Mapping epigenetic quantitative trait loci (QTL) altering a developmental trajectory

Genome ◽  
2002 ◽  
Vol 45 (1) ◽  
pp. 28-33 ◽  
Author(s):  
Rongling Wu ◽  
Chang-Xing Ma ◽  
Jun Zhu ◽  
George Casella
Keyword(s):  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Genetic Variance ◽  
Genetic Model ◽  
Epigenetic Modification ◽  
Growth Trait ◽  
Developmental Trajectory ◽  
Growth Trajectory ◽  
Trait Loci

Genetic variation in a quantitative trait that changes with age is important to both evolutionary biologists and breeders. A traditional analysis of the dynamics of genetic variation is based on the genetic variance–covariance matrix among different ages estimated from a quantitative genetic model. Such an analysis, however, cannot reveal the mechanistic basis of the genetic variation for a growth trait during ontogeny. Age-specific genetic variance at time t conditional on the causal genetic effect at time t – 1 implies the generation of episodes of new genetic variation arising during the interval t – 1 to t. In the present paper, the conditional genetic variance estimated from Zhu's (1995) conditional model was partitioned into its underlying individual quantitative trait loci (QTL) using molecular markers in an F2 progeny of poplars (Populus trichocarpa and Populus deltoides). These QTL, defined as epigenetic QTL, govern the alterations of growth trajectory in a population. Three epigenetic QTL were detected to contribute significantly to variation in growth trajectory during the period from the establishment year to the subsequent year in the field. It is suggested that the activation and expression of epigenetic QTL are influenced by the developmental status of trees and the environment in which they are grown.Key words: epigenetic modification, development, marker, poplar, QTL.

Modeling Epistasis of Quantitative Trait Loci Using Cockerham's Model

Genetics ◽  
2002 ◽  
Vol 160 (3) ◽  
pp. 1243-1261 ◽  
Author(s):  
Chen-Hung Kao ◽  
Zhao-Bang Zeng
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Genetic Variance ◽  
Genetic Model ◽  
Linkage Equilibrium ◽  
Gene Effects ◽  
Multiple Loci ◽  
Trait Loci ◽  
Orthogonal Property

AbstractWe use the orthogonal contrast scales proposed by Cockerham to construct a genetic model, called Cockerham's model, for studying epistasis between genes. The properties of Cockerham's model in modeling and mapping epistatic genes under linkage equilibrium and disequilibrium are investigated and discussed. Because of its orthogonal property, Cockerham's model has several advantages in partitioning genetic variance into components, interpreting and estimating gene effects, and application to quantitative trait loci (QTL) mapping when compared to other models, and thus it can facilitate the study of epistasis between genes and be readily used in QTL mapping. The issues of QTL mapping with epistasis are also addressed. Real and simulated examples are used to illustrate Cockerham's model, compare different models, and map for epistatic QTL. Finally, we extend Cockerham's model to multiple loci and discuss its applications to QTL mapping.

Empirical Nonparametric Bootstrap Strategies in Quantitative Trait Loci Mapping: Conditioning on the Genetic Model

Genetics ◽  
1998 ◽  
Vol 148 (1) ◽  
pp. 525-535
Author(s):  
Claude M Lebreton ◽  
Peter M Visscher
Keyword(s):  
Quantitative Trait Loci ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Quantitative Trait ◽  
Genetic Factors ◽  
Genetic Model ◽  
Original Data ◽  
Nonparametric Bootstrap ◽  
Test Protocol ◽  
Trait Loci

