Dataset Information

The flashfm approach for fine-mapping multiple quantitative traits.

ABSTRACT: Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Here we show flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.

SUBMITTER: Hernandez N

PROVIDER: S-EPMC8536717 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The flashfm approach for fine-mapping multiple quantitative traits.

Hernández N N Soenksen J J Newcombe P P Sandhu M M Barroso I I Wallace C C Asimit J L JL

Nature communications 20211022 1

Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between trait ...[more]

PMID: 34686674

Similar Datasets

Project description:Complex traits important for humans are often correlated phenotypically and genetically. Joint mapping of quantitative-trait loci (QTLs) for multiple correlated traits plays an important role in unraveling the genetic architecture of complex traits. Compared with single-trait analysis, joint mapping addresses more questions and has advantages for power of QTL detection and precision of parameter estimation. Some statistical methods have been developed to map QTLs underlying multiple traits, most of which are based on maximum-likelihood methods. We develop here a multivariate version of the Bayes methodology for joint mapping of QTLs, using the Markov chain-Monte Carlo (MCMC) algorithm. We adopt a variance-components method to model complex traits in outbred populations (e.g., humans). The method is robust, can deal with an arbitrary number of alleles with arbitrary patterns of gene actions (such as additive and dominant), and allows for multiple phenotype data of various types in the joint analysis (e.g., multiple continuous traits and mixtures of continuous traits and discrete traits). Under a Bayesian framework, parameters--including the number of QTLs--are estimated on the basis of their marginal posterior samples, which are generated through two samplers, the Gibbs sampler and the reversible-jump MCMC. In addition, we calculate the Bayes factor related to each identified QTL, to test coincident linkage versus pleiotropy. The performance of our method is evaluated in simulations with full-sib families. The results show that our proposed Bayesian joint-mapping method performs well for mapping multiple QTLs in situations of either bivariate continuous traits or mixed data types. Compared with the analysis for each trait separately, Bayesian joint mapping improves statistical power, provides stronger evidence of QTL detection, and increases precision in estimation of parameter and QTL position. We also applied the proposed method to a set of real data and detected a coincident linkage responsible for determining bone mineral density and areal bone size of wrist in humans.

Project description:BackgroundYam (Dioscorea spp.) is multiple species with various ploidy level and considered as cash crop in many producing areas. Selection based phenotyping for yield and its related traits such as mosaic virus and anthracnose diseases resistance and plant vigor in multiple species of yam is lengthy however, marker information has proven to enhance selection efficiency.MethodologyIn this study, a panel of 182 yam accessions distributed across six yam species were assessed for diversity and marker-traits association study using SNP markers generated from Diversity Array Technology platform. For the traits association analysis, the relation matrix alongside the population structure were used as co-factor to avoid false discovery using Multiple random Mixed Linear Model (MrMLM) followed by gene annotation.ResultsAccessions performance were significantly different (p < 0.001) across all the traits with high broad-sense heritability (H2). Phenotypic and genotypic correlations showed positive relationships between yield and vigor but negative for yield and yam mosaic disease severity. Population structure revealed k = 6 as optimal clusters-based species. A total of 22 SNP markers were identified to be associated with yield, vigor, mosaic and anthracnose diseases resistance. Gene annotation for the significant SNP loci identified some putative genes associated with primary metabolism, pest and resistance to anthracnose disease, maintenance of NADPH in biosynthetic reaction especially those involving nitro-oxidative stress for resistance to mosaic virus, and seed development, photosynthesis, nutrition use efficiency, stress tolerance, vegetative and reproductive development for tuber yield.ConclusionThis study provides valuable insights into the genetic control of plant vigor, anthracnose, mosaic virus resistance, and tuber yield in yam and thus, opens an avenue for developing additional genomic resources for markers-assisted selection focusing on multiple yam species.

Dataset Information

The flashfm approach for fine-mapping multiple quantitative traits.

Publications

The flashfm approach for fine-mapping multiple quantitative traits.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets