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

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

Scientific Reports ◽

10.1038/srep10298 ◽

2015 ◽

Vol 5 (1) ◽

Cited By ~ 24

Author(s):

Fu-Tao Zhang ◽

Zhi-Hong Zhu ◽

Xiao-Ran Tong ◽

Zhi-Xiang Zhu ◽

Ting Qi ◽

...

Keyword(s):

Association Mapping ◽

Linear Model ◽

Complex Traits ◽

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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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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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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Genome-wide association study of total number born and number born alive in pigsusing both compressed mixed linear model and Bayes model

Hereditas (Beijing) ◽

10.3724/sp.j.1005.2012.01261 ◽

2012 ◽

Vol 34 (10) ◽

pp. 1261-1270

Author(s):

Xiao-Lei LIU ◽

Song-Bai YANG ◽

F Rothschild Max ◽

Zhi-Wu ZHANG ◽

Bin FAN

Keyword(s):

Association Study ◽

Linear Model ◽

Genome Wide Association Study ◽

Mixed Linear Model ◽

Genome Wide Association ◽

Bayes Model ◽

Genome Wide

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Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches

Theoretical and Applied Genetics ◽

10.1007/s001220051331 ◽

1999 ◽

Vol 99 (7-8) ◽

pp. 1255-1264 ◽

Cited By ~ 397

Author(s):

D. L. Wang ◽

J. Zhu ◽

Z. K. L. Li ◽

A. H. Paterson

Keyword(s):

Linear Model ◽

Mixed Linear Model ◽

Epistatic Effects

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Closed-Form Approximations to the REML Estimator of a Variance Ratio (or Heritability) in a Mixed Linear Model

Biometrics ◽

10.1111/j.0006-341x.2001.01148.x ◽

2001 ◽

Vol 57 (4) ◽

pp. 1148-1156 ◽

Cited By ~ 9

Author(s):

Brent D. Burch ◽

Ian R. Harris

Keyword(s):

Closed Form ◽

Linear Model ◽

Mixed Linear Model ◽

Variance Ratio ◽

Reml Estimator

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Detecting dominance QTL in poultry pedigrees using variance component methodology

Proceedings of the British Society of Animal Science ◽

10.1017/s1752756200019050 ◽

2007 ◽

Vol 2007 ◽

pp. 2-2

Author(s):

S. J. Rowe ◽

R. Pong-Wong ◽

C.S. Haley ◽

S.A. Knott ◽

D.J. de Koning

Keyword(s):

Linear Model ◽

Variance Component ◽

Mixed Linear Model ◽

Variance Component Analysis ◽

Order Effects ◽

Empirical Power ◽

Commercial Broiler ◽

Underlying Mechanisms ◽

Higher Order Effects ◽

Qtl Effects

Methods that detect QTL within commercial populations circumvent the need for expensive experimental populations and facilitate direct application of results through marker assisted selection. Variance component analysis (VCA) uses phenotypic, pedigree and marker information within a mixed linear model to simultaneously detect QTL and estimate breeding values. The inclusion of non-additive effects has potential for greater accuracy of selection and understanding of underlying mechanisms. The linear model can be extended to include higher order effects such as dominance, however, there is little information on empirical power. Here VCA was applied to real and simulated commercial broiler data to detect additive and dominant QTL effects.

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