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.     

Genetic Mapping of Quantitative Trait Loci Controlling Growth and Wood Quality Traits in Eucalyptus Grandis Using a Maternal Half-Sib Family and Rapd Markers

Quantitative trait loci (QTL) mapping of forest productivity traits was performed using an open pollinated half-sib family of Eucalyptus grandis. For volume growth, a sequential QTL mapping approach was applied using bulk segregant analysis (BSA), selective genotyping (SG) and cosegregation analysis (CSA). Despite the low heritability of this trait and the heterogeneous genetic background employed for mapping. BSA detected one putative QTL and SG two out of the three later found by CSA. The three putative QTL for volume growth were found to control 13.7% of the phenotypic variation, corresponding to an estimated 43.7% of the genetic variation. For wood specific gravity five QTL were identified controlling 24.7% of the phenotypic variation corresponding to 49% of the genetic variation. Overlapping QTL for CBH, WSG and percentage dry weight of bark were observed. A significant case of digenic epistasis was found, involving unlinked QTL for volume. Our results demonstrate the applicability of the within half-sib design for QTL mapping in forest trees and indicate the existence of major genes involved in the expression of economically important traits related to forest productivity in Eucalyptus grandis. These findings have important implications for marker-assisted tree breeding.     

水稻对叶瘟和穗瘟部分抗性的遗传分析

在一个水稻籼籼交重组自交系群体中,选用由感病株系构成的2个亚群体和2个不同的稻瘟病菌小种,进行了水稻对叶瘟部分抗性的QTL定位,还选用由感病而且抽穗期相近的株系构成的亚群体和另一个病菌小种,进行了水稻对穗瘟部分抗性的QTL定位,将病叶面积百分比(DLA)、病斑大小(LS)和病斑数(LN)作为对叶瘟部分抗性的性状,将病斑长度(LL)和孢子量(CA)作为对穗瘟部分抗性的性状。所构建的图谱包含168个标记。应用QTLMapper 1．01b,共检测到11个表现主效应的QTL和28对双因子互作,有3个表现主效应的QTL参与对同一性状的互作。QTL的主效应对单一性状的贡献率为4．7％～38．8％,而上位性效应对单一性状的贡献率为16．0％～51．7％,QTL的主效应对大多数性状的贡献率小于互作效应,表明互作效应对于部分抗性的重要作用。对穗瘟部分抗性的两个性状LL和CA,所检测到QTL总效应的贡献率分别达到70．6％和82．6％,表明由排除了主效抗病基因的感病株系组成的亚群体适合于进行部分抗性QTL定位。     

Use of a dense single nucleotide polymorphism map for in silico mapping in the mouse

Rapid expansion of available data, both phenotypic and genotypic, for multiple strains of mice has enabled the development of new methods to interrogate the mouse genome for functional genetic perturbations. In silico mapping provides an expedient way to associate the natural diversity of phenotypic traits with ancestrally inherited polymorphisms for the purpose of dissecting genetic traits. In mouse, the current single nucleotide polymorphism (SNP) data have lacked the density across the genome and coverage of enough strains to properly achieve this goal. To remedy this, 470,407 allele calls were produced for 10,990 evenly spaced SNP loci across 48 inbred mouse strains. Use of the SNP set with statistical models that considered unique patterns within blocks of three SNPs as an inferred haplotype could successfully map known single gene traits and a cloned quantitative trait gene. Application of this method to high-density lipoprotein and gallstone phenotypes reproduced previously characterized quantitative trait loci (QTL). The inferred haplotype data also facilitates the refinement of QTL regions such that candidate genes can be more easily identified and characterized as shown for adenylate cyclase 7.     

Genetic analysis of the short-stem populations of rye (Secale cereale L.) for the plant height trait

The process and results of genetic analysis of the short-stem rye populations (Secale cereale L.) for the trait "plant height" are described. The analysis has been performed with the joint scaling test using data of estimation of the parental and hybrid populations developed in the cyclic reciprocal crosses between four initial populations. Monogenic, digenic, and trigenic differences between parental rye populations for two genes of incomplete dominance and one multiallelic gene with additive allelic interaction were determined.     

