Dataset Information

Statistical Methods for Latent Class Quantitative Trait Loci Mapping.

ABSTRACT: Identifying the genetic basis of complex traits is an important problem with the potential to impact a broad range of biological endeavors. A number of effective statistical methods are available for quantitative trait loci (QTL) mapping that allow for the efficient identification of multiple, potentially interacting, loci under a variety of experimental conditions. Although proven useful in hundreds of studies, the majority of these methods assumes a single model common to each subject, which may reduce power and accuracy when genetically distinct subclasses exist. To address this, we have developed an approach to enable latent class QTL mapping. The approach combines latent class regression with stepwise variable selection and traditional QTL mapping to estimate the number of subclasses in a population, and to identify the genetic model that best describes each subclass. Simulations demonstrate good performance of the method when latent classes are present as well as when they are not, with accurate estimation of QTL. Application of the method to case studies of obesity and diabetes in mouse gives insight into the genetic basis of related complex traits.

SUBMITTER: Ye S

PROVIDER: S-EPMC5500132 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Statistical Methods for Latent Class Quantitative Trait Loci Mapping.

Ye Shuyun S Bacher Rhonda R Keller Mark P MP Attie Alan D AD Kendziorski Christina C

Genetics 20170526 3

Identifying the genetic basis of complex traits is an important problem with the potential to impact a broad range of biological endeavors. A number of effective statistical methods are available for quantitative trait loci (QTL) mapping that allow for the efficient identification of multiple, potentially interacting, loci under a variety of experimental conditions. Although proven useful in hundreds of studies, the majority of these methods assumes a single model common to each subject, which m ...[more]

PMID: 28550015

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Project description:BackgroundWe introduce a QTL-mapping algorithm based on Statistical Machine Learning (SML) that is conceptually quite different to existing methods as there is a strong focus on generalisation ability. Our approach combines ridge regression, recursive feature elimination, and estimation of generalisation performance and marker effects using bootstrap resampling. Model performance and marker effects are determined using independent testing samples (individuals), thus providing better estimates. We compare the performance of SML against Composite Interval Mapping (CIM), Bayesian Interval Mapping (BIM) and single Marker Regression (MR) on synthetic datasets and a multi-trait and multi-environment dataset of the progeny for a cross between two barley cultivars.ResultsIn an analysis of the synthetic datasets, SML accurately predicted the number of QTL underlying a trait while BIM tended to underestimate the number of QTL. The QTL identified by SML for the barley dataset broadly coincided with known QTL locations. SML reported approximately half of the QTL reported by either CIM or MR, not unexpected given that neither CIM nor MR incorporates independent testing. The latter makes these two methods susceptible to producing overly optimistic estimates of QTL effects, as we demonstrate for MR. The QTL resolution (peak definition) afforded by SML was consistently superior to MR, CIM and BIM, with QTL detection power similar to BIM. The precision of SML was underscored by repeatedly identifying, at < or = 1-cM precision, three QTL for four partially related traits (heading date, plant height, lodging and yield). The set of QTL obtained using a 'raw' and a 'curated' version of the same genotypic dataset were more similar to each other for SML than for CIM or MR.ConclusionThe SML algorithm produces better estimates of QTL effects because it eliminates the optimistic bias in the predictive performance of other QTL methods. It produces narrower peaks than other methods (except BIM) and hence identifies QTL with greater precision. It is more robust to genotyping and linkage mapping errors, and identifies markers linked to QTL in the absence of a genetic map.

Project description:A new mixed-model method was developed for mapping quantitative trait loci (QTL) by incorporating multiple polygenic covariance structures. First, we used genome-wide markers to calculate six different kinship matrices. We then partitioned the total genetic variance into six variance components, one corresponding to each kinship matrix, including the additive, dominance, additive × additive, dominance × dominance, additive × dominance, and dominance × additive variances. The six different kinship matrices along with the six estimated polygenic variances were used to control the genetic background of a QTL mapping model. Simulation studies showed that incorporating epistatic polygenic covariance structure can improve QTL mapping resolution. The method was applied to yield component traits of rice. We analyzed four traits (yield, tiller number, grain number, and grain weight) using 278 immortal F2 crosses (crosses between recombinant inbred lines) and 1619 markers. We found that the relative importance of each type of genetic variance varies across different traits. The total genetic variance of yield is contributed by additive × additive (18%), dominance × dominance (14%), additive × dominance (48%), and dominance × additive (15%) variances. Tiller number is contributed by additive (17%), additive × additive (22%), and dominance × additive (43%) variances. Grain number is mainly contributed by additive (42%), additive × additive (19%), and additive × dominance (31%) variances. Grain weight is almost exclusively contributed by the additive (73%) variance plus a small contribution from the additive × additive (10%) variance. Using the estimated genetic variance components to capture the polygenic covariance structure, we detected 39 effects for yield, 39 effects for tiller number, 24 for grain number, and 15 for grain weight. The new method can be directly applied to polygenic-effect-adjusted genome-wide association studies (GWAS) in human and other species.

Dataset Information

Statistical Methods for Latent Class Quantitative Trait Loci Mapping.

Publications

Statistical Methods for Latent Class Quantitative Trait Loci Mapping.

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