Power estimation for in silico mapping

A fundamentally new approach to gene mapping of complex traits was suggested recently. It consists in computer analysis of existing databases on the phenotypes and single nucleotide polymorphisms (SNPs) in inbred mouse strains and was termed in silico mapping. The power of this method has been studied by simulating quantitative traits controlled by one, two, or three genes. The results have demonstrated that the power of in silico mapping is high in the case of a monogenic trait. The probability of mapping all genes determining a digenic or, especially, trigenic trait is low. If two or three genes make equal phenotypic contributions to a trait, the proportions of experiments where none of them is localized are 17 and 25%, respectively. In the case of a major gene effect, when the phenotypic contribution of one gene considerably exceeds those of the other genes, the probability to map the major gene is 0.95 and 0.80 for the digenic and trigenic models, respectively. This shows that, in the case of polygenic control, the new method could localize only the genes with major effects, while most genes involved in the control of the trait would not be mapped.     

Power estimation for in silico mapping

A fundamentally new approach to gene mapping of complex traits was suggested recently. It consists in computer analysis of existing databases on the phenotypes and single nucleotide polymorphisms (SNPs) in inbred mouse strains and was termed in silico mapping. The power of this method has been studied by simulating quantitative traits controlled by one, two, or three genes. The results have demonstrated that the power of in silico mapping is high in the case of a monogenic trait. The probability of mapping all genes determining a digenic or, especially, trigenic trait is low. If two or three genes make equal phenotypic contributions to a trait, the proportions of experiments where none of them is localized are 17 and 25%, respectively. In the case of a major gene effect, when the phenotypic contribution of one gene considerably exceeds those of the other genes, the probability to map the major gene is 0.95 and 0.80 for the digenic and trigenic models, respectively. This shows that, in the case of polygenic control, the new method could localize only the genes with major effects, while most genes involved in the control of the trait would not be mapped.     

Mapping and manipulating quantitative traits in maize

Maize has been used effectively as a model organism in the development and evaluation of molecular markers for the identification, mapping and manipulation of major genes affecting the expression of quantitative traits in plants. Although quantitative geneticists have recognized the possibility of major loci, the general dogma bad emerged that quantitative traits were controlled by many loci, each with a small effect. This interpretation sent a signal to the molecular biologist not to bother with quantitative traits because it would be essentially impossible to isolate a gene responsible for the trait. Recent results from numerous mapping studies have shown that quantitative traits are controlled by, at least some, factors with major effects, and have given credibility to the conclusion that major loci exist and that one might be able to study them. Positive results from marker-facilitated selection and introgression studies have further strengthened this conclusion.     

Sequential elimination of major-effect contributors identifies additional quantitative trait loci conditioning high-temperature growth in yeast

Several quantitative trait loci (QTL) mapping strategies can successfully identify major-effect loci, but often have poor success detecting loci with minor effects, potentially due to the confounding effects of major loci, epistasis, and limited sample sizes. To overcome such difficulties, we used a targeted backcross mapping strategy that genetically eliminated the effect of a previously identified major QTL underlying high-temperature growth (Htg) in yeast. This strategy facilitated the mapping of three novel QTL contributing to Htg of a clinically derived yeast strain. One QTL, which is linked to the previously identified major-effect QTL, was dissected, and NCS2 was identified as the causative gene. The interaction of the NCS2 QTL with the first major-effect QTL was background dependent, revealing a complex QTL architecture spanning these two linked loci. Such complex architecture suggests that more genes than can be predicted are likely to contribute to quantitative traits. The targeted backcrossing approach overcomes the difficulties posed by sample size, genetic linkage, and epistatic effects and facilitates identification of additional alleles with smaller contributions to complex traits.     