Linkage disequilibrium mapping of quantitative trait loci under truncation selection

As a dense map of single nucleotide polymorphism (SNP) markers are available, population-based linkage disequilibrium (LD) mapping or association study is becoming one of the major tools for identifying quantitative trait loci (QTL) and for fine gene mapping. However, in many cases, LD between the marker and trait locus is not very strong. Approaches that maximize the potential of detecting LD will be essential for the success of LD mapping of QTL. In this paper, we propose two strategies for increasing the probability of detecting LD: (1) phenotypic selection and (2) haplotype LD mapping. To provide the foundations for LD mapping of QTL under selection, we develop analytic tools for assessing the impact of phenotypic selection on allele and haplotype frequencies, and LD under three trait models: single trait locus, two unlinked trait loci, and two linked trait loci with or without epistasis. In addition to a traditional chi(2) test, which compares the difference in allele or haplotype frequencies in the selected sample and population sample, we present multiple regression methods for LD mapping of QTL, and investigate which methods are effective in employing phenotypic selection for QTL mapping. We also develop a statistical framework for investigating and comparing the power of the single marker and multilocus haplotype test for LD mapping of QTL. Finally, the proposed methods are applied to mapping QTL influencing variation in systolic blood pressure in an isolated Chinese population.     

Do large effect QTL fractionate? A case study at the maize domestication QTL teosinte branched1

Quantitative trait loci (QTL) mapping is a valuable tool for studying the genetic architecture of trait variation. Despite the large number of QTL studies reported in the literature, the identified QTL are rarely mapped to the underlying genes and it is usually unclear whether a QTL corresponds to one or multiple linked genes. Similarly, when QTL for several traits colocalize, it is usually unclear whether this is due to the pleiotropic action of a single gene or multiple linked genes, each affecting one trait. The domestication gene teosinte branched1 (tb1) was previously identified as a major domestication QTL with large effects on the differences in plant and ear architecture between maize and teosinte. Here we present the results of two experiments that were performed to determine whether the single gene tb1 explains all trait variation for its genomic region or whether the domestication QTL at tb1 fractionates into multiple linked QTL. For traits measuring plant architecture, we detected only one QTL per trait and these QTL all mapped to tb1. These results indicate that tb1 is the sole gene for plant architecture traits that segregates in our QTL mapping populations. For most traits related to ear morphology, we detected multiple QTL per trait in the tb1 genomic region, including a large effect QTL at tb1 itself plus one or two additional linked QTL. tb1 is epistatic to two of these additional QTL for ear traits. Overall, these results provide examples for both a major QTL that maps to a single gene, as well as a case in which a QTL fractionates into multiple linked QTL.     

The genetic architecture of complex traits in teosinte (Zea mays ssp. parviglumis): new evidence from association mapping

Previous association analyses showed that variation at major regulatory genes contributes to standing variation for complex traits in Balsas teosinte, the progenitor of maize. This study expands our previous association mapping effort in teosinte by testing 123 markers in 52 candidate genes for association with 31 traits in a population of 817 individuals. Thirty-three significant associations for markers from 15 candidate genes and 10 traits survive correction for multiple testing. Our analyses suggest several new putative causative relationships between specific genes and trait variation in teosinte. For example, two ramosa genes (ra1 and ra2) associate with ear structure, and the MADS-box gene, zagl1, associates with ear shattering. Since zagl1 was previously shown to be a target of selection during maize domestication, we suggest that this gene was under selection for its effect on the loss of ear shattering, a key domestication trait. All observed effects were relatively small in terms of the percentage of phenotypic variation explained (<10%). We also detected several epistatic interactions between markers in the same gene that associate with the same trait. Candidate-gene-based association mapping appears to be a promising method for investigating the inheritance of complex traits in teosinte.     

