NAM: association studies in multiple populations

Xavier, Alencar; Xu, Shizhong; Muir, William M.; Rainey, Katy Martin

doi:10.1093/bioinformatics/btv448

Cited by 76 publications

(91 citation statements)

References 22 publications

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“…Genotyping of the lines employed an Illumina SoyNAM BeadChip SNP array designed for this population, using 5305 single nucleotide polymorphism (SNP) markers identified from the genomic sequences of all 41 parental lines. We imputed missing SNP locus calls using random forest, and removed SNPs with a minor allele frequency <0.15 (Xavier et al 2016) using the R package NAM (Xavier et al 2015). This left a final total of 4077 SNPs for the association analysis.…”

Section: Methodsmentioning

confidence: 99%

“…Genome-wide association analysis used the random effect model designed for multi-parental populations (Wei and Xu 2015), implemented in the function gwas2 from the R package NAM (Xavier et al 2015). Analysis were performed for individual canopy coverage measurement days spanning 14–56 days after planting, using a random linear effect model:

g = 1 μ + X α + ψ + ε

where g is the vector of genetic values of canopy coverage for a given point in time fitted in Equation 3, μ is the intercept, X is the incidence matrix of alleles generated from the marker data and family information, α is the vector of regression coefficients corresponding to the allele effects, ψ corresponds to the polygenic effect that accounts for population structure, and ε is the vector of residuals.…”

Section: Methodsmentioning

confidence: 99%

“…Thus,

LRT = - 2 (L_{1} - L_{0}) .

In the random model, the LRT follows a mixture of chi-squared and binomial distributions (Xavier et al 2015), with P -values computed using a chi-squared distribution with 0.5 degrees of freedom. The Bonferroni threshold that accounts for false positives under multiple hypothesis testing (

α = 0.05

) was used to define which markers were associated with the trait of interest.…”

Section: Methodsmentioning

confidence: 99%

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Genetic Architecture of Phenomic-Enabled Canopy Coverage inGlycine max

et al. 2017

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Digital imagery can help to quantify seasonal changes in desirable crop phenotypes that can be treated as quantitative traits. Because limitations in precise and functional phenotyping restrain genetic improvement in the postgenomic era, imagery-based phenomics could become the next breakthrough to accelerate genetic gains in field crops. Whereas many phenomic studies focus on exploratory analysis of spectral data without obvious interpretative value, we used field images to directly measure soybean canopy development from phenological stage V2 to R5. Over 3 years, we collected imagery using ground and aerial platforms of a large and diverse nested association panel comprising 5555 lines. Genome-wide association analysis of canopy coverage across sampling dates detected a large quantitative trait locus (QTL) on soybean (Glycine max, L. Merr.) chromosome 19. This QTL provided an increase in yield of 47.3 kg ha−1. Variance component analysis indicated that a parameter, described as average canopy coverage, is a highly heritable trait (h2 = 0.77) with a promising genetic correlation with grain yield (0.87), enabling indirect selection of yield via canopy development parameters. Our findings indicate that fast canopy coverage is an early season trait that is inexpensive to measure and has great potential for application in breeding programs focused on yield improvement. We recommend using the average canopy coverage in multiple trait schemes, especially for the early stages of the breeding pipeline (including progeny rows and preliminary yield trials), in which the large number of field plots makes collection of grain yield data challenging.

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Section: Methodsmentioning

confidence: 99%

g = 1 μ + X α + ψ + ε

Section: Methodsmentioning

confidence: 99%

“…Thus,

LRT = - 2 (L_{1} - L_{0}) .

