Mixed Linear Model Approaches of Association Mapping for Complex Traits Based on Omics Variants

Cotton productivity has been hindered by the narrow genetic base of cultivated cotton. Linkage disequilibrium-based association mapping has become a powerful molecular tool to dissect and exploit genetic diversity. In the present study, population structure and marker-trait associations for fibre quality traits in genotypes belonging to six races of Gossypium arboreum were assessed. Out of 300 simple sequence repeat (SSR) markers, 100 were found polymorphic, yielding a total of 240 alleles (all polymorphic). Structure analysis revealed allelic admixtures between genotypes. A Q-matrix exhibited mixed ancestry for the majority of genotypes, the race indicum forming a significant percent ancestry for almost all genotypes. At significant threshold values of r2 ≥ 0.05, 7.37% of SSR loci showed significant linkage disequilibrium (LD), while at highly significant threshold of r2 ≥ 0.1, the value was reduced to 5.31%. LD clearly decayed within the genetic distance of 9–10 cM, with r2 ≥ 0.1. Twenty-eight SSR markers were found associated with six fibre quality traits using general linear model and mixed linear model.

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Population genetic structure and association mapping for iron toxicity tolerance in rice

PLoS ONE ◽

10.1371/journal.pone.0246232 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0246232

Author(s):

S. Pawar ◽

E. Pandit ◽

I. C. Mohanty ◽

D. Saha ◽

S. K. Pradhan

Keyword(s):

Genetic Variation ◽

Genetic Structure ◽

Association Mapping ◽

Stress Tolerance ◽

Linear Model ◽

Generalized Linear Model ◽

Mixed Linear Model ◽

Iron Toxicity ◽

Rice Grain ◽

Fe Toxicity

Iron (Fe) toxicity is a major abiotic stress which severely reduces rice yield in many countries of the world. Genetic variation for this stress tolerance exists in rice germplasms. Mapping of gene(s)/QTL controlling the stress tolerance and transfer of the traits into high yielding rice varieties are essential for improvement against the stress. A panel population of 119 genotypes from 352 germplasm lines was constituted for detecting the candidate gene(s)/QTL through association mapping. STRUCTURE, GenAlEx and Darwin softwares were used to classify the population. The marker-trait association was detected by considering both the Generalized Linear Model (GLM) and Mixed Linear Model (MLM) analyses. Wide genetic variation was observed among the genotypes present in the panel population for the stress tolerance. Linkage disequilibrium was detected in the population for iron toxicity tolerance. The population was categorized into three genetic structure groups. Marker-trait association study considering both the Generalized Linear Model (GLM) and Mixed Linear Model (MLM) showed significant association of leaf browning index (LBI) with markers RM471, RM3, RM590 and RM243. Three novel QTL controlling Fe-toxicity tolerance were detected and designated as qFeTox4.3, qFeTox6.1 and qFeTox10.1. A QTL reported earlier in the marker interval of C955-C885 on chromosome 1 is validated using this panel population. The present study showed that QTL controlling Fe-toxicity tolerance to be co-localized with the QTL for Fe-biofortification of rice grain indicating involvement of common pathway for Fe toxicity tolerance and Fe content in rice grain. Fe-toxicity tolerance QTL qFeTox6.1 was co-localized with grain Fe-biofortification QTLs qFe6.1 and qFe6.2 on chromosome 6, whereas qFeTox10.1 was co-localized with qFe10.1 on chromosome 10. The Fe-toxicity tolerance QTL detected from this mapping study will be useful in marker-assisted breeding programs.

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Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data

BioMed Research International ◽

10.1155/2015/135782 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Haiming Xu ◽

Beibei Jiang ◽

Yujie Cao ◽

Yingxin Zhang ◽

Xiaodeng Zhan ◽

...

