Genome-wide efficient mixed-model analysis for association studies

Zhou, Xiang; Stephens, Matthew

doi:10.1038/ng.2310

Cited by 2,811 publications

(2,936 citation statements)

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“…Genome scan of palatability association. We used a mixed linear model as implemented in gemma 54 for a case-control study of citrus acidity and palatability with 37 citrus accessions. A conservative Bonferroni correction was used to select significant genomic loci, with subsequent manual examination of each candidate variant in all accessions to identify most discriminatory loci for fruit palatability (Supplementary Note 10).…”

Section: Resultsmentioning

confidence: 99%

Genomics of the origin and evolution of Citrus

Terol

Ibáñez

et al. 2018

Nature

632

604

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Genomics of the origin and evolution of CitrusGuohong albert Wu 1 , Javier Terol 2 , Victoria ibanez 2 , antonio López-García 2 , estela Pérez-román 2 , carles borredá 2 , concha Domingo 2 , francisco r. Tadeo 2 , Jose carbonell-caballero 3 , roberto alonso 3 , franck curk 4 , Dongliang Du 5 , Patrick Ollitrault 6 , Mikeal L. roose 7 , Joaquin Dopazo 3,8 , frederick G. Gmitter Jr 5 , Daniel S. rokhsar 1,9,10 & Manuel Talon 2The genus Citrus and related genera (Fortunella, Poncirus, Eremocitrus and Microcitrus) belong to the angiosperm subfamily Aurantioideae of the Rutaceae family, which is widely distributed across the monsoon region from west Pakistan to north-central China and south through the East Indian Archipelago to New Guinea and the Bismarck Archipelago, northeastern Australia, New Caledonia, Melanesia and the western Polynesian islands 1 . Native habitats of citrus and related genera roughly extend throughout this broad area (Extended Data Fig. 1a and Supplementary Table 1), although the geogra phical origin, timing and dispersal of citrus species across southeast Asia remain unclear. A major obstacle to resolving these uncertainties is our poor understanding of the genealogy of complex admixture in cultivated citrus, as has recently been shown 2 . Some citrus are clonally propagated apomictically 3 through nucellar embryony, that is, the development of non-sexual embryos originating in the maternal nucellar tissue of the ovule, and this natural process may have been co-opted during domestication; grafting is a relatively recent phenomenon 4 . Both modes of clonal propagation have led to the domestication of fixed (desirable) genotypes, including interspecific hybrids, such as oranges, limes, lemons, grapefruits and other types.Under this scenario, it is not surprising that the current chaotic citrus taxonomy-based on long-standing, conflicting proposals 5,6 -requires a solid reformulation consistent with a full understanding of the hybrid and/or admixture nature of cultivated citrus species. Here we analyse genome sequences of diverse citrus to characterize the diversity and evolution of citrus at the species level and identify citrus admixtures and interspecific hybrids. We further examine the network of relatedness among mandarins and sweet orange, as well as the pattern of the introgression of pummelos among mandarins for clues to the early stages of citrus domestication. Diversity and evolution of the genus CitrusTo investigate the genetic diversity and evolutionary history of citrus, we analysed the genomes of 58 citrus accessions and two outgroup genera (Poncirus and Severinia) that were sequenced to high coverage, including recently published sequences 2,3,7 as well as 30 new genome sequences described here. For our purpose, we do not include accessions related by somatic mutations. These sequences represent a diverse sampling of citrus species, their admixtures and hybrids (Supplementary Tables 2, 3 and Supplementary Notes 1, 2). Our collection includes accessions from eight previously unsequ...

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Section: Resultsmentioning

confidence: 99%

Genomics of the origin and evolution of Citrus

Terol

Ibáñez

et al. 2018

Nature

632

604

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“…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%

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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“…New Analysis Methodology Underpinning New Discovery GWAS data have led to new analysis methods that fall into a number of categories depending on their purpose: (1) methods of better modeling population structure and relatedness between individuals in a sample during association analyses, [28][29][30][31][32][33][34] (2) methods of detecting novel variants and gene loci on the basis of GWAS summary statistics, [35][36][37] (3) methods of estimating and partitioning genetic (co)variance, 38,39 and (4) methods of inferring causality. [40][41][42] In addition, GWAS discoveries and interpretation have benefited substantially from improved algorithms in statistical imputation of unobserved genotypes and statistical imputation of human leukocyte antigen (HLA) genes and amino acid polymorphisms.…”

Section: Pleiotropy Is Pervasivementioning

confidence: 99%