Exact multipoint quantitative-trait linkage analysis in pedigrees by variance components

Methods based on variance components are powerful tools for linkage analysis of quantitative traits, because they allow simultaneous consideration of all pedigree members. The central idea is to identify loci making a significant contribution to the population variance of a trait, by use of allele-sharing probabilities derived from genotyped marker loci. The technique is only as powerful as the methods used to infer these probabilities, but, to date, no implementation has made full use of the inheritance information in mapping data. Here we present a new implementation that uses an exact multipoint algorithm to extract the full probability distribution of allele sharing at every point in a mapped region. At each locus in the region, the program fits a model that partitions total phenotypic variance into components due to environmental factors, a major gene at the locus, and other unlinked genes. Numerical methods are used to derive maximum-likelihood estimates of the variance components, under the assumption of multivariate normality. A likelihood-ratio test is then applied to detect any significant effect of the hypothesized major gene. Simulations show the method to have greater power than does traditional sib-pair analysis. The method is freely available in a new release of the software package GENEHUNTER.     

Using Association Mapping in Teosinte to Investigate the Function of Maize Selection-Candidate Genes

Background

Large-scale screens of the maize genome identified 48 genes that show the putative signature of artificial selection during maize domestication or improvement. These selection-candidate genes may act as quantitative trait loci (QTL) that control the phenotypic differences between maize and its progenitor, teosinte. The selection-candidate genes appear to be located closer in the genome to domestication QTL than expected by chance.

Methods and Findings

As a step toward defining the traits controlled by these genes, we performed phenotype-genotype association mapping in teosinte for 32 of the 48 plus three other selection-candidate genes. Our analyses assayed 32 phenotypic traits, many of which were altered during maize domestication or improvement. We observed several significant associations between SNPs in the selection-candidate genes and trait variation in teosinte. These included two associations that surpassed the Bonferroni correction and five instances where a gene significantly associated with the same trait in both of our association mapping panels. Despite these significant associations, when compared as a group the selection-candidate genes performed no better than randomly chosen genes.

Conclusions

Our results suggest association analyses can be helpful for identifying traits under the control of selection-candidate genes. Indeed, we present evidence for new functions for several selection-candidate genes. However, with the current set of selection-candidate genes and our association mapping strategy, we found very few significant associations overall and no more than we would have found with randomly chosen genes. We discuss possible reasons that a large number of significant genotype-phenotype associations were not discovered.     

Effect of gene conversion on variances of digenic identity measures

The variances and covariances of digenic descent measures are studied for a two-locus model incorporating mutation, gene conversion, recombination, drift, and finite sampling. Gene conversion can occur between allelic pairs of genes or between non-allelic pairs on the same or different gametes within individuals. Most interest therefore centers on pairs of genes, and five digenic identity measures are required. The behavior over time of these measures is studied, with an emphasis on the effects of gene conversion. Because of the stochastic nature of the forces of drift, recombination, mutation, and conversion, the actual identity status of gene pairs can vary from expectation among replicate populations. To study this variation we compute the expected variances and covariances of the measures, and show that this requires the introduction of trigenic and quadrigenic measures. Allowing for conversion between genes on different gametes requires a large number of these higher-order measures.     

An integrated in silico gene mapping strategy in inbred mice

In recent years in silico analysis of common laboratory mice has been introduced and subsequently applied, in slightly different ways, as a methodology for gene mapping. Previously we have demonstrated some limitation of the methodology due to sporadic genetic correlations across the genome. Here, we revisit the three main aspects that affect in silico analysis. First, we report on the use of marker maps: we compared our existing 20,000 SNP map to the newly released 140,000 SNP map. Second, we investigated the effect of varying strain numbers on power to map QTL. Third, we introduced a novel statistical approach: a cladistic analysis, which is well suited for mouse genetics and has increased flexibility over existing in silico approaches. We have found that in our examples of complex traits, in silico analysis by itself does fail to uniquely identify quantitative trait gene (QTG)-containing regions. However, when combined with additional information, it may significantly help to prioritize candidate genes. We therefore recommend using an integrated work flow that uses other genomic information such as linkage regions, regions of shared ancestry, and gene expression information to obtain a list of candidate genes from the genome.     

