Quantitative trait loci in Drosophila

Phenotypic variation for quantitative traits results from the simultaneous segregation of alleles at multiple quantitative trait loci. Understanding the genetic architecture of quantitative traits begins with mapping quantitative trait loci to broad genomic regions and ends with the molecular definition of quantitative trait loci alleles. This has been accomplished for some quantitative trait loci in Drosophila. Drosophila quantitative trait loci have sex-, environment- and genotype-specific effects, and are often associated with molecular polymorphisms in non-coding regions of candidate genes. These observations offer valuable lessons to those seeking to understand quantitative traits in other organisms, including humans.     

基于选择牵连效应的标记/性状关联分析方法简介

许多重要农艺性状如产量、品质、抗逆性等多表现为数量性状, 是由多个基因和环境共同作用的结果, 对其遗传基础的研究比较困难。近年发展起来的以选择牵连效应分析为基础, 通过标记/性状之间的关联分析方法为这些性状的作图和遗传解析提供了新的手段, 也为作物的分子设计育种提供了新的思路, 其与QTL作图结果互相验证、互相补充, 必将促进数量遗传学、应用基因组学和育种学的发展。文章对关联分析的思路、方法、优缺点及应用时应注意的问题进行了比较系统的介绍。     

我国农作物QTL定位研究的现状和进展

植物大多数重要的经济性状都是数量性状,研究植物数量性状遗传对植物育种具有十分重要的意义。近十几年来,分子生物学特别是分子标记技术的飞速发展,许多植物高饱和的分子连锁图谱的构建,使得我们可以更深入地进行数量性状的研究。本文综述了近年来我国在农作物数量性状遗传研究方面的进展及发展动态。列举了我国利用DNA分子标记进行数量性状QTL定位的47个实例。总结了控制数量性状的QTL的数目、类别、效应及其互作和QTL与环境间的互作关系。讨论了当前在该研究领域中存在的问题,展望了该领域的发展前景。     

Inference of genetic architecture from chromosome partitioning analyses is sensitive to genome variation,sample size,heritability and effect size distribution

Genomewide association studies have contributed immensely to our understanding of the genetic basis of complex traits. One major conclusion arising from these studies is that most traits are controlled by many loci of small effect, confirming the infinitesimal model of quantitative genetics. A popular approach to test for polygenic architecture involves so‐called “chromosome partitioning” where phenotypic variance explained by each chromosome is regressed on the size of the chromosome. First developed for humans, this has now been repeatedly used in other species, but there has been no evaluation of the suitability of this method in species that can differ in their genome characteristics such as number and size of chromosomes. Nor has the influence of sample size, heritability of the trait, effect size distribution of loci controlling the trait or the physical distribution of the causal loci in the genome been examined. Using simulated data, we show that these characteristics have major influence on the inferences of the genetic architecture of traits we can infer using chromosome partitioning analyses. In particular, small variation in chromosome size, small sample size, low heritability, a skewed effect size distribution and clustering of loci can lead to a loss of power and consequently altered inference from chromosome partitioning analyses. Future studies employing this approach need to consider and derive an appropriate null model for their study system, taking these parameters into consideration. Our simulation results can provide some guidelines on these matters, but further studies examining a broader parameter space are needed.     

Genetic and molecular dissection of naturally occurring variation

Recent progress in plant genome analysis has made it possible to examine the naturally occurring allelic variation underlying complex traits. Many studies have described the genetic mapping of quantitative trait loci for several kinds of complex phenotypic traits. Some researchers have taken up the challenge of performing the molecular cloning of genes at these loci, and examples of cloning have recently been reported. Naturally occurring allelic variation could be a new resource for the functional analysis of plant genes.     

我国农作物QTL定位研究的现状和进展

植物大多数重要的经济性状都是数量性状,研究植物数量性状遗传对植物育种具有十分重要的意义。近十几年来,分子生物学特别是分子标记技术的飞速发展,许多植物高饱和的分子连锁图谱的构建,使得我们可以更深入地进行数量性状的研究。本文综述了近年来我国在农作物数量性状遗传研究方面的进展及发展动态。列举了我国利用DNA分子标记进行数量性状QTL定位的47个实例。总结了控制数量性状的QTL的数目、类别、效应及其互作和 QTL与环境间的互作关系。讨论了当前在该研究领域中存在的问题,展望了该领域的发展前 景。     

Mapping Quantitative Trait Loci for Complex Binary Diseases Using Line Crosses

A composite interval gene mapping procedure for complex binary disease traits is proposed in this paper. The binary trait of interest is assumed to be controlled by an underlying liability that is normally distributed. The liability is treated as a typical quantitative character and thus described by the usual quantitative genetics model. Translation from the liability into a binary (disease) phenotype is through the physiological threshold model. Logistic regression analysis is employed to estimate the effects and locations of putative quantitative trait loci (our terminology for a single quantitative trait locus is QTL while multiple loci are referred to as QTLs). Simulation studies show that properties of this mapping procedure mimic those of the composite interval mapping for normally distributed data. Potential utilization of the QTL mapping procedure for resolving alternative genetic models (e.g., single- or two-trait-locus model) is discussed.     

