducting multiple trait selection in different and variable environments and their choices are often trade-offs between an ideal plant architecture, product quality, yield and adaptation to targeted environments. Low cost genotyping can accelerate the selection of large effect beneficial alleles and high throughput genotyping can improve the accuracy of breeding value predictions for multi-traits controlled by small effect quantitative trait loci (QTLs).In cereals, yield remains the main objective even though it may be negatively correlated with some quality traits. Yield is a complex trait to predict as it is the result of many trait interactions during plant development and growth. The objective is to combine plant architecture that maximizes light interception, phenology that synchronizes reproduction stage with optimal environmental conditions to minimize abortion, inflorescence architecture for optimal seed set, and numerous factors contributing to environmental adaptation and grain filling.

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Maize association population: a high‐resolution platform for quantitative trait locus dissection

Cited by 805 publications

References 34 publications

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

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

The genetic architecture of leaf number and its genetic relationship to flowering time in maize

Association mapping for phenology and plant architecture in maize shows higher power for developmental traits compared with growth influenced traits

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