Genetic diversity and population structure of a diverse set of rice germplasm for association mapping

Germplasm diversity is the mainstay for crop improvement and genetic dissection of complex traits. Understanding genetic diversity, population structure, and the level and distribution of linkage disequilibrium (LD) in target populations is of great importance and a prerequisite for association mapping. In this study, 100 genome-wide simple sequence repeat (SSR) markers were used to assess genetic diversity, population structure, and LD of 416 rice accessions including landraces, cultivars and breeding lines collected mostly in China. A model-based population structure analysis divided the rice materials into seven subpopulations. 63% of the SSR pairs in these accessions were in LD, which was mostly due to an overall population structure, since the number of locus pairs in LD was reduced sharply within each subpopulation, with the SSR pairs in LD ranging from 5.9 to 22.9%. Among those SSR pairs showing significant LD, the intrachromosomal LD had an average of 25–50 cM in different subpopulations. Analysis of the phenotypic diversity of 25 traits showed that the population structure accounted for an average of 22.4% of phenotypic variation. An example association mapping for starch quality traits using both the candidate gene mapping and genome-wide mapping strategies based on the estimated population structure was conducted. Candidate gene mapping confirmed that the Wx and starch synthase IIa (SSIIa) genes could be identified as strongly associated with apparent amylose content (AAC) and pasting temperature (PT), respectively. More importantly, we revealed that the Wx gene was also strongly associated with PT. In addition to the major genes, we found five and seven SSRs were associated with AAC and PT, respectively, some of which have not been detected in previous linkage mapping studies. The results suggested that the population may be useful for the genome-wide marker–trait association mapping. This new association population has the potential to identify quantitative trait loci (QTL) with small effects, which will aid in dissecting complex traits and in exploiting the rich diversity present in rice germplasm.     

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.     

From QTL to QTN: Candidate Gene Set Approach and a Case Study in Porcine IGF1-FoxO Pathway

Unraveling the genetic background of economic traits is a major goal in modern animal genetics and breeding. Both candidate gene analysis and QTL mapping have previously been used for identifying genes and chromosome regions related to studied traits. However, most of these studies may be limited in their ability to fully consider how multiple genetic factors may influence a particular phenotype of interest. If possible, taking advantage of the combined effect of multiple genetic factors is expected to be more powerful than analyzing single sites, as the joint action of multiple loci within a gene or across multiple genes acting in the same gene set will likely have a greater influence on phenotypic variation. Thus, we proposed a pipeline of gene set analysis that utilized information from multiple loci to improve statistical power. We assessed the performance of this approach by both simulated and a real IGF1-FoxO pathway data set. The results showed that our new method can identify the association between genetic variation and phenotypic variation with higher statistical power and unravel the mechanisms of complex traits in a point of gene set. Additionally, the proposed pipeline is flexible to be extended to model complex genetic structures that include the interactions between different gene sets and between gene sets and environments.     

