Efficient multivariate linear mixed model algorithms for genome-wide association studies

Zhou, Xiang; Stephens, Matthew

doi:10.1038/nmeth.2848

Cited by 739 publications

(919 citation statements)

References 56 publications

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“…Following the approach taken in ref. 52, we quantile-normalized each trait to follow a unit variance normal distribution. We used genetic features (SNPs) for a total of 328,517 variants with a MAF of at least 1%.…”

Section: Methodsmentioning

confidence: 99%

“…From a total of 328,515 genetic features (SNPs), we report the 40 most associated features identified by the mixed RF. Among these, 29 loci have previously been confirmed in a large meta-study 51 or have been identified using alternative methods applied to the same data 52 Considered were eQTLs for 300 gene expression traits mapped using the identical methods as considered in Fig. 2.…”

Section: Article Nature Communications | Doi: 101038/ncomms8432mentioning

confidence: 99%

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A random forest approach to capture genetic effects in the presence of population structure

2015

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The accurate mapping of causal variants in genome-wide association studies requires the consideration of both, confounding factors (for example, population structure) and nonlinear interactions between individual genetic variants. Here, we propose a method termed 'mixed random forest' that simultaneously accounts for population structure and captures nonlinear genetic effects. We test the model in simulation experiments and show that the mixed random forest approach improves detection power compared with established approaches. In an application to data from an outbred mouse population, we find that mixed random forest identifies associations that are more consistent with prior knowledge than competing methods. Further, our approach allows predicting phenotypes from genotypes with greater accuracy than any of the other methods that we tested. Our results show that approaches that simultaneously account for both, confounding due to population structure and epistatic interactions, are important to fully explain the heritable component of complex quantitative traits.

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Section: Methodsmentioning

confidence: 99%

Section: Article Nature Communications | Doi: 101038/ncomms8432mentioning

confidence: 99%

A random forest approach to capture genetic effects in the presence of population structure

2015

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“…There are several multiple-phenotypes analysis methods considering population structure Zhou and Stephens 2014). These methods explicitly model the dependencies of phenotypes to accurately estimate associations between a SNP and phenotypes.…”

Section: Efficiency Of Gammamentioning

confidence: 99%

“…Unfortunately, none of the previously discussed multivariate methods are able to correct for population structure and may cause a significant number of false positive results. Recently, multiple-phenotypes analysis methods have been developed that consider population structure Zhou and Stephens 2014). However, these methods are impractical for cases with large number of phenotypes (.100) since their computational time scales quadratically with the number of phenotypes considered.…”

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confidence: 99%

Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure

et al. 2016

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A typical genome-wide association study tests correlation between a single phenotype and each genotype one at a time. However, single-phenotype analysis might miss unmeasured aspects of complex biological networks. Analyzing many phenotypes simultaneously may increase the power to capture these unmeasured aspects and detect more variants. Several multivariate approaches aim to detect variants related to more than one phenotype, but these current approaches do not consider the effects of population structure. As a result, these approaches may result in a significant amount of false positive identifications. Here, we introduce a new methodology, referred to as GAMMA for generalized analysis of molecular variance for mixed-model analysis, which is capable of simultaneously analyzing many phenotypes and correcting for population structure. In a simulated study using data implanted with true genetic effects, GAMMA accurately identifies these true effects without producing false positives induced by population structure. In simulations with this data, GAMMA is an improvement over other methods which either fail to detect true effects or produce many false positive identifications. We further apply our method to genetic studies of yeast and gut microbiome from mice and show that GAMMA identifies several variants that are likely to have true biological mechanisms.

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“…Genome-Wide Association Study (GWAS), locus sequencing by Next-Generation Sequencing (NGS) and Allele-Specific PCR Using the Illumina Canine HD 173k (BeadChip), 62 dogs were genotyped at the Centre National de Génotypage (CNG; Evry, France) and statistical analyses were performed using Plink software (v1.06-1.07) and GEMMA software (v0.94.1) (Purcell et al 2007;Zhou & Stephens, 2014). Locus sequencing was performed by capture-sequencing on four affected and four unaffected dogs by Integragen (Evry, France): the genomic libraries were made using the Illumina paired-end library sample preparation kit (Illumina Inc.), the capture experiment was performed with the Agilent SureSelect Target Enrichment System Kit and samples were paired-end sequenced on an Illumina HiSeq2000.…”

Section: Nucleic Acid Extractionmentioning

confidence: 99%

A spontaneous dog model for a human sensory neuropathy: identification of a mutation in the upstream region of a neurotrophic factor

Correard¹,

Plassais²,

Lagoutte³

et al. 2016

bavf

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Dans cette étude, nous avons recherché la cause génétique du syndrome d'automutilation chez les chiens de chasse, qui correspond chez l'Homme à une neuropathie sensitive de type HSAN. Nous avons identifié une mutation en amont d'un gène codant un facteur neurotrophique : GDNF (« Glial cell line-Derived Neurotrophic Factor »). Cette mutation ségrége parfaitement avec la maladie chez 250 chiens de chasse de statut clinique connu et est absente chez 900 chiens indemnes de 130 autres races. Cette mutation étant située dans un ARN long non codant, une analyse fine de la région chromosomique et des études d'expression de gènes ont été réalisées pour comprendre son rôle dans la physiopathologie de la maladie. Ainsi, non seulement, nous apportons un nouveau gène candidat pour les neuropathies humaines, mais nous proposons également un mécanisme de régulation transcriptionnelle original, basé sur un « partenariat » entre le gène codant le facteur neurotrophique et l'ARN long non codant. In this study, we sought the genetic cause of self-mutilation syndrome in sporting dogs, which corresponds to human Hereditary Sensory and Autonomic Neuropathies (HSAN). We have identified a genetic mutation upstream of the gene encoding Glial cell line-Derived Neurotrophic Factor (GDNF). This mutation is responsible for insensitivity to pain in four sporting dog breeds and it perfectly seg-

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Efficient multivariate linear mixed model algorithms for genome-wide association studies

Cited by 739 publications

References 56 publications

A random forest approach to capture genetic effects in the presence of population structure

A random forest approach to capture genetic effects in the presence of population structure

Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure

A spontaneous dog model for a human sensory neuropathy: identification of a mutation in the upstream region of a neurotrophic factor

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