High-resolution quantitative trait locus mapping reveals sign epistasis controlling ovariole number between two Drosophila species

Identifying the genes underlying genetically complex traits is of fundamental importance for medicine, agriculture, and evolutionary biology. However, the level of resolution offered by traditional quantitative trait locus (QTL) mapping is usually coarse. We analyze here a trait closely related to fitness, ovariole number. Our initial interspecific mapping between Drosophila sechellia (8 ovarioles/ovary) and D. simulans (15 ovarioles/ovary) identified a major QTL on chromosome 3 and a minor QTL on chromosome 2. To refine the position of the major QTL, we selected 1038 additional recombinants in the region of interest using flanking morphological markers (selective phenotyping). This effort generated approximately one recombination event per gene and increased the mapping resolution by approximately seven times. Our study thus shows that using visible markers to select for recombinants can efficiently increase the resolution of QTL mapping. We resolved the major QTL into two epistatic QTL, QTL3a and QTL3b. QTL3a shows sign epistasis: it has opposite effects in two different genetic backgrounds, the presence vs. the absence of the QTL3b D. sechellia allele. This property of QTL3a allows us to reconstruct the probable order of fixation of the QTL alleles during evolution.     

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.     

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.     

Pleiotropy, epistasis and new QTL: the genetic architecture of honey bee foraging behavior

The regulation of division of labor in social insects, particularly in the honey bee (Apis mellifera L.), has received considerable attention from a number of biological subdisciplines, including quantitative and behavioral genetics, because of the high complexity of the behavioral traits involved. The foraging choices of honey bee workers can be accurately quantified, and previous studies have made the foraging behavior of honey bees one of the best studied naturally occurring behavioral phenotypes. Three quantitative trait loci (QTL) have been identified that influence a set of foraging variables, including the concentration of nectar collected and the amount of pollen and nectar brought back to the hive. This study extends previous genetic investigations and represents the most comprehensive investigation of the genetic architecture of these foraging variables. We examined the effects of markers for the three established QTL and for one further candidate gene (Amfor), in two reciprocal backcross populations. These populations were also used to carry out two new QTL mapping studies, with over 400 Amplified Fragment Length Polymorphism (AFLP) markers in each. We detected a variety of effects of the genetic markers for the established QTL and the candidate gene, which were mostly epistatic in nature. A few new QTL could be detected with a variety of mapping techniques. Our results add complexity to the genetic architecture of the foraging behavior of the honey bee. Specifically, we support the hypotheses that pln1, pln2, pln3, and Amfor are involved in the regulation of foraging behavior in the honey bee and add some new factors that deserve further study in the future.     

Genetic Regulation of Bone Metabolism in the Chicken: Similarities and Differences to Mammalian Systems

Birds have a unique bone physiology, due to the demands placed on them through egg production. In particular their medullary bone serves as a source of calcium for eggshell production during lay and undergoes continuous and rapid remodelling. We take advantage of the fact that bone traits have diverged massively during chicken domestication to map the genetic basis of bone metabolism in the chicken. We performed a quantitative trait locus (QTL) and expression QTL (eQTL) mapping study in an advanced intercross based on Red Junglefowl (the wild progenitor of the modern domestic chicken) and White Leghorn chickens. We measured femoral bone traits in 456 chickens by peripheral computerised tomography and femoral gene expression in a subset of 125 females from the cross with microarrays. This resulted in 25 loci for female bone traits, 26 loci for male bone traits and 6318 local eQTL loci. We then overlapped bone and gene expression loci, before checking for an association between gene expression and trait values to identify candidate quantitative trait genes for bone traits. A handful of our candidates have been previously associated with bone traits in mice, but our results also implicate unexpected and largely unknown genes in bone metabolism. In summary, by utilising the unique bone metabolism of an avian species, we have identified a number of candidate genes affecting bone allocation and metabolism. These findings can have ramifications not only for the understanding of bone metabolism genetics in general, but could also be used as a potential model for osteoporosis as well as revealing new aspects of vertebrate bone regulation or features that distinguish avian and mammalian bone.     

从QTL到QTG的路还有多远?