AbstractSeveral nonparametric bootstrap methods are tested to obtain better confidence intervals for the quantitative trait loci (QTL) positions, i.e., with minimal width and unbiased coverage probability. Two selective resampling schemes are proposed as a means of conditioning the bootstrap on the number of genetic factors in our model inferred from the original data. The selection is based on criteria related to the estimated number of genetic factors, and only the retained bootstrapped samples will contribute a value to the empirically estimated distribution of the QTL position estimate. These schemes are compared with a nonselective scheme across a range of simple configurations of one QTL on a one-chromosome genome. In particular, the effect of the chromosome length and the relative position of the QTL are examined for a given experimental power, which determines the confidence interval size. With the test protocol used, it appears that the selective resampling schemes are either unbiased or least biased when the QTL is situated near the middle of the chromosome. When the QTL is closer to one end, the likelihood curve of its position along the chromosome becomes truncated, and the nonselective scheme then performs better inasmuch as the percentage of estimated confidence intervals that actually contain the real QTL's position is closer to expectation. The nonselective method, however, produces larger confidence intervals. Hence, we advocate use of the selective methods, regardless of the QTL position along the chromosome (to reduce confidence interval sizes), but we leave the problem open as to how the method should be altered to take into account the bias of the original estimate of the QTL's position.

Quantitative trait loci define genes and pathways underlying genetic variation in longevity

2006 ◽  
Vol 41 (10) ◽  
pp. 1046-1054 ◽  
Author(s):  
Robert J. Shmookler Reis ◽  
Ping Kang ◽  
Srinivas Ayyadevara
Keyword(s):  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Trait Loci

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.

Markers, maps and the detection of trait loci

1996 ◽  
Vol 1996 ◽  
pp. 50-50
Author(s):  
C.S. Haley
Keyword(s):  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Genetic Markers ◽  
Quantitative Trait ◽  
Coat Colour ◽  
Combined Effects ◽  
Potential Interest ◽  
Naturally Occurring ◽  
Trait Loci ◽  
Modern Breeding

Naturally occurring genetic variation is the basis for differences in performance and appearance between and within different breeds and lines of livestock. In a few instances (e.g. coat colour, polling) the genes (or loci) which control the variation between animals and breeds have a large enough effect to be individually recognisable. For many traits, however, the combined effects of many different genes act together to control quantitative differences between breeds and individuals within breeds (hence such genes are often referred to as quantitative trait loci or QTLs). Thus the dramatic successes of modern breeding result from generations of selection which has produced accumulated changes at a number of different loci. The genome contains up to 100,000 different genes and identifying those which contribute to variation in traits of interest is a difficult task. One first step is to identify regions of the genome containing loci of potential interest through their linkage to genetic markers.

Genetic variation underlying pod size and color traits of common bean depends on quantitative trait loci with epistatic effects

2014 ◽  
Vol 33 (4) ◽  
pp. 939-952 ◽  
Author(s):  
Fernando J. Yuste-Lisbona ◽  
Ana M. González ◽  
Carmen Capel ◽  
Manuel García-Alcázar ◽  
Juan Capel ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Common Bean ◽  
Quantitative Trait ◽  
Epistatic Effects ◽  
Color Traits ◽  
Trait Loci

scholarly journals Quantitative Trait Loci Variation for Growth and Obesity Between and Within Lines of Pigs (Sus scrofa)

Genetics ◽  
2003 ◽  
Vol 164 (2) ◽  
pp. 629-635 ◽  
Author(s):  
Yoshitaka Nagamine ◽  
Chris S Haley ◽  
Asheber Sewalem ◽  
Peter M Visscher
Keyword(s):  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Sus Scrofa ◽  
Strong Support ◽  
Wild Type ◽  
Porcine Genome ◽  
Trait Loci ◽  
Sib Families ◽  
Related Population

Abstract The hypothesis that quantitative trait loci (QTL) that explain variation between divergent populations also account for genetic variation within populations was tested using pig populations. Two regions of the porcine genome that had previously been reported to harbor QTL with allelic effects that differed between the modern pig and its wild-type ancestor and between the modern pig and a more distantly related population of Asian pigs were studied. QTL for growth and obesity traits were mapped using selectively genotyped half-sib families from five domesticated modern populations. Strong support was found for at least one QTL segregating in each population. For all five populations there was evidence of a segregating QTL affecting fatness in a region on chromosome 7. These findings confirm that QTL can be detected in highly selected commercial populations and are consistent with the hypothesis that the same chromosome locations that account for variation between populations also explain genetic variation within populations.

scholarly journals Linkage Analysis of Molecular Markers and Quantitative Trait Loci in Populations of Inbred Backcross Lines of Brassica napus L.