Genetic and genomic analyses for economically important traits and their applications in molecular breeding of cultured fish

The traits of cultured fish must continually be genetically improved to supply high-quality animal protein for human consumption.Economically important fish traits are controlled by multiple gene quantitative trait loci(QTL),most of which have minor effects,but a few genes may have major effects useful for molecular breeding.In this review,we chose relevant studies on some of the most intensively cultured fish and concisely summarize progress on identifying and verifying QTLs for such traits as growth,disease and stress resistance and sex in recent decades.The potential applications of these major-effect genes and their associated markers in marker-assisted selection and molecular breeding,as well as future research directions are also discussed.These genetic and genomic analyses will be valuable for elucidating the mechanisms modulating economically important traits and to establish more effective molecular breeding techniques in fish.     

Revealing the architecture of gene regulation: the promise of eQTL studies

Expression quantitative trait loci (eQTL) mapping studies have become a widely used tool for identifying genetic variants that affect gene regulation. In these studies, expression levels are viewed as quantitative traits, and gene expression phenotypes are mapped to particular genomic loci by combining studies of variation in gene expression patterns with genome-wide genotyping. Results from recent eQTL mapping studies have revealed substantial heritable variation in gene expression within and between populations. In many cases, genetic factors that influence gene expression levels can be mapped to proximal (putatively cis) eQTLs and, less often, to distal (putatively trans) eQTLs. Beyond providing great insight into the biology of gene regulation, a combination of eQTL studies with results from traditional linkage or association studies of human disease may help predict a specific regulatory role for polymorphic sites previously associated with disease.     

水稻QTL分析的研究进展

水稻许多重要的性状是由多基因控制的数量性状,经典的数量遗传学只能把数量性状作为一个整体进行研究。近年来．高密度分子标记连锁图的构建和有效的生物统计学方法的发展使人们对数量性状遗传基础的研究出现了革命性的变化。通过对不同群体内的个体或品系的分子标记基因型和表型数据的共分离分析,能对QTL进行检测和定位。本文对QTL定位的原理和方法进行了介绍,从QTL的数目和效应、上位性效应、QTL基因型与环境的互作、相关性状的QTL以及个体发育不同阶段的QTL等方面对水稻QTL分析的研究进展进行了综述。水稻基因组测序计划已经完成,本文还对基因组时代水稻QTL精细定位和克隆的方法进行了探讨,对QTL分析在水稻育种中的应用前景进行了展望。     

Genetic mapping of quantitative phenotypic traits in Saccharomyces cerevisiae

Saccharomyces cerevisiae has become a favorite production organism in industrial biotechnology presenting new challenges to yeast engineers in terms of introducing advantageous traits such as stress tolerances. Exploring subspecies diversity of S. cerevisiae has identified strains that bear industrially relevant phenotypic traits. Provided that the genetic basis of such phenotypic traits can be identified inverse engineering allows the targeted modification of production strains. Most phenotypic traits of interest in S. cerevisiae strains are quantitative, meaning that they are controlled by multiple genetic loci referred to as quantitative trait loci (QTL). A straightforward approach to identify the genetic basis of quantitative traits is QTL mapping which aims at the allocation of the genetic determinants to regions in the genome. The application of high-density oligonucleotide arrays and whole-genome re-sequencing to detect genetic variations between strains has facilitated the detection of large numbers of molecular markers thus allowing high-resolution QTL mapping over the entire genome. This review focuses on the basic principle and state of the art of QTL mapping in S. cerevisiae. Furthermore we discuss several approaches developed during the last decade that allow down-scaling of the regions identified by QTL mapping to the gene level. We also emphasize the particular challenges of QTL mapping in nonlaboratory strains of S. cerevisiae.     

Molecular marker analysis of genes controlling morphological variation in Brassica rapa (syn. campestris)

Construction of a detailed RFLP linkage map of B. rapa (syn. campestris) made it possible, for the first time, to study individual genes controlling quantitative traits in this species. Ninety-five F₂ individuals from a cross of Chinese cabbage cv Michihili by Spring broccoli were analyzed for segregation at 220 RFLP loci and for variation in leaf, stem, and flowering characteristics. The number, location, and magnitude of genes underlying 28 traits were determined by using an interval mapping method. Zero to five putative quantitative trait loci (QTL) were detected for each of the traits examined. There were unequal gene effects on the expression of many traits, and the inheritance patterns of traits ranged from those controlled by a single major gene plus minor genes to those controlled by polygenes with small and similar effects. The effect of marker locus density on detection of QTL was analyzed, and the results showed that the number of QTL detected did not change when the number of marker loci used for QTL mapping was decreased from 220 to 126; however, a further reduction from 126 to 56 caused more than 15% loss of the total QTL detected. The detection of putative minor QTL by removing the masking effects of major QTL was explored.     