Keyword(s):

Linear Model ◽

Complex Traits ◽

Genetic Architecture ◽

Mixed Linear Model ◽

Processing Unit ◽

Environment Interaction ◽

Quality Traits ◽

Gene Environment ◽

Genome Wide ◽

Model Approach

With development of sequencing technology, dense single nucleotide polymorphisms (SNPs) have been available, enabling uncovering genetic architecture of complex traits by genome-wide association study (GWAS). However, the current GWAS strategy usually ignores epistatic and gene-environment interactions due to absence of appropriate methodology and heavy computational burden. This study proposed a new GWAS strategy by combining the graphics processing unit- (GPU-) based generalized multifactor dimensionality reduction (GMDR) algorithm with mixed linear model approach. The reliability and efficiency of the analytical methods were verified through Monte Carlo simulations, suggesting that a population size of nearly 150 recombinant inbred lines (RILs) had a reasonable resolution for the scenarios considered. Further, a GWAS was conducted with the above two-step strategy to investigate the additive, epistatic, and gene-environment associations between 701,867 SNPs and three important quality traits, gelatinization temperature, amylose content, and gel consistency, in a RIL population with 138 individuals derived from super-hybrid rice Xieyou9308 in two environments. Four significant SNPs were identified with additive, epistatic, and gene-environment interaction effects. Our study showed that the mixed linear model approach combining with the GPU-based GMDR algorithm is a feasible strategy for implementing GWAS to uncover genetic architecture of crop complex traits.

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Mixed linear model association mapping for low chloride accumulation rate in oriental-type tobacco (Nicotiana tabaccum L.) germplasm

Journal of Plant Interactions ◽

10.1080/17429145.2014.893453 ◽

2014 ◽

Vol 9 (1) ◽

pp. 666-672 ◽

Cited By ~ 10

Author(s):

Ashkan Basirnia ◽

Hamid Hatami Maleki ◽

Reza Darvishzadeh ◽

Farhad Ghavami

Keyword(s):

Association Mapping ◽

Linear Model ◽

Accumulation Rate ◽

Mixed Linear Model ◽

Nicotiana Tabaccum ◽

Chloride Accumulation

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Association mapping of genomic loci linked with Fusarium wilt resistance (Foc2) in chickpea

Plant Genetic Resources ◽

10.1017/s1479262121000228 ◽

2021 ◽

pp. 1-8

Author(s):

Uday Chand Jha ◽

Rintu Jha ◽

Abhishek Bohra ◽

Lakshmaiah Manjunatha ◽

Parasappa Rajappa Saabale ◽

...

Keyword(s):

Association Mapping ◽

Linear Model ◽

Fusarium Wilt ◽

Generalized Linear Model ◽

Mixed Linear Model ◽

Diversity Analysis ◽

Breeding Lines ◽

Genetic Diversity Analysis ◽

Simple Sequence ◽

Model Approach

Abstract Improving plant resistance against Fusarium wilt (FW) is key to sustaining chickpea production worldwide. Given this, the current study tested a set of 75 FW-responsive chickpea breeding lines including checks in a wilt-sick plot for two consecutive years (2016 and 2017). Genetic diversity analysis using 75 simple sequence repeats (SSRs) revealed a total of 267 alleles with an average of 3.56 alleles per marker. The entire set was divided into two major classes based on clustering method and factorial analysis. Similarly, STRUCTURE analysis placed the 75 genotypes into three distinct sub-groups (K = 3). Marker-trait association (MTA) analysis using the generalized linear model approach revealed nine and eight significant MTAs for FW resistance in the years 2016 and 2017, respectively. Three significant MTAs were obtained for FW resistance following the mixed linear model approach for both years. The SSR markers CESSR433, NCPGR21 and ICCM0284 could be potentially employed for targeted and accelerated improvement of FW resistance in chickpea. To the best of our knowledge, this is the first report on association mapping of the genomic loci controlling FW (Foc2) resistance in chickpea.

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Optimizing the identification of causal variants across varying genetic architectures in crops

10.1101/310391 ◽

2018 ◽

Cited By ~ 1

Author(s):

Chenyong Miao ◽

Jinliang Yang ◽

James C. Schnable

Keyword(s):

Linear Model ◽

Complex Traits ◽

Statistical Power ◽

Linear Models ◽

Association Studies ◽

Mixed Linear Model ◽

Frequency Distributions ◽

Model Based ◽

A Genome ◽

The Impact

AbstractBackgroundAssociation studies use statistical links between genetic markers and variation in a phenotype’s value across many individuals to identify genes controlling variation in the target phenotype. However, this approach, particularly conducted on a genome-wide scale (GWAS), has limited power to identify the genes responsible for variation in traits controlled by complex genetic architectures.ResultsHere we employ simulation studies utilizing real-world genotype datasets from association populations in four species with distinct minor allele frequency distributions, population structures, and patterns linkage disequilibrium to evaluate the impact of variation in both heritability and trait complexity on both conventional mixed linear model based GWAS and two new approaches specifically developed for complex traits. Mixed linear model based GWAS rapidly losses power for more complex traits. FarmCPU, a method based on multi-locus mixed linear models, provides the greatest statistical power for moderately complex traits. A Bayesian approach adopted from genomic prediction provides the greatest statistical power to identify causal genetic loci for extremely complex traits.ConclusionsUsing estimates of the complexity of the genetic architecture of target traits can guide the selection of appropriate statistical methods and improve the overall accuracy and power of GWAS.

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Association mapping and favourable allele exploration for plant architecture traits in upland cotton (Gossypium hirsutum L.) accessions

The Journal of Agricultural Science ◽

10.1017/s0021859615000428 ◽

2015 ◽

Vol 154 (4) ◽

pp. 567-583 ◽

Cited By ~ 14

Author(s):

C. Q. LI ◽

N. J. AI ◽

Y. J. ZHU ◽

Y. Q. WANG ◽

X. D. CHEN ◽

...

Keyword(s):

Gossypium Hirsutum ◽

Association Mapping ◽

Ssr Markers ◽

Complex Traits ◽

Upland Cotton ◽

Plant Architecture ◽

Final Analysis ◽

Mixed Linear Model ◽

Mapping Panel ◽

Breeding Programmes

SUMMARYAssociation mapping based on linkage disequilibrium (LD) is a promising tool to identify genes responsible for quantitative variations underlying complex traits. The present paper presents an association mapping panel consisting of 172 upland cotton (Gossypium hirsutum L.) accessions. The panel was phenotyped for five cotton plant architecture traits across multiple environments and genotyped using 386 simple sequence repeat (SSR) markers. Of these markers, 101 polymorphic SSR markers were used in the final analysis. There were abundant phenotypic variations within this germplasm panel and a total of 267 alleles ranging from two to seven per locus were identified in all collections. The threshold of LD decay was set to r2 = 0·1 and 0·2, and the genome-wide LD extended up to about 13–14 and 6–7 cM, respectively, providing the potential for association mapping of agronomically important traits in upland cotton. A total of 66 marker–trait associations were detected based on a mixed linear model, of which 35 were found in more than one environment. The favourable alleles from 35 marker loci can be used in marker-assisted selection of target traits. Both the synergistic alleles and the negative alleles for some traits, especially plant height and fruit branch angle, can be utilized in plant architecture breeding programmes according to specific breeding objectives.

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Association Mapping for Drought Tolerance and Yield-Related Traits in Cowpea Accessions

10.20944/preprints202010.0001.v1 ◽

2020 ◽

Author(s):

Gabriel Vusanimuzi Nkomo ◽

Moosa Sedibe ◽

Maletsema Alina Mofokeng ◽

Rian Pierneef

Keyword(s):

Drought Tolerance ◽

Association Mapping ◽

Linear Model ◽

Genotyping By Sequencing ◽

Mixed Linear Model ◽

Snp Markers ◽

P Value ◽

Nucleotide Polymorphisms ◽

Single Marker ◽

Peak Of Delta

The objective of this study were to conduct association mapping for drought tolerance at the seedling stage and yield-related traits. 60 cowpea accessions were used in the study. Single-nucleotide polymorphisms (SNPs) discovered through genotyping by sequencing (GBS) were used for genotyping. Association mapping was conducted using single-marker regression (SMR) in Q Gene, and general linear model (GLM) and mixed linear model (MLM) built in TASSEL. The population of the cowpea accessions were analysed using STRUCTURE 2.3.4 and the peak of delta K in the greenhouse showed seven population types, whereas the peak of delta K in the glasshouse indicated the presence of six population types. One SNP marker, 14083649|F|0-9 was associated with NP with a p value <0.001. Fifty SNP markers were associated with PWT at p <0.001. Four SNP markers, 14074781|F|0-16, 100047392|F|0-36, 14083801|F|0-28 and 100051488|F|0-49 were associated with AVSPD at p <0.001. SNP markers, 14074781|F|0-16, 14083801|F|0-28 and 100051488|F|0-49 were associated with PL at P <0.001. Five SNP markers, 100047392|F|0-36, 14083801|F|0-28, 100072738|F|0-34, 14076881|F|0-49 and 14076881|F|0-49 were associated with PWDTH at p <0.001. The 65 SNP markers identified can be used in cowpea molecular breeding to select for AVSPD, NP, PL, PWDTH, PWT, and RR through marker assisted selection (MAS).

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