α = 0.05

) was used to define which markers were associated with the trait of interest.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Genetic Architecture of Phenomic-Enabled Canopy Coverage inGlycine max

et al. 2017

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“…When testing a given marker, we used the corresponding kinship matrix that does not include that chromosome arm. Other approaches to mapping that could be implemented to avoid overly conservative results are the NAM-R (Xavier et al 2015) R package or the BayesCpi function in the gdmp (Abdel-Azim 2016) R package. For the DGRP, we used the A.mat function in the rrBLUP package to obtain these kinship matrices (Endelman 2011; Poland et al 2012).…”

Section: Methodsmentioning

confidence: 99%

The Beavis Effect in Next-Generation Mapping Panels inDrosophila melanogaster

King

Long

2017

G3 Genes|Genomes|Genetics

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A major goal in the analysis of complex traits is to partition the observed genetic variation in a trait into components due to individual loci and perhaps variants within those loci. However, in both QTL mapping and genetic association studies, the estimated percent variation attributable to a QTL is upwardly biased conditional on it being discovered. This bias was first described in two-way QTL mapping experiments by William Beavis, and has been referred to extensively as “the Beavis effect.” The Beavis effect is likely to occur in multiparent population (MPP) panels as well as collections of sequenced lines used for genome-wide association studies (GWAS). However, the strength of the Beavis effect is unknown—and often implicitly assumed to be negligible—when “hits” are obtained from an association panel consisting of hundreds of inbred lines tested across millions of SNPs, or in multiparent mapping populations where mapping involves fitting a complex statistical model with several d.f. at thousands of genetic intervals. To estimate the size of the effect in more complex panels, we performed simulations of both biallelic and multiallelic QTL in two major Drosophila melanogaster mapping panels, the GWAS-based Drosophila Genetic Reference Panel (DGRP), and the MPP the Drosophila Synthetic Population Resource (DSPR). Our results show that overestimation is determined most strongly by sample size and is only minimally impacted by the mapping design. When < 100, 200, 500, and 1000 lines are employed, the variance attributable to hits is inflated by factors of 6, 3, 1.5, and 1.1, respectively, for a QTL that truly contributes 5% to the variation in the trait. This overestimation indicates that QTL could be difficult to validate in follow-up replication experiments where additional individuals are examined. Further, QTL could be difficult to cross-validate between the two Drosophila resources. We provide guidelines for: (1) the sample sizes necessary to accurately estimate the percent variance to an identified QTL, (2) the conditions under which one is likely to replicate a mapped QTL in a second study using the same mapping population, and (3) the conditions under which a QTL mapped in one mapping panel is likely to replicate in the other (DGRP and DSPR).

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“…Specifically, EMMA employs restricted maximum likelihood (REML) to efficiently estimate variance components whereas variance components are numerically optimized in the MCMC framework. EMMA models were fit using the NAM R package (45), and MCMC models were fit using the MCMCglmm R package (44).…”

Section: Heritability Analyses: Heritability Was Estimated Using Effimentioning

confidence: 99%

Highly Heritable and Functionally Relevant Breed Differences in Dog Behavior

MacLeant

Snyder‐Mackler

vonHoldt

et al. 2019

Preprint

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Variation across dog breeds presents a unique opportunity for investigating the evolution and biological basis of complex behavioral traits. We integrated behavioral data from more than 17,000 dogs from 101 breeds with breed-averaged genotypic data (N = 5,697 dogs) from over 100,000 loci in the dog genome. Across 14 traits, we found that breed differences in behavior are highly heritable, and that clustering of breeds based on behavior accurately recapitulates genetic relationships. We identify 131 single nucleotide polymorphisms associated with breed differences in behavior, which are found in genes that are highly expressed in the brain and enriched for neurobiological functions and developmental processes. Our results provide insight into the heritability and genetic architecture of complex behavioral traits, and suggest that dogs provide a powerful model for these questions.

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NAM: association studies in multiple populations

Abstract: Supplementary date are available at Bioinformatics online.

Cited by 76 publications

References 22 publications

Genetic Architecture of Phenomic-Enabled Canopy Coverage inGlycine max

Genetic Architecture of Phenomic-Enabled Canopy Coverage inGlycine max

The Beavis Effect in Next-Generation Mapping Panels inDrosophila melanogaster

Highly Heritable and Functionally Relevant Breed Differences in Dog Behavior

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