Detection of genes for ordinal traits in nuclear families and a unified approach for association studies

There is growing interest in genomewide association analysis using single-nucleotide polymorphisms (SNPs), because traditional linkage studies are not as powerful in identifying genes for common, complex diseases. Tests for linkage disequilibrium have been developed for binary and quantitative traits. However, since many human conditions and diseases are measured in an ordinal scale, methods need to be developed to investigate the association of genes and ordinal traits. Thus, in the current report we propose and derive a score test statistic that identifies genes that are associated with ordinal traits when gametic disequilibrium between a marker and trait loci exists. Through simulation, the performance of this new test is examined for both ordinal traits and quantitative traits. The proposed statistic not only accommodates and is more powerful for ordinal traits, but also has similar power to that of existing tests when the trait is quantitative. Therefore, our proposed statistic has the potential to serve as a unified approach to identifying genes that are associated with any trait, regardless of how the trait is measured. We further demonstrated the advantage of our test by revealing a significant association (P = 0.00067) between alcohol dependence and a SNP in the growth-associated protein 43.     

Estimating the power of a proposed linkage study for a complex genetic trait.

Many genetic traits have complex modes of inheritance; they may exhibit incomplete or age-dependent penetrance or fail to show any clear Mendelian inheritance pattern. As primary linkage maps for the human genome near completion, it is becoming increasingly possible to map these traits. Prior to undertaking a linkage study, it is important to consider whether the pedigrees available for the proposed study are likely to provide sufficient information to demonstrate linkage, assuming a linked marker is tested. In the current paper, we describe a computer simulation method to estimate the power of a proposed study to detect linkage for a complex genetic trait, given a hypothesized genetic model for the trait. Our method simulates trait locus genotypes consistent with observed trait phenotypes, in such a way that the probability to detect linkage can be estimated by sample statistics of the maximum lod score distribution. The method uses terms available when calculating the likelihood of the trait phenotypes for the pedigree and is applicable to any trait determined by one or a few genetic loci; individual-specific environmental effects can also be dealt with. Our method provides an objective answer to the question, Will these pedigrees provide sufficient information to map this complex genetic trait?     

Molecular approaches in pig breeding to improve meat quality.

This article reviews the advances in molecular genetics that have led to the identification of genes and markers associated with meat quality in pig. The development of a considerable number of annotated livestock genome sequences represents an incredibly rich source of information that can be used to identify candidate genes responsible for complex traits and quantitative trait loci effects. In pig, the huge amount of information emerging from the study of the genome has helped in the acquisition of new knowledge concerning biological systems and it is opening new opportunities for the genetic selection of this specie. Among the new fields of genomics recently developed, functional genomics and proteomics that allow considering many genes and proteins at the same time are very useful tools for a better understanding of the function and regulation of genes, and how these participate in complex networks controlling the phenotypic characteristics of a trait. In particular, global gene expression profiling at the mRNA and protein level can provide a better understanding of gene regulation that underlies biological functions and physiology related to the delivery of a better pig meat quality. Moreover, the possibility to realize an integrated approach of genomics and proteomics with bioinformatics tools is essential to obtain a complete exploitation of the available molecular genetics information. The development of this knowledge will benefit scientists, industry and breeders considering that the efficiency and accuracy of the traditional pig selection schemes will be improved by the implementation of molecular data into breeding programs.     

Functional association between malting quality trait components and cDNA array based expression patterns in barley (Hordeum vulgare L.)

We developed an approach for relating differences in gene expression to the phenotypic variation of a trait of interest. This allows the identification of candidate genes for traits that display quantitative variation. To validate the principle, gene expression was monitored on a cDNA array with 1400 ESTs to identify genes involved in the variation of the complex trait malting quality in barley. RNA profiles were monitored during grain germination in a set of 10 barley genotypes that had been characterized for 6 quality-associated trait components. The selection of the candidate genes was achieved via a correlation of dissimilarity matrices that were based on (i) trait variation and (ii) gene expression data. As expected, a comparison based on the complete set of differentially-expressed genes did not reveal any correlation between the matrices, because not all genes that show differential expression between the 10 cultivars are responsible for the observed differences in malting quality. However, by iteratively taking out one gene (with replacement) and re-computing the correlation, those genes that are positively contributing to the correlation could be identified. Using this procedure between 17 and 30 candidate genes were identified for each of the six malting parameters analysed. In addition to genes of unknown function, the list of candidates contains well-known malting-related genes. Five out of eight mapped candidate genes display linkage to known QTLs for malting quality traits. The described functional association strategy may provide an efficient link between functional genomics and plant 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.     

