mrMLM v4.0: An R Platform for Multi-locus Genome-wide Association Studies

Zhang, Yawen; Tamba, Cox Lwaka; Wen, Yang; Li, Pei; Ren, Wenlong; Ni, Yuanli; Gao, Jun; Zhang, Yuanming

doi:10.1101/2020.03.04.976464

Cited by 14 publications

(25 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To validate and increase the accuracy of the multi-locus GWAS results, we used mrMLM 4.0 ( Zhang et al, 2020 ) and FarmCPU ( Liu et al, 2016 ). The six multi-locus GWAS methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO) from mrMLM 4.0 R package were used.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, six multi-locus GWAS approaches were integrated into an R package, named mrMLM ( Zhang et al, 2020 ). The mrMLM 4.0 R package comprises the mrMLM ( Wang et al, 2016 ), FASTmrMLM ( Tamba and Zhang, 2018 ), FASTmrEMMA ( Wen et al, 2017 ), ISIS EM-BLASSO ( Tamba et al, 2017 ), pLARmEB ( Zhang et al, 2017 ), and pKWmEB ( Ren et al, 2018 ) two-step multi-locus GWAS methods.…”

Section: Introductionmentioning

confidence: 99%

“…First, various algorithms are used to select all potentially associated markers. Second, these selected markers are put in one model, in which all the effects are obtained by empirical Bayes, and all the non-zero effects are further identified by likelihood ratio test for true Quantitative Trait Nucleotides (QTNs) ( Zhang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Locus Genome-Wide Association Studies Reveal Fruit Quality Hotspots in Peach Genome

Linge

Cai

et al. 2021

Front. Plant Sci.

View full text Add to dashboard Cite

Peach is one of the most important fruit crops in the world, with the global annual production about 24.6 million tons. The United States is the fourth-largest producer after China, Spain, and Italy. Peach consumption has decreased over the last decade, most likely due to inconsistent quality of the fruit on the market. Thus, marker-assisted selection for fruit quality traits is highly desired in fresh market peach breeding programs and one of the major goals of the RosBREED project. The ability to use DNA information to select for desirable traits would enable peach breeders to efficiently plan crosses and select seedlings with desired quality traits early in the selection process before fruiting. Therefore, we assembled a multi-locus genome wide association study (GWAS) of 620 individuals from three public fresh market peach breeding programs (Arkansas, Texas, and South Carolina). The material was genotyped using 9K SNP array and the traits were phenotyped for three phenological (bloom date, ripening date, and days after bloom) and 11 fruit quality-related traits (blush, fruit diameter, fruit weight, adherence, fruit firmness, redness around pit, fruit texture, pit weight, soluble solid concentration, titratable acidity, and pH) over three seasons (2010, 2011, and 2012). Multi-locus association analyses, carried out using mrMLM 4.0 and FarmCPU R packages, revealed a total of 967 and 180 quantitative trait nucleotides (QTNs), respectively. Among the 88 consistently reliable QTNs detected using multiple multi-locus GWAS methods and/or at least two seasons, 44 were detected for the first time. Fruit quality hotspots were identified on chromosomes 1, 3, 4, 5, 6, and 8. Out of 566 candidate genes detected in the genomic regions harboring the QTN clusters, 435 were functionally annotated. Gene enrichment analyses revealed 68 different gene ontology (GO) terms associated with fruit quality traits. Data reported here advance our understanding of genetic mechanisms underlying important fruit quality traits and further support the development of DNA tools for breeding.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Locus Genome-Wide Association Studies Reveal Fruit Quality Hotspots in Peach Genome

Linge

Cai

et al. 2021

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Two different GWAS methodologies were considered: single-locus GWAS (SL-GWAS) based on MLM [ 26 , 27 ] and multi-locus GWAS (ML-GWAS) based on six models: mrMLM, FASTmrMLM, FASTmrEMMA, ISIS EM-BLASSO, pKWmEB, and pLARmEB [28−33]. SL-GWAS was conducted in Tassel v 5.2 [ 38 ], while ML-GWAS was performed using the “mrMLM” package [ 42 ] in R. To account for the multiple levels of relatedness within the lines included in the panel, population structure and kinship matrix were considered. Population structure was measured by Principal Component Analysis (PCA) conducted on the genotypic data set, considering two principal components according to the results of Campa et al [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