Genetics ◽  
1999 ◽  
Vol 153 (2) ◽  
pp. 949-964 ◽  
Author(s):  
David V Butruille ◽  
Raymond P Guries ◽  
Thomas C Osborn
Keyword(s):  
Genetic Variation ◽  
Brassica Napus ◽  
Quantitative Trait Loci ◽  
Qtl Analysis ◽  
Quantitative Trait ◽  
Agronomic Traits ◽  
Pedigree Structure ◽  
Inbred Backcross Lines ◽  
Trait Loci ◽  
Inbred Backcross

Abstract Backcross populations are often used to study quantitative trait loci (QTL) after they are initially discovered in balanced populations, such as F2, BC1, or recombinant inbreds. While the latter are more powerful for mapping marker loci, the former have the reduced background genetic variation necessary for more precise estimation of QTL effects. Many populations of inbred backcross lines (IBLs) have been developed in plant and animal systems to permit simultaneous study and dissection of quantitative genetic variation introgressed from one source to another. Such populations have a genetic structure that can be used for linkage estimation and discovery of QTL. In this study, four populations of IBLs of oilseed Brassica napus were developed and analyzed to map genomic regions from the donor parent (a winter-type cultivar) that affect agronomic traits in spring-type inbreds and hybrids. Restriction fragment length polymorphisms (RFLPs) identified among the IBLs were used to calculate two-point recombination fractions and LOD scores through grid searches. This information allowed the enrichment of a composite genetic map of B. napus with 72 new RFLP loci. The selfed and hybrid progenies of the IBLs were evaluated during two growing seasons for several agronomic traits. Both pedigree structure and map information were incorporated into the QTL analysis by using a regression approach. The number of QTL detected for each trait and the number of effective factors calculated by using biometrical methods were of similar magnitude. Populations of IBLs were shown to be valuable for both marker mapping and QTL analysis.

scholarly journals A general mixture model approach for mapping quantitative trait loci from diverse cross designs involving multiple inbred lines

2000 ◽  
Vol 75 (3) ◽  
pp. 345-355 ◽  
Author(s):  
YUEFU LIU ◽  
ZHAO-BANG ZENG
Keyword(s):  
Quantitative Trait Loci ◽  
Statistical Method ◽  
Mixture Model ◽  
Quantitative Trait ◽  
Genetic Model ◽  
General Procedure ◽  
Inbred Lines ◽  
Design Matrix ◽  
Trait Loci ◽  
Parental Lines

Most current statistical methods developed for mapping quantitative trait loci (QTL) based on inbred line designs apply to crosses from two inbred lines. Analysis of QTL in these crosses is restricted by the parental genetic differences between lines. Crosses from multiple inbred lines or multiple families are common in plant and animal breeding programmes, and can be used to increase the efficiency of a QTL mapping study. A general statistical method using mixture model procedures and the EM algorithm is developed for mapping QTL from various cross designs of multiple inbred lines. The general procedure features three cross design matrices, W, that define the contribution of parental lines to a particular cross and a genetic design matrix, D, that specifies the genetic model used in multiple line crosses. By appropriately specifying W matrices, the statistical method can be applied to various cross designs, such as diallel, factorial, cyclic, parallel or arbitrary-pattern cross designs with two or multiple parental lines. Also, with appropriate specification for the D matrix, the method can be used to analyse different kinds of cross populations, such as F2 backcross, four-way cross and mixed crosses (e.g. combining backcross and F2). Simulation studies were conducted to explore the properties of the method, and confirmed its applicability to diverse experimental designs.

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