scholarly journals Bayesian Mapping of Quantitative Trait Loci for Multiple Complex Traits with the Use of Variance Components

2007 ◽  
Vol 81 (2) ◽  
pp. 304-320 ◽  
Author(s):  
Jianfeng Liu ◽  
Yongjun Liu ◽  
Xiaogang Liu ◽  
Hong-Wen Deng
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Variance Components ◽  
Trait Loci ◽  
Bayesian Mapping

scholarly journals Bayesian Mapping of Quantitative Trait Loci for Complex Binary Traits

Genetics ◽  
2000 ◽  
Vol 155 (3) ◽  
pp. 1391-1403
Author(s):  
Nengjun Yi ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Loci ◽  
Bayesian Statistics ◽  
Quantitative Trait ◽  
Data Augmentation ◽  
Threshold Model ◽  
Binary Trait ◽  
Monte Carlo Algorithm ◽  
Binary Traits ◽  
Trait Loci ◽  
Bayesian Mapping

Abstract A complex binary trait is a character that has a dichotomous expression but with a polygenic genetic background. Mapping quantitative trait loci (QTL) for such traits is difficult because of the discrete nature and the reduced variation in the phenotypic distribution. Bayesian statistics are proved to be a powerful tool for solving complicated genetic problems, such as multiple QTL with nonadditive effects, and have been successfully applied to QTL mapping for continuous traits. In this study, we show that Bayesian statistics are particularly useful for mapping QTL for complex binary traits. We model the binary trait under the classical threshold model of quantitative genetics. The Bayesian mapping statistics are developed on the basis of the idea of data augmentation. This treatment allows an easy way to generate the value of a hypothetical underlying variable (called the liability) and a threshold, which in turn allow the use of existing Bayesian statistics. The reversible jump Markov chain Monte Carlo algorithm is used to simulate the posterior samples of all unknowns, including the number of QTL, the locations and effects of identified QTL, genotypes of each individual at both the QTL and markers, and eventually the liability of each individual. The Bayesian mapping ends with an estimation of the joint posterior distribution of the number of QTL and the locations and effects of the identified QTL. Utilities of the method are demonstrated using a simulated outbred full-sib family. A computer program written in FORTRAN language is freely available on request.

scholarly journals Mapping of Quantitative Trait Loci Controlling Adaptive Traits in Coastal Douglas Fir. III. Quantitative Trait Loci-by-Environment Interactions

Genetics ◽  
2003 ◽  
Vol 165 (3) ◽  
pp. 1489-1506
Author(s):  
Kathleen D Jermstad ◽  
Daniel L Bassoni ◽  
Keith S Jech ◽  
Gary A Ritchie ◽  
Nicholas C Wheeler ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Moisture Stress ◽  
Interval Mapping ◽  
Douglas Fir ◽  
Adaptive Traits ◽  
Growth Cessation ◽  
Trait Loci ◽  
Growth Initiation

Abstract Quantitative trait loci (QTL) were mapped in the woody perennial Douglas fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) for complex traits controlling the timing of growth initiation and growth cessation. QTL were estimated under controlled environmental conditions to identify QTL interactions with photoperiod, moisture stress, winter chilling, and spring temperatures. A three-generation mapping population of 460 cloned progeny was used for genetic mapping and phenotypic evaluations. An all-marker interval mapping method was used for scanning the genome for the presence of QTL and single-factor ANOVA was used for estimating QTL-by-environment interactions. A modest number of QTL were detected per trait, with individual QTL explaining up to 9.5% of the phenotypic variation. Two QTL-by-treatment interactions were found for growth initiation, whereas several QTL-by-treatment interactions were detected among growth cessation traits. This is the first report of QTL interactions with specific environmental signals in forest trees and will assist in the identification of candidate genes controlling these important adaptive traits in perennial plants.

Bayesian Model Choice and Search Strategies for Mapping Interacting Quantitative Trait Loci

Genetics ◽  
2003 ◽  
Vol 165 (2) ◽  
pp. 867-883 ◽  
Author(s):  
Nengjun Yi ◽  
Shizhong Xu ◽  
David B Allison
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Bayesian Model ◽  
Genetic Model ◽  
Genetic Effects ◽  
Monte Carlo Algorithm ◽  
Data Set ◽  
Epistatic Effects ◽  
Trait Loci