Genome-Wide Association Study (GWAS) for Resistance to Sclerotinia sclerotiorum in Common Bean

Čampa

García-Fernández

Fernández

2020

Genes

View full text Add to dashboard Cite

White mold (WM) is a devastating fungal disease affecting common bean (Phaseolus vulgaris L.). In this research, a genome-wide association study (GWAS) for WM resistance was conducted using 294 lines of the Spanish diversity panel. One single-locus method and six multi-locus methods were used in the GWAS. Response to this fungus showed a continuous distribution, and 28 lines were identified as potential resistance sources, including lines of Andean and Mesoamerican origin, as well as intermediate lines between the two gene pools. Twenty-two significant associations were identified, which were organized into 15 quantitative trait intervals (QTIs) located on chromosomes Pv01, Pv02, Pv03, Pv04, Pv08, and Pv09. Seven of these QTIs were identified for the first time, whereas eight corresponded to chromosome regions previously identified in the WM resistance. In all, 468 genes were annotated in these regions, 61 of which were proposed potential candidate genes for WM resistance, based on their function related to the three main defense stages on the host: recognition (22), signal transduction (8), and defense response (31). Results obtained from this work will contribute to a better understanding of the complex quantitative resistance to WM in common bean and reveal information of significance for future breeding programs.

show abstract

“…With the availability of computational facilities and robust sequencing technologies, the importance of genomics for the identification of genes and pathways is undisputed. Notably, genome-wide scanning approaches and digital candidate gene identification approaches, such as QTL, LD mapping, GWAS, GBS, and functional annotation, either alone or in combination with traditional candidate gene approaches are now frequently used (Mackay and Powell, 2007;Götz et al, 2008;McCarthy et al, 2008;Chen et al, 2009;Zhang et al, 2010Zhang et al, , 2020Bush and Moore, 2012;Glaubitz et al, 2014;Torkamaneh et al, 2020). The following three subsections provide an overview of the strength of genomics, with special reference towards understanding endosperm variability (both across species and within the seed).…”

mentioning

confidence: 99%

Endosperm variability: from endoreduplication within a seed to higher ploidy across species, and its competence

Rangan

2020

Seed Sci. Res.

View full text Add to dashboard Cite

Endosperm tissue that nourishes the embryo during seed development, upon maturity, nourishes the global population with special reference to cereal crops like maize, wheat and rice. In about 70% of the angiosperms, endosperm genome content is ‘3n’ with 2:1 (maternal:paternal) contribution, as a result of the second fertilization event. However, angiosperms evolution also documents diversity in endosperm genome content from ‘2n’ to ‘15n’, in scale with the corresponding maternal genome dosage variability (‘1n’ to ‘14n’), whereas paternal contribution is invariable. In apomicts, due to lack of fertilization, or pseudogamy (fertilization of the central cell for endosperm formation), endosperm genome dosage (m:p) has been reported to range between 1:1 and 8:3. Exceptionally, the central cell with one unreduced nucleus and fused with a reduced sperm cell, with 2:1 normal genome dosage, has been reported in Panicum. Altered genome dosage levels are reportedly correlative with eccentricities among maternal and paternal contribution to seed resource allocation. Besides endosperm ploidy variability between species of angiosperms, the present review gives an overview of the ploidy variability in endosperm cells within a seed, up to ‘690n’. In addition to genome-scale variability in the endosperm, some taxa of angiosperms exhibit chlorophyllous endosperms and some chlorophyllous embryos. Also, endosperm cell number during seed development is reported to have a strong association with grain weight at maturity. Genes underlying these traits of variability are unknown, and the present review underscores the variability and highlights the potential of the single-cell sequencing techniques towards understanding the genetic mechanisms associated with these variable traits.

show abstract

mrMLM v4.0: An R Platform for Multi-locus Genome-wide Association Studies

Cited by 14 publications

References 34 publications

Multi-Locus Genome-Wide Association Studies Reveal Fruit Quality Hotspots in Peach Genome

Multi-Locus Genome-Wide Association Studies Reveal Fruit Quality Hotspots in Peach Genome

Genome-Wide Association Study (GWAS) for Resistance to Sclerotinia sclerotiorum in Common Bean

Endosperm variability: from endoreduplication within a seed to higher ploidy across species, and its competence

Contact Info

Product

Resources

About