Phenotyping approaches for physiological breeding and gene discovery in wheat

Conceptual models of drought‐adaptive traits have been used in breeding to accumulate complementary physiological traits (PT) in selected progeny, resulting in distribution of advanced lines to rain‐fed environments worldwide by the International Maize and Wheat Improvement Center (CIMMYT). Key steps in PT breeding at CIMMYT include characterisation of crossing block lines for stress adaptive mechanisms, strategic crossing among parents that encompass as many target traits as possible and early generation selection (EGS) of bulks for canopy temperature (CT). The approach has been successful using both elite × elite crosses as well as three way crosses involving stress adapted landraces. Other EGS techniques that are amenable to high throughput include measurement of spectral reflectance indices and stomatal aperture‐related traits. Their genetic‐ and cost‐effectiveness are supported by realisation of genetic yield gains in response to trait selection, and by economic analysis, respectively. Continual reselection within restricted gene pools is likely to lead to diminishing returns, however, exotic parents can be used to introduce new allelic diversity. Examples include landraces from the primary gene pool, and products of inter‐specific hybridisation with the secondary gene pool consisting of closely related wheat genomes. Both approaches have been successful in introducing stress‐adaptive traits. The main problem with knowing which genetic resource to use in wide‐crossing is the uncertainty with which phenotypic expression can be extrapolated from one genome/genepool to another because of their unimproved or undomesticated genetic backgrounds. Nonetheless, their PT expression can be measured and used as a basis for investing in crossing or wide crossing. Discovering the genetic basis of PT is highly complex because putative QTLs may interact with environment and genetic background, including genes of major effect. Detection of QTLs was improved in mapping populations where flowering time was controlled, while new mapping populations have been designed by screening potential parents that do not contrast in the Rht, Ppd and Vrn alleles. Association genetics mapping is another approach that can be employed for gene discovery using exclusively agronomically improved material, thereby minimising the probability of identifying yield QTLs whose alleles have been already improved by conventional breeding.     

A genetical genomics approach to genome scans increases power for QTL mapping

We describe a method for integrating gene expression information into genome scans and show that this can substantially increase the statistical power of QTL mapping. The method has three stages. First, standard clustering methods identify small (size 5-20) groups of genes with similar expression patterns. Second, each gene group is tested for a causative genetic locus shared with the clinical trait of interest. This is done using an EM algorithm approach that treats genotype at the putative causative locus as an unobserved variable and combines expression information from all of the genes in the group to infer genotype information at the locus. Finally, expression QTL (eQTL) are mapped for each gene group that shares a causative locus with the clinical trait. Such eQTL are candidates for the causative locus. Simulation results show that this method has far superior power to standard QTL mapping techniques in many circumstances. We applied this method to existing data on mouse obesity. Our method identified 27 putative body weight QTL, whereas standard QTL mapping produced only one. Furthermore, most gene groups with body weight QTL included cis genes, so candidate genes could be immediately identified. Eleven body weight QTL produced 16 candidate genes that have been previously associated with body weight or body weight-related traits, thus validating our method. In addition, 15 of the 16 other loci produced 32 candidate genes that have not been associated with body weight. Thus, this method shows great promise for finding new causative loci for complex traits.     

Whole-Genome Quantitative Trait Locus Mapping Reveals Major Role of Epistasis on Yield of Rice

Although rice yield has been doubled in most parts of the world since 1960s, thanks to the advancements in breeding technologies, the biological mechanisms controlling yield are largely unknown. To understand the genetic basis of rice yield, a number of quantitative trait locus (QTL) mapping studies have been carried out, but whole-genome QTL mapping incorporating all interaction effects is still lacking. In this paper, we exploited whole-genome markers of an immortalized F₂ population derived from an elite rice hybrid to perform QTL mapping for rice yield characterized by yield per plant and three yield component traits. Our QTL model includes additive and dominance main effects of 1,619 markers and all pair-wise interactions, with a total of more than 5 million possible effects. The QTL mapping identified 54, 5, 28 and 4 significant effects involving 103, 9, 52 and 7 QTLs for the four traits, namely the number of panicles per plant, the number of grains per panicle, grain weight, and yield per plant. Most identified QTLs are involved in digenic interactions. An extensive literature survey of experimentally characterized genes related to crop yield shows that 19 of 54 effects, 4 of 5 effects, 12 of 28 effects and 2 of 4 effects for the four traits, respectively, involve at least one QTL that locates within 2 cM distance to at least one yield-related gene. This study not only reveals the major role of epistasis influencing rice yield, but also provides a set of candidate genetic loci for further experimental investigation.