Methodological implementation of mixed linear models in multi-locus genome-wide association studies

Wen, Yang; Zhang, Hanwen; Ni, Yuan Li; Huang, Bo; Zhang, Jin; Feng, Jian; Wang, Shi Bo; Dunwell, Jim M.; Zhang, Yuan Ming; Wu, Rongling

doi:10.1093/bib/bbx028

Cited by 57 publications

(99 citation statements)

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“…Thereafter, Liu et al [14] developed FarmCPU. Based on the advantages of the random model of QTN effect over the fixed model [15], recently, we have developed six multi-locus methods: mrMLM [16], FASTmrMLM [17], FASTmrEMMA [18], ISIS EM-BLASSO [19], pLARmEB [20] and pKWmEB [21] (Files S1 and S2). These methods include two stages.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Tamba

Wen

et al. 2020

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Previous studies reported that some important loci are missed in single-locus genomewide association studies (GWAS), especially because of the large phenotypic error in field experiments. To solve this issue, multi-locus GWAS methods have been recommended. However, only a few software packages are available. Therefore, an R software mrMLM, which includes our six multi-locus methods, was developed. mrMLM includes three components: dataset input, parameter setting and result output.The fread function in data.table is used to quickly read datasets, especially big datasets,

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Section: Introductionmentioning

confidence: 99%

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

Zhang

Tamba

Wen

et al. 2020

Preprint

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“…FASTmrEMMA [23], pKWmEB [24], and FASTmrMLM [25]. The MLM method is a single-locus xed-single nucleotide polymorphism (SNP)-effect approach used in the case of a polygenic background to control population structure [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…There are two steps in this model. First, a reduced number of SNPs is selected through different algorithms, and the SNPs are then used in the multilocus model to detect true signals [20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Studies and Whole-Genome Prediction Reveal the Genetic Architecture of KRN in Maize

Chen

et al. 2020

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Background: Kernel row number (KRN) is an important trait for the domestication and improvement of maize. To explore the genetic basis of KRN has great research significance and can provide the valuable information for molecular assisted selection.Results: In this study, one single-locus method (MLM) and six multi-locus methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO) of genome-wide association studies (GWASs) were used to identify significant quantitative trait nucleotides (QTNs) for KRN in an association panel including 639 maize inbred lines that were genotyped by the MaizeSNP50 BeadChip. In three phenotyping environments and with best linear unbiased prediction (BLUP) values, seven GWAS methods revealed different numbers of KRN-associated QTNs, ranging from 11 to 177. Based on these results, seven important regions for KRN located on chromosomes 1, 2, 3, 5, 9, and 10 were identified by at least three methods and in at least two environments. Moreover, 49 genes from the seven regions were expressed in different maize tissues. Among the 49 genes, ARF29 (Zm00001d026540, encoding auxin response factor 29) and CKO4 (Zm00001d043293, encoding cytokinin oxidase protein) were significantly related to KRN based on expression analysis and candidate gene association mapping. Whole-genome prediction (WGP) for KRN was also performed, and we found that the KRN-associated tagSNPs achieved a high prediction accuracy. The best strategy was to integrate the total KRN-associated tagSNPs identified by all GWAS models. Conclusions: These results aid in our understanding of the genetic architecture of KRN and provide useful information for genomic selection for KRN in maize breeding.

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“…Multivariate GWAS method considers the confounding problem between covariates and test marker to detect more QTLs with a lower threshold and false discovery rate. In recent years, a large number of multivariate GWAS methods have been developed, including MLMM (multi-locus mixed-model) [26], FarmCPU (Fixed and random model Circulating Probability Unification) [27], mrMLM (multi-locus random-SNP-effect MLM) [28], FASTmrMLM (fast mrMLM) [29], FASTmrEMMA (fast multi-locus random-SNP-effect efficient mixed model analysis) [30], pLARmEB (polygenic-background-control-based least angle regression plus empirical Bayes) [31], 6 pKWmEB (integration of Kruskal-Wallis test with empirical Bayes) [32], ISIS EM-BLASSO (iterative modified-sure independence screening expectation-maximization-Bayesian least absolute shrinkage and selection operator) [33], and GPWAS (Genome-Phenome Wide Association Study) [34]. The MLMM [26] [35], rice [36,37], foxtail millet [38], soybean [39,40], maize [41,42], and wheat [43,44].…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association studies of ionomic and agronomic traits in USDA minicore collection of rice and comparative analyses of different mapping methods

Liu

Zhong

Meng

et al. 2020

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Rice is an important human staple food vulnerable to heavy metal contamination due to its unique physiology and growth environment. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge. In this report, a comprehensive GWAS analyses for ionomic and agronomic traits based on 3,259,478 SNPs were performed using two univariate methods and two multivariate methods. Under the criterium p-value <1.53×10-8, 106, 47, and 97 QTLs were identified for ionomics in flooded environment, unflooded environment, and agronomic traits, respectively. Detailed analysis of the QTLs revealed that many of the identified QTLs are co-localized with the QTLs reported in prior ionomic and agronomic studies or posited near the genes with known functions in the related traits, suggesting that our GWAS analyses are reliable. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs. When univariate methods failed to identify QTLs for a trait, the multivariate methods frequently detected QTLs. However, when many QTLs were detected by univariate methods, the number of QTLs detected by multivariate methods were reduced in many cases. These analyses suggest that using multiple GWAS methods can complement each other in QTL identification and some methods may be more powerful with less false discovery rate. In addition, several candidate genes involved in ionomic and agronomic traits control were identified by sequence analysis of the QTL regions. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing an important foundation for further studies on reducing heavy-metal contamination and improving crop yields.

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Methodological implementation of mixed linear models in multi-locus genome-wide association studies

Cited by 57 publications

References 0 publications

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

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

Genome-Wide Association Studies and Whole-Genome Prediction Reveal the Genetic Architecture of KRN in Maize

Genome-wide association studies of ionomic and agronomic traits in USDA minicore collection of rice and comparative analyses of different mapping methods

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