植物大多数重要的经济性状都是数量性状, 人们对许多植物进行了数量性状基因座(QTL)的研究, 并取得了长足的发展。文章详尽地分析了数量性状表型与基因型的复杂关系, 介绍了当前QTL研究领域里的几种精细作图策略。讨论了当前挖掘控制目标性状QTL基因的研究过程中存在的困难和问题, 提出几个有待发展的研究方向, 并展望了该领域的发展前景。因目前的QTL仍然是一个相当大的染色体区段, 往往含有多个候选基因。文章就怎样从QTL粗放位点研究进一步发展到数量性状基因(quantitative trait gene, QTG)水平上的变异, 再从QTG到相应于基因内多态性的数量性状核苷酸(quantitative trait nucleotides, QTN), 提出了一些见解。来迎接后基因组时代数量遗传领域的挑战。     

Dimension reduction for mapping mRNA abundance as quantitative traits

The advent of sophisticated genomic techniques for gene mapping and microarray analysis has provided opportunities to map mRNA abundance to quantitative trait loci (QTL) throughout the genome. Unfortunately, simple mapping of each individual mRNA trait on the scale of a typical microarray experiment is computationally intensive, subject to high sample variance, and therefore underpowered. However, this problem can be addressed by capitalizing on correlation among the large number of mRNA traits. We present a method to reduce the dimensionality for mapping gene expression data as quantitative traits. We used a blind method, principal components, and a sighted method, hierarchical clustering seeded by disease relevant traits, to define new traits composed of a small collection of promising mRNAs. We validated the principle of our approach by mapping the expression levels of metabolism genes in a population of F(2)-ob/ob mice derived from the BTBR and C57BL/6J strains. We found that lipogenic and gluconeogenic mRNAs, which are known targets of insulin action, were closely associated with the insulin trait. Multiple interval mapping and Bayesian interval mapping of this new trait revealed significant linkages to chromosome regions that were contained in loci associated with type 2 diabetes in this same mouse sample. As a further statistical refinement, we show that principal component analysis also effectively reduced dimensions for mapping phenotypes composed of mRNA abundances.     

Chaser (Csr), a New Gene Affecting Larval Foraging Behavior in Drosophila Melanogaster

Chaser (Csr) was uncovered in a gamma mutagenesis screen to identify genes that modify the larval foraging behavior of sitters to rovers. Rover larvae have significantly longer path lenghts than sitters while foraging on a yeast and water paste. This difference is influenced by one major gene, foraging (for), which has two naturally occurring alleles, for(R) (rover) and for(s) (sitter). In a mutagenesis screen for modifiers of for, we identified three lines with viable mutations on chromosome 3 that alter foraging behavior. Each of these mutations increased larval path lengths in for(s)/for(s) larvae in a dominant fashion, and were not separable by recombination. These mutations are therefore probably allelic and define a new gene that we have called Csr. Csr was genetically localized using the lethal-tagging technique. This technique resulted in seven lines with a significant decrease in larval path-length and recessive lethal mutations on chromosome 3. We refer to these as reverted Csr (Csr(rv)) lines. Deficiencies that uncovered cytologically visible chromosome rearrangements in three of the seven reverted lines were used in a complementation analysis. In this way we mapped the lethal mutations in the Csr(rv) lines to cytological region 95F7-96A1 on the right arm of chromosome 3.     

Multivariate segregation analysis for quantitative traits in line crosses

Segregation analysis is a method of detecting major genes for quantitative traits without using marker information. It serves as an important tool in helping investigators to plan further studies such as quantitative trait loci mapping or more sophisticated genomic analyses. However, current methods of segregation analysis for a single trait typically have low statistical power. We propose a multivariate segregation analysis (MSA) that takes advantage of the correlation structure of multiple quantitative traits to detect major genes. This method not only increases the statistical power, but allows dissection of the genetic architecture underlying the trait complex. In MSA the observed phenotypes of multiple correlated traits are fitted to a multivariate Gaussian mixture model. Model parameters are estimated under the maximum likelihood framework via the expectation-maximization algorithm. The presence of major genes is tested using likelihood ratio test statistics. Pleiotropy is distinguished from close linkage by comparing three possible models using the Bayesian information criterion. Two simulation experiments were performed based on the F(2) mating design. In the first, the statistical properties of MSA under varying heritabilities and sample sizes were investigated and the results compared with those obtained from single-trait analysis. In the second simulation the efficacy of MSA in separating pleiotropy from close linkage was demonstrated. Finally, the new method was applied to real data and detected a major gene responsible for both plant height and tiller number in rice.     