Gene Structure Prediction Using an Orthologous Gene of Known Exon-Intron Structure

Given the availability of complete genome sequences from related organisms, sequence conservation can provide important clues for predicting gene structure. In particular, one should be able to leverage information about known genes in one species to help determine the structures of related genes in another. Such an approach is appealing in that high-quality gene prediction can be achieved for newly sequenced species, such as mouse and puffer fish, using the extensive knowledge that has been accumulated about human genes. This article reports a novel approach to predicting the exon-intron structures of mouse genes by incorporating constraints from orthologous human genes using techniques that have previously been exploited in speech and natural language processing applications. The approach uses a context-free grammar to parse a training corpus of annotated human genes. A statistical training procedure produces a weighted recursive transition network (RTN) intended to capture the general features of a mammalian gene. This RTN is expanded into a finite state transducer (FST) and composed with an FST capturing the specific features of the human orthologue. This model includes a trigram language model on the amino acid sequence as well as exon length constraints. A final stage uses the free software package ClustalW to align the top n candidates in the search space. For a set of 98 orthologous human-mouse pairs, we achieved 96% sensitivity and 97% specificity at the exon level on the mouse genes, given only knowledge gleaned from the annotated human genome.     

Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies

Linkage studies of complex traits frequently yield multiple linkage regions covering hundreds of genes. Testing each candidate gene from every region is prohibitively expensive and computational methods that simplify this process would benefit genetic research. We present a new method based on commonality of functional annotation (CFA) that aids dissection of complex traits for which multiple causal genes act in a single pathway or process. CFA works by testing individual Gene Ontology (GO) terms for enrichment among candidate gene pools, performs multiple hypothesis testing adjustment using an estimate of independent tests based on correlation of GO terms, and then scores and ranks genes annotated with significantly-enriched terms based on the number of quantitative trait loci regions in which genes bearing those annotations appear. We evaluate CFA using simulated linkage data and show that CFA has good power despite being conservative. We apply CFA to published linkage studies investigating age-of-onset of Alzheimer's disease and body mass index and obtain previously known and new candidate genes. CFA provides a new tool for studies in which causal genes are expected to participate in a common pathway or process and can easily be extended to utilize annotation schemes in addition to the GO.     

Genomics to tree breeding and forest health

Genomic discovery in forest trees follows paradigms from both agricultural crop and livestock improvement and human medicine. Forest trees in a domesticated state can be improved using genomic-based breeding technologies, whereas the health of trees in a natural and undomesticated state might be managed using those same technologies. These applications begin by first dissecting complex traits in trees to their individual gene components and for that the association genetics approach is quite powerful in trees. This is true for several reasons including large, random mating, and unstructured populations and the rapid decay of linkage disequilibrium in many tree species. Once marker by trait associations are discovered, they can be used in genomic-based breeding and forest health diagnostics. Initial studies in trees have found ample nucleotide diversity in candidate genes to perform association studies and single nucleotide polymorphisms have been associated with economic and adaptive traits. Population genetic neutrality tests have been applied to identify genes probably under natural selection and thus make good candidates for developing forest health diagnostic tools.     

Genome-wide search for new genes controlling plasma lipid concentrations in mice and humans

PURPOSE OF REVIEW: Quantitative trait locus analysis has been used in both humans and mice for the purpose of finding new genes regulating plasma lipid levels. We review these methods and discuss new approaches that can help find quantitative trait locus genes. RECENT FINDINGS: Many quantitative trait loci have been found that regulate plasma levels for HDL cholesterol (37 in mice and 30 in humans), LDL cholesterol (25 in mice and 20 in humans) and triglycerides (19 in mice and 30 in humans). Most of the human quantitative trait loci have concordant mouse quantitative trait loci mapping to homologous regions (93% for HDL cholesterol, 100% for LDL cholesterol and 80% for triglycerides), suggesting that many genes identified in mice may also regulate the same traits in humans. New approaches based on recently developed genomic and bioinformatic technologies and resources should greatly facilitate finding these genes. SUMMARY: New genes regulating plasma lipid levels can be found in mice and then tested in humans. Some of these genes could be potential therapeutic targets for human atherosclerosis.     

