Association mapping for morphological and physiological traits in Populus simonii

Abstract

BackgroundTo optimize marker-assisted selection programs, knowledge of the genetic architecture of phenotypic traits is very important for breeders. Generally, most phenotypes, e.g. morphological and physiological traits, are quantitatively inherited, and thus detection of the genes underlying variation for these traits is difficult. Association mapping based on linkage disequilibrium has recently become a powerful approach to map genes or quantitative trait loci (QTL) in plants.ResultsIn this study, association analysis using 20 simple sequence repeat (SSR) markers was performed to detect the marker loci linked to 13 morphological traits and 10 physiological traits in a wild P. simonii population that consisted of 528 individuals sampled from 16 sites along the Yellow River in China. Based on a model controlling for both population structure (Q) and relative kinship (K), three SSR markers (GCPM_616-1 in 31.2 Mb on LG I, GCPM_4055-2 in 5.7 Mb on LG XV, and GCPM_3142 of unknown location) were identified for seven traits. GCPM_616-1 was associated with five morphological traits (R2 = 5.14-10.09%), whereas GCPM_3142 (15.03%) and GCPM_4055-2 (13.26%) were associated with one morphological trait and one physiological trait, respectively.ConclusionsThe results suggest that this wild population is suitable for association mapping and the identified markers will be suitable for marker-assisted selection breeding or detection of target genes or QTL in the near future.

Association mapping of leaf traits in spinach (Spinacia oleracea L.)

“…, Wei et al. ), which was supported by this study. Using the quantile–quantile (QQ) plots, the model which produces more significant results than expected by chance could be found for marker–trait association analysis (Bastien et al.…”

“…The GLM only accounts for population structure (Q-matrix), but the MLM accounts for both the kinship (K-matrix) and the population structure (Q-matrix) and allows one to overcome the issue of false positives in marker-trait association (Pritchard et al 2000, Zhu et al 2000, Price et al 2006, Yu et al 2006. The comparisons among different marker-trait association models for association mapping studies were reported by previous researchers, and MLM discovered less significant markers associated with traits of interest in comparison with GLM (Pritchard et al 2000, Evanno et al 2005, Price et al 2006, Yu et al 2006, Mandel et al 2013, Xu et al 2013, Wei et al 2014, which was supported by this study. Using the quantile-quantile (QQ) plots, the model which produces more significant results than expected by chance could be found for marker-trait association analysis (Bastien et al 2014).…”

“…Association mapping becomes the alternative strategy for conventional linkage-based mapping to study the genetic basis of the phenotypic variation in different plants (Goldstein and Weale 2001). To date, many plant association studies for different phenotypic traits have been reported, such as flowering time (Zhao et al 2007), leaf and plant architecture (Tian et al 2011, Wei et al 2014) and fruit quality (Xu et al 2013).…”

Genome‐Wide Association Analysis of Diverse Soybean Genotypes Reveals Novel Markers for Nitrogen Traits

“…Multiple testing was performed to assess the significance of marker trait associations using QVALUE R 3.1.0 (http://genomics.princeton.edu/storeylab/qvalue/windows.html) employing the smoother method (Storey and Tibshirani, 2003), an extension of the false discovery rate (FDR) method (Benjamini and Hochberg, 1995). Single nucleotide polymorphisms with a q FDR < 0.05 (Cheng et al, 2015; Wei et al, 2014) were considered significant. For the analyses by environment, we selected those SNPs that were identified in at least two of the four environments.…”

Genetic diversity, population structure, and association mapping of biomass traits in maize with simple sequence repeat markers

“…Association mapping exploits historical and evolutionary recombination at the population level. Association mapping, which is based on linkage disequilibrium (LD) studies statistical association between genetic markers and phenotype is widely used to identify quantitative trait loci (QTL) in crops (Wei et al 2014), partly because it has higher mapping resolution than linkage mapping. Other advantages are that many alleles can be evaluated simultaneously and it is possible to assess many recombination events, in numerous generations, and in natural populations without developing a mapping population Abdurakhmonov and Abdukarimov 2008;Gupta et al 2014).…”