Mapping viability loci using molecular markers

In genetic mapping experiments, some molecular markers often show distorted segregation ratios. We hypothesize that these markers are linked to some viability loci that cause the observed segregation ratios to deviate from Mendelian expectations. Although statistical methods for mapping viability loci have been developed for line-crossing experiments, methods for viability mapping in outbred populations have not been developed yet. In this study, we develop a method for mapping viability loci in outbred populations using a full-sib family as an example. We develop a maximum likelihood (ML) method that uses the observed marker genotypes as data and the proportions of the genotypes of the viability locus as parameters. The ML solutions are obtained via the expectation-maximization algorithm. Application and efficiencies of the method are demonstrated and tested using a set of simulated data. We conclude that mapping viability loci can be accomplished using similar statistical techniques used in quantitative trait locus mapping for quantitative traits.     

From genes to individuals: developmental genes and the generation of the phenotype.

The success of the genetic approach to developmental biology has provided us with a suite of genes that are involved in the regulation of ontogenetic pathways. It is therefore time to ask whether and how such genes might be involved in the generation of adaptive phenotypes. Unfortunately, the current results do not provide a clear answer. Most of the genes that have been studied by developmental biologists affect early embryonic traits with significant effects on the whole organism. These genes are often highly conserved which allows us to do comparative studies even across phyla. However, whether the same genes are also involved in short-term ecological adaptations remains unclear. The suggestion that early acting ontogenetic genes may also affect late phenotypes comes from the genetic analysis of quantitative traits like bristle numbers in Drosophila. A rough mapping of the major loci affecting these traits shows that these loci might correspond to well known early acting genes. On the other hand, there are also many minor effect loci that are as yet uncharacterized. We suggest that these minor loci might correspond to a different class of genes. In comparative studies of randomly drawn cDNAs from Drosophila we find that there is a large group of genes that evolve fast and that are significantly under-represented in normal genetic screens. We speculate that these genes might provide a large, as yet poorly understood, reservoir of genes that might be involved in the evolution of quantitative traits and short-term adaptations.     

Mapping genetic loci that interact with myostatin to affect growth traits

Myostatin, or GDF8, is an inhibitor of skeletal muscle growth. A non-functional myostatin mutation leads to a double muscling phenotype in some species, for example, mice, cattle and humans. Previous studies have indicated that there are loci in the genome that interact with myostatin to control backfat depth and other complex traits. We now report a quantitative trait loci (QTL) mapping study designed to identify loci that interact with myostatin to impact growth traits in mice. Body weight and average daily gain traits were collected on F2 progeny derived from a myostatin-null C57BL/6 strain by M16i cross. In all, 44 main effect QTL were detected above a 5% genome-wide significance threshold when an interval mapping method was used. An additional 37 QTL were identified to significantly interact with myostatin, sex or reciprocal cross. A total of 12 of these QTL interacted with myostatin genotype. These results provide a foundation for the further fine mapping of genome regions that harbor loci that interact with myostatin.     

Linkage disequilibrium analysis of biallelic DNA markers, human quantitative trait loci, and threshold-defined case and control subjects

Linkage disequilibrium (LD) mapping has been applied to many simple, monogenic, overtly Mendelian human traits, with great success. However, extensions and applications of LD mapping approaches to more complex human quantitative traits have not been straightforward. In this article, we consider the analysis of biallelic DNA marker loci and human quantitative trait loci in settings that involve sampling individuals from opposite ends of the trait distribution. The purpose of this sampling strategy is to enrich samples for individuals likely to possess (and not possess) trait-influencing alleles. Simple statistical models for detecting LD between a trait-influencing allele and neighboring marker alleles are derived that make use of this sampling scheme. The power of the proposed method is investigated analytically for some hypothetical gene-effect scenarios. Our studies indicate that LD mapping of loci influencing human quantitative trait variation should be possible in certain settings. Finally, we consider possible extensions of the proposed methods, as well as areas for further consideration and improvement.     

An EM algorithm for mapping quantitative resistance loci

Many disease resistance traits in plants have a polygenic background and the disease phenotypes are modified by environmental factors. As a consequence, the phenotypic values usually show a quantitative variation. The phenotypes of such disease traits, however, are often measured in discrete but ordered categories. These traits are called ordinal traits. In terms of disease resistance, they are called quantitative resistance traits, as opposed to qualitative resistance traits, and are controlled by the quantitative resistance loci (QRL). Classical quantitative trait locus mapping methods are not optimal for ordinal trait analysis because the assumption of normal distribution is violated. Methods for mapping binary trait loci are not suitable either because there are more than two categories in ordinal traits. We developed a maximum likelihood method to map these QRL. The method is implemented via a multicycle expectation-conditional-maximization (ECM) algorithm under the threshold model, where we can estimate both the QRL effects and the thresholds that link the disease liability and the categorical phenotype. The method is verified in simulated data under various combinations of the parameters. An SAS program is available to implement the multicycle ECM algorithm. The program can be downloaded from our website at www.statgen.ucr.edu.