AbstractMost complex traits of animals, plants, and humans are influenced by multiple genetic and environmental factors. Interactions among multiple genes play fundamental roles in the genetic control and evolution of complex traits. Statistical modeling of interaction effects in quantitative trait loci (QTL) analysis must accommodate a very large number of potential genetic effects, which presents a major challenge to determining the genetic model with respect to the number of QTL, their positions, and their genetic effects. In this study, we use the methodology of Bayesian model and variable selection to develop strategies for identifying multiple QTL with complex epistatic patterns in experimental designs with two segregating genotypes. Specifically, we develop a reversible jump Markov chain Monte Carlo algorithm to determine the number of QTL and to select main and epistatic effects. With the proposed method, we can jointly infer the genetic model of a complex trait and the associated genetic parameters, including the number, positions, and main and epistatic effects of the identified QTL. Our method can map a large number of QTL with any combination of main and epistatic effects. Utility and flexibility of the method are demonstrated using both simulated data and a real data set. Sensitivity of posterior inference to prior specifications of the number and genetic effects of QTL is investigated.

scholarly journals How to deal with genotype uncertainty in variance component quantitative trait loci analyses

2011 ◽  
Vol 93 (5) ◽  
pp. 333-342 ◽  
Author(s):  
XIA SHEN ◽  
LARS RÖNNEGÅRD ◽  
ÖRJAN CARLBORG
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Variance Component ◽  
Large Animal ◽  
Trait Loci ◽  
Sib Families ◽  
Variance Component Estimates ◽  
Variance Component Models ◽  
Component Models

SummaryDealing with genotype uncertainty is an ongoing issue in genetic analyses of complex traits. Here we consider genotype uncertainty in quantitative trait loci (QTL) analyses for large crosses in variance component models, where the genetic information is included in identity-by-descent (IBD) matrices. An IBD matrix is one realization from a distribution of potential IBD matrices given available marker information. In QTL analyses, its expectation is normally used resulting in potentially reduced accuracy and loss of power. Previously, IBD distributions have been included in models for small human full-sib families. We develop an Expectation–Maximization (EM) algorithm for estimating a full model based on Monte Carlo imputation for applications in large animal pedigrees. Our simulations show that the bias of variance component estimates using traditional expected IBD matrix can be adjusted by accounting for the distribution and that the calculations are computationally feasible for large pedigrees.

scholarly journals Characterization of expression quantitative trait loci in extensively phenotyped pedigrees ascertained for bipolar disorder

2015 ◽  
Author(s):  
Christine Peterson ◽  
Susan Service ◽  
Anna Jasinska ◽  
Fuying Gao ◽  
Ivette Zelaya ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bipolar Disorder ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Expression Quantitative Trait Loci ◽  
Genome Wide ◽  
Wide Range ◽  
Trait Loci

The observation that variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits has made the genome-wide characterization of gene expression an important tool in genetic mapping studies of such traits. As part of a study to identify genetic loci contributing to bipolar disorder and a wide range of BP-related quantitative traits in members of 26 pedigrees from Costa Rica and Colombia, we measured gene expression in lymphoblastoid cell lines derived from 786 pedigree members. The study design enabled us to comprehensively reconstruct the genetic regulatory network in these families, provide estimates of heritability, identify eQTL, evaluate missing heritability for the eQTL, and quantify the number of different alleles contributing to any given locus.

scholarly journals Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids

2017 ◽  
Author(s):  
Fanny Bonnafous ◽  
Ghislain Fievet ◽  
Nicolas Blanchet ◽  
Marie-Claude Boniface ◽  
Sébastien Carrère ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Flowering Time ◽  
Genetic Control ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Association Studies ◽  
Additive Models ◽  
Genome Wide Association Studies ◽  
Trait Loci ◽  
Genomic Regions

AbstractGenome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

scholarly journals Identification of putative effector genes across the GWAS Catalog using molecular quantitative trait loci from 68 tissues and cell types

2019 ◽  
Author(s):  
Cong Guo ◽  
Karsten B. Sieber ◽  
Jorge Esparza-Gordillo ◽  
Mark R. Hurle ◽  
Kijoung Song ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Effector Genes ◽  
Genome Wide ◽  
Trait Loci ◽  
Gwas Catalog

AbstractIdentifying the effector genes from genome-wide association studies (GWAS) is a crucial step towards understanding the biological mechanisms underlying complex traits and diseases. Colocalization of expression and protein quantitative trait loci (eQTL and pQTL, hereafter collectively called “xQTL”) can be effective for mapping associations to genes in many loci. However, existing colocalization methods require full single-variant summary statistics which are often not readily available for many published GWAS or xQTL studies. Here, we present PICCOLO, a method that uses minimum SNP p-values within a locus to determine if pairs of genetic associations are colocalized. This method greatly expands the number of GWAS and xQTL datasets that can be tested for colocalization. We applied PICCOLO to 10,759 genome-wide significant associations across the NHGRI-EBI GWAS Catalog with xQTLs from 28 studies. We identified at least one colocalized gene-xQTL in at least one tissue for 30% of associations, and we pursued multiple lines of evidence to demonstrate that these mappings are biologically meaningful. PICCOLO genes are significantly enriched for biologically relevant tissues, and 4.3-fold enriched for targets of approved drugs.

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