GAPIT: genome association and prediction integrated tool

Lipka, Alexander E.; Tian, Feng; Wang, Qishan; Peiffer, Jason A.; Li, Meng; Bradbury, Peter J.; Gore, Michael A.; Buckler, Edward S.; Zhang, Zhiwu

doi:10.1093/bioinformatics/bts444

Cited by 1,846 publications

(1,662 citation statements)

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“…This has been proposed in Lippert et al (2011) (supplement), but to our knowledge this has not been implemented in the Fast-LMM software. Although we did not find one-stage GWAS to be more powerful in the present study, the ability to perform fast association mapping for genetically identical individuals is useful in the context of a compressed kinship matrix (Bradbury et al 2007;Zhang et al 2010;Lipka et al 2012). scan_GLS also includes a function to perform GLS calculations with nondiagonal residual variance structure, allowing association mapping with extra (possibly nongenetic) random effects.…”

Section: Softwarementioning

confidence: 85%

“…Many state-of-the-art methods for GWAS (Kang et al 2010;Lipka et al 2012;Zhou and Stephens 2012) use the same mixed model as in marker-based estimation of heritability, apart from the additional marker effect (Appendix D). When testing the significance of this marker effect, the estimated genetic and residual variances (and hence the heritability) determine the correction for population structure or genetic effects elsewhere in the genome.…”

Section: Gwasmentioning

confidence: 99%

“…In mixed-model-based GWAS (Kang et al 2010;Lipka et al 2012;Zhou and Stephens 2012) the phenotype of genotype i is modeled as…”

Section: Genome-wide Association Studiesmentioning

confidence: 99%

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Marker-Based Estimation of Heritability in Immortal Populations

et al. 2014

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Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within-and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.KEYWORDS marker-based estimation of heritability; GWAS; genomic prediction; Arabidopsis thaliana; one-vs. two-stage approaches N ARROW-SENSE heritability is an important parameter in quantitative genetics, determining the response to selection and representing the proportion of phenotypic variance that is due to additive genetic effects (Jacquard 1983;Ritland 1996;Visscher et al. 2006Visscher et al. , 2008Holland et al. 2010;Sillanpaa 2011). This definition of heritability goes back to Fisher (1918) and Wright (1920) almost a century ago. In plant species for which replicates of the same genotype are available (inbred lines, doubled haploids, clones), a different form of heritability, broadsense heritability, is traditionally estimated by the intraclass correlation coefficient for genotypic effects, using estimates for within-and between-genotype variance. Broad-sense heritability is also referred to as repeatability and gives the proportion of phenotypic variance explained by heritable (additive) and nonheritable (dominance, epistasis) genetic variance.With the arrival of high-density genotyping there is growing interest in marker-based estimation of narrow-sense heritability (WTCCC 2007;Yang et al. 2010Yang et al. , 2011Vatti...

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Section: Softwarementioning

confidence: 85%

Section: Gwasmentioning

confidence: 99%

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Marker-Based Estimation of Heritability in Immortal Populations

et al. 2014

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“…GWA was executed in R with GAPIT using CMLM Lipka et al, 2012). Significant associations were determined by estimates of false discovery rate (P = 0.05; Benjamini and Hochberg, 1995).…”

Section: Gwamentioning

confidence: 99%

“…A kinship matrix was constructed to correct for population structure and cryptic relatedness (Supplemental Table S10). The kinship matrix was estimated from all of the SNPs in the data set using the VanRaden method (VanRaden, 2008) in GAPIT (Lipka et al, 2012). Kinship was included as a random effect in the MLMM.…”

Section: Gwamentioning

confidence: 99%

Integration of Experiments across Diverse Environments Identifies the Genetic Determinants of Variation in Sorghum bicolor Seed Element Composition

et al. 2016

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Seedling establishment and seed nutritional quality require the sequestration of sufficient element nutrients. The identification of genes and alleles that modify element content in the grains of cereals, including sorghum (Sorghum bicolor), is fundamental to developing breeding and selection methods aimed at increasing bioavailable element content and improving crop growth. We have developed a high-throughput work flow for the simultaneous measurement of multiple elements in sorghum seeds. We measured seed element levels in the genotyped Sorghum Association Panel, representing all major cultivated sorghum races from diverse geographic and climatic regions, and mapped alleles contributing to seed element variation across three environments by genome-wide association. We observed significant phenotypic and genetic correlation between several elements across multiple years and diverse environments. The power of combining high-precision measurements with genome-wide association was demonstrated by implementing rank transformation and a multilocus mixed model to map alleles controlling 20 element traits, identifying 255 loci affecting the sorghum seed ionome. Sequence similarity to genes characterized in previous studies identified likely causative genes for the accumulation of zinc, manganese, nickel, calcium, and cadmium in sorghum seeds. In addition to strong candidates for these five elements, we provide a list of candidate loci for several other elements. Our approach enabled the identification of single-nucleotide polymorphisms in strong linkage disequilibrium with causative polymorphisms that can be evaluated in targeted selection strategies for plant breeding and improvement.

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Genome‐Wide Association Studies in Plants

Anderson

Edwards

Batley

et al. 2019

Encyclopedia of Life Sciences

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Cheap genome sequencing technology has made it possible to search for genomic variants called single nucleotide polymorphisms (SNPs) for hundreds of individuals. Linking these genomic variants to phenotypes is the main goal in running genome‐wide association studies (GWAS). SNPs can be discovered and called using different technologies and methods, and subsequent quality control must be performed taking into account the species of study and genotyping techniques. GWAS can be performed using different mathematical approaches, demonstrated within the current range of software packages, which are used to perform the GWAS and interpret the subsequent results. Key Concepts GWAS is a powerful tool to associate genomic variants with phenotypes. Quality control is core to any good GWAS. Numerous powerful tools now exist to make running a GWAS straightforward. Interpreting the output is still a challenge, especially in the presence of hidden confounding factors. GWAS can be performed using different types of genotyping data, each with its own advantages and disadvantages.

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GAPIT: genome association and prediction integrated tool

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 1,846 publications

References 16 publications

Marker-Based Estimation of Heritability in Immortal Populations

Marker-Based Estimation of Heritability in Immortal Populations

Integration of Experiments across Diverse Environments Identifies the Genetic Determinants of Variation in Sorghum bicolor Seed Element Composition

Genome‐Wide Association Studies in Plants

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