Major regulatory genes in maize contribute to standing variation in teosinte (Zea mays ssp. parviglumis)

In plants, many major regulatory genes that control plant growth and development have been identified and characterized. Despite a detailed knowledge of the function of these genes little is known about how they contribute to the natural variation for complex traits. To determine whether major regulatory genes of maize contribute to standing variation in Balsas teosinte we conducted association mapping in 584 Balsas teosinte individuals. We tested 48 markers from nine candidate regulatory genes against 13 traits for plant and inflorescence architecture. We identified significant associations using a mixed linear model that controls for multiple levels of relatedness. Ten associations involving five candidate genes were significant after correction for multiple testing, and two survive the conservative Bonferroni correction. zfl2, the maize homolog of FLORICAULA of Antirrhinum, was associated with plant height. zap1, the maize homolog of APETALA1 of Arabidopsis, was associated with inflorescence branching. Five SNPs in the maize domestication gene, teosinte branched1, were significantly associated with either plant or inflorescence architecture. Our data suggest that major regulatory genes in maize do play a role in the natural variation for complex traits in teosinte and that some of the minor variants we identified may have been targets of selection during domestication.     

Phenotypic variation and natural selection at catsup, a pleiotropic quantitative trait gene in Drosophila

Quantitative traits are shaped by networks of pleiotropic genes . To understand the mechanisms that maintain genetic variation for quantitative traits in natural populations and to predict responses to artificial and natural selection, we must evaluate pleiotropic effects of underlying quantitative trait genes and define functional allelic variation at the level of quantitative trait nucleotides (QTNs). Catecholamines up (Catsup), which encodes a negative regulator of tyrosine hydroxylase , the rate-limiting step in the synthesis of the neurotransmitter dopamine, is a pleiotropic quantitative trait gene in Drosophila melanogaster. We used association mapping to determine whether the same or different QTNs at Catsup are associated with naturally occurring variation in multiple quantitative traits. We sequenced 169 Catsup alleles from a single population and detected 33 polymorphisms with little linkage disequilibrium (LD). Different molecular polymorphisms in Catsup are independently associated with variation in longevity, locomotor behavior, and sensory bristle number. Most of these polymorphisms are potentially functional variants in protein coding regions, have large effects, and are not common. Thus, Catsup is a pleiotropic quantitative trait gene, but individual QTNs do not have pleiotropic effects. Molecular population genetic analyses of Catsup sequences are consistent with balancing selection maintaining multiple functional polymorphisms.     

Quantitative trait loci for floral morphology in Arabidopsis thaliana

A central question in biology is how genes control the expression of quantitative variation. We used statistical methods to estimate genetic variation in eight Arabidopsis thaliana floral characters (fresh flower mass, petal length, petal width, sepal length, sepal width, long stamen length, short stamen length, and pistil length) in a cosmopolitan sample of 15 ecotypes. In addition, we used genome-wide quantitative trait locus (QTL) mapping to evaluate the genetic basis of variation in these same traits in the Landsberg erecta x Columbia recombinant inbred line population. There was significant genetic variation for all traits in both the sample of naturally occurring ecotypes and in the Ler x Col recombinant inbred line population. In addition, broad-sense genetic correlations among the traits were positive and high. A composite interval mapping (CIM) analysis detected 18 significant QTL affecting at least one floral character. Eleven QTL were associated with several floral traits, supporting either pleiotropy or tight linkage as major determinants of flower morphological integration. We propose several candidate genes that may underlie these QTL on the basis of positional information and functional arguments. Genome-wide QTL mapping is a promising tool for the discovery of candidate genes controlling morphological development, the detection of novel phenotypic effects for known genes, and in generating a more complete understanding of the genetic basis of floral development.     

An integrative approach for the identification of quantitative trait loci

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.