scholarly journals Power analysis of artificial selection experiments using efficient whole genome simulation of quantitative traits

2014 ◽  
Author(s):  
Darren Kessner ◽  
John Novembre
Keyword(s):  
Quantitative Trait Loci ◽  
Artificial Selection ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Quantitative Traits ◽  
Extreme Values ◽  
Massively Parallel Sequencing ◽  
Experimental Population ◽  
Selection Experiments ◽  
Trait Loci

AbstractEvolve and resequence studies combine artificial selection experiments with massively parallel sequencing technology to study the genetic basis for complex traits. In these experiments, individuals are selected for extreme values of a trait, causing alleles at quantitative trait loci (QTLs) to increase or decrease in frequency in the experimental population. We present a new analysis of the power of artificial selection experiments to detect and localize quantitative trait loci. This analysis uses a simulation framework that explicitly models whole genomes of individuals, quantitative traits, and selection based on individual trait values. We find that explicitly modeling QTL provides produces qualitatively different insights than considering independent loci with constant selection coefficients. Specifically, we observe how interference between QTLs under selection impacts the trajectories and lengthens the fixation times of selected alleles. We also show that a substantial portion of the genetic variance of the trait (50–100%) can be explained by detected QTLs in as little as 20 generations of selection, depending on the trait architecture and experimental design. Furthermore, we show that power depends crucially on the opportunity for recombination during the experiment. Finally, we show that an increase in power is obtained by leveraging founder haplotype information to obtain allele frequency estimates.

scholarly journals Mapping quantitative trait loci with epistatic effects

2002 ◽  
Vol 79 (2) ◽  
pp. 185-198 ◽  
Author(s):  
NENGJUN YI ◽  
SHIZHONG XU
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Quantitative Traits ◽  
Threshold Model ◽  
Monte Carlo Algorithm ◽  
Epistatic Effects ◽  
Trait Loci ◽  
Robust Statistical Methods ◽  
Qtl Effects

Epistatic variance can be an important source of variation for complex traits. However, detecting epistatic effects is difficult primarily due to insufficient sample sizes and lack of robust statistical methods. In this paper, we develop a Bayesian method to map multiple quantitative trait loci (QTLs) with epistatic effects. The method can map QTLs in complicated mating designs derived from the cross of two inbred lines. In addition to mapping QTLs for quantitative traits, the proposed method can even map genes underlying binary traits such as disease susceptibility using the threshold model. The parameters of interest are various QTL effects, including additive, dominance and epistatic effects of QTLs, the locations of identified QTLs and even the number of QTLs. When the number of QTLs is treated as an unknown parameter, the dimension of the model becomes a variable. This requires the reversible jump Markov chain Monte Carlo algorithm. The utility of the proposed method is demonstrated through analysis of simulation data.

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.

Impact of the D genome and quantitative trait loci on quantitative traits in a spring durum by spring bread wheat cross

2015 ◽  
Vol 128 (9) ◽  
pp. 1799-1811 ◽  
Author(s):  
J. R. Kalous ◽  
J. M. Martin ◽  
J. D. Sherman ◽  
H.-Y. Heo ◽  
N. K. Blake ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Bread Wheat ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Spring Bread Wheat ◽  
D Genome ◽  
Trait Loci ◽  
Wheat Cross

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 The contribution of quantitative trait loci and neutral marker loci to the genetic variances and covariances among quantitative traits in random mating populations.

Genetics ◽  
1995 ◽  
Vol 139 (1) ◽  
pp. 445-455 ◽  
Author(s):  
A Ruiz ◽  
A Barbadilla
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Random Mating ◽  
Unbiased Estimation ◽  
Limited Information ◽  
Trait Loci ◽  
Marker Loci ◽  
Neutral Marker ◽  
Variance Explained

Abstract Using Cockerham's approach of orthogonal scales, we develop genetic models for the effect of an arbitrary number of multiallelic quantitative trait loci (QTLs) or neutral marker loci (NMLs) upon any number of quantitative traits. These models allow the unbiased estimation of the contributions of a set of marker loci to the additive and dominance variances and covariances among traits in a random mating population. The method has been applied to an analysis of allozyme and quantitative data from the European oyster. The contribution of a set marker loci may either be real, when the markers are actually QTLs, or apparent, when they are NMLs that are in linkage disequilibrium with hidden QTLs. Our results show that the additive and dominance variances contributed by a set of NMLs are always minimum estimates of the corresponding variances contributed by the associated QTLs. In contrast, the apparent contribution of the NMLs to the additive and dominance covariances between two traits may be larger than, equal to or lower than the actual contributions of the QTLs. We also derive an expression for the expected variance explained by the correlation between a quantitative trait and multilocus heterozygosity. This correlation explains only a part of the genetic variance contributed by the markers, i.e., in general, a combination of additive and dominance variances and, thus, provides only very limited information relative to the method supplied here.

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