Procreative beneficence: why we should select the best children

Eugenic selection of embryos is now possible by employing in vitro fertilization (IVF) and preimplantation genetic diagnosis (PGD). While PGD is currently being employed for the purposes of detecting chromosomal abnormalities or inherited genetic abnormalities, it could in principle be used to test any genetic trait such as hair colour or eye colour.
Genetic research is rapidly progressing into the genetic basis of complex traits like intelligence and a gene has been identified for criminal behaviour in one family. Once the decision to have IVF is made, PGD has few 'costs' to couples, and people would be more inclined to use it to select less serious medical traits, such as a lower risk of developing Alzheimer Disease, or even for non-medical traits. PGD has already been used to select embryos of a desired gender in the absence of any history of sex-linked genetic disease.
I will argue that: (1) some non-disease genes affect the likelihood of us leading the best life; (2) we have a reason to use information which is available about such genes in our reproductive decision-making; (3) couples should select embryos or fetuses which are most likely to have the best life, based on available genetic information, including information about non-disease genes. I will also argue that we should allow selection for non-disease genes even if this maintains or increases social inequality. I will focus on genes for intelligence and sex selection.
I will defend a principle which I call Procreative Beneficence: couples (or single reproducers) should select the child, of the possible children they could have, who is expected to have the best life, or at least as good a life as the others, based on the relevant, available information.     

Heritability and identification of QTLs and underlying candidate genes associated with the architecture of the grapevine cluster (Vitis vinifera L.)

Key message

We have identified 19 QTLs for rachis architecture, a key and complex trait for grapevine production. Fifty out of 1,173 genes underlying these QTLs are candidates to be further explored.

Abstract

In the table grape industry, the rachis architecture has economic and management implications. Therefore, understanding the genetics of this trait is key for its breeding. The aim of this work was to identify genetic determinants of traits associated with the cluster architecture. Characterisations of eight traits was performed on a ‘Ruby Seedless’ × ‘Sultanina’ crossing (F₁: n = 137) during three seasons, with and without gibberellic acid (GA₃) applications. The genotypic effects and the genotype × GA₃ interactions were significant for several traits. Rachis length (rl), lateral shoulder length and node number along the central axis were the most prominent traits. On average, the heritability of these traits was ~71 %, with heritability of rl being 76 % as estimated under different seasons. Quantitative trait loci (QTLs) analyses showed that linkage group 5 (LG₅) and LG₁₈ harboured the largest number of QTLs for these traits. According to the variance explained, the main QTL (corresponding to rl) was found on LG₉. These QTLs were supported mainly by a paternal additive effect and revealed possible pleiotropic effects. Based on the grapevine reference genome, we identified 1,173 genes located under these QTL confidence intervals. Fifty of the 891 annotated genes of this list were selected for their further characterisation because of their possible participation in the rachis architecture. In conclusion, the QTLs detected indicate that these traits and their GA₃ responsiveness have a clear genetic basis. Due to the percentage of the total variance explained, they are good candidates to participate in the genetic determination of the cluster architecture.     

Association genetics of complex traits in plants

Association mapping is rapidly becoming the main method for dissecting the genetic architecture of complex traits in plants. Currently most association mapping studies in plants are preformed using sets of genes selected to be putative candidates for the trait of interest, but rapid developments in genomics will allow for genome-wide mapping in virtually any plant species in the near future. As the costs for genotyping are decreasing, the focus has shifted towards phenotyping. In plants, clonal replication and/or inbred lines allows for replicated phenotyping under many different environmental conditions. Reduced sequencing costs will increase the number of studies that use RNA sequencing data to perform expression quantitative trait locus (eQTL) mapping, which will increase our knowledge of how gene expression variation contributes to phenotypic variation. Current population sizes used in association mapping studies are modest in size and need to be greatly increased if mutations explaining less than a few per cