Advances on methods for mapping QTL in plant
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Family-based QTL mapping
Germ plasm
Inheritance
Trait
Abstract Identifying the genetic basis of complex traits remains an important and challenging problem with the potential to affect a broad range of biological endeavors. A number of statistical methods are available for mapping quantitative trait loci (QTL), but their application to high-throughput phenotypes has been limited as most require user input and interaction. Recently, methods have been developed specifically for expression QTL (eQTL) mapping, but they too are limited in that they do not allow for interactions and QTL of moderate effect. We here propose an automated model-selection-based approach that identifies multiple eQTL in experimental populations, allowing for eQTL of moderate effect and interactions. Output can be used to identify groups of transcripts that are likely coregulated, as demonstrated in a study of diabetes in mouse.
Family-based QTL mapping
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Abstract A maximum-likelihood QTL mapping method that simultaneously exploits linkage and linkage disequilibrium and that is applicable in outbred half-sib pedigrees is described. The method is applied to fine map a QTL with major effect on milk fat content in a 3-cM marker interval on proximal BTA14. This proximal location is confirmed by applying a haplotype-based association method referred to as recombinant ancestral haplotype analysis. The origin of the discrepancy between the QTL position derived in this work and that of a previous analysis is examined and shown to be due to the existence of distinct marker haplotypes associated with QTL alleles having large substitution effects.
Linkage Disequilibrium
Pedigree chart
Family-based QTL mapping
Genetic linkage
Linkage (software)
Association mapping
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Family-based QTL mapping
Germ plasm
Inheritance
Trait
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Citations (14)
Advances on methods for mapping quantitative trait loci (QTL) are firstly summarized. Then, some new methods, including mapping multiple QTL, fine mapping of QTL, and mapping QTL for dynamic traits, are mainly described. Finally, some future prospects are proposed, including how to dig novel genes in the germplasm resource, map expression QTL (eQTL) by the use of all markers, phenotypes and micro-array data, identify QTL using genetic mating designs and detect viability loci. The purpose is to direct plant geneticists to choose a suitable method in the inheritance analysis of quantitative trait and in search of novel genes in germplasm resource so that more potential genetic information can be uncovered.
Family-based QTL mapping
Germ plasm
Inheritance
Trait
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Summary The genetic dissection of complex traits is one of the most difficult and most important challenges facing science today. We discuss here an integrative approach to quantitative trait loci (QTL) mapping in mice. This approach makes use of the wealth of genetic tools available in mice, as well as the recent advances in genome sequence data already available for a number of inbred mouse strains. We have developed mapping strategies that allow a stepwise narrowing of a QTL mapping interval, prioritizing candidate genes for further analysis with the potential of identifying the most probable candidate gene for the given trait. This approach integrates traditional mapping tools, fine mapping tools, sequence‐based analysis, bioinformatics and gene expression.
Family-based QTL mapping
Identification
Trait
Candidate gene
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Inbred strain
Family-based QTL mapping
SNP
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Summary Traditional genetic mapping has largely focused on the identification of loci affecting one, or at most a few, complex traits. Microarrays allow for measurement of thousands of gene expression abundances, themselves complex traits, and a number of recent investigations have considered these measurements as phenotypes in mapping studies. Combining traditional quantitative trait loci (QTL) mapping methods with microarray data is a powerful approach with demonstrated utility in a number of recent biological investigations. These expression quantitative trait loci (eQTL) studies are similar to traditional QTL studies, as a main goal is to identify the genomic locations to which the expression traits are linked. However, eQTL studies probe thousands of expression transcripts; and as a result, standard multi‐trait QTL mapping methods, designed to handle at most tens of traits, do not directly apply. One possible approach is to use single‐trait QTL mapping methods to analyze each transcript separately. This leads to an increased number of false discoveries, as corrections for multiple tests across transcripts are not made. Similarly, the repeated application, at each marker, of methods for identifying differentially expressed transcripts suffers from multiple tests across markers. Here, we demonstrate the deficiencies of these approaches and propose a mixture over markers (MOM) model that shares information across both markers and transcripts. The utility of all methods is evaluated using simulated data as well as data from an F 2 mouse cross in a study of diabetes. Results from simulation studies indicate that the MOM model is best at controlling false discoveries, without sacrificing power. The MOM model is also the only one capable of finding two genome regions previously shown to be involved in diabetes.
Family-based QTL mapping
Trait
Expression (computer science)
Identification
Multiple comparisons problem
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Congenic
Candidate gene
Family-based QTL mapping
Positional cloning
Genetic linkage
Chromosomal region
Genetic architecture
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Abstract We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.
Family-based QTL mapping
Pleiotropy
Trait
Linkage (software)
Genetic architecture
Genetic linkage
Genome-wide Association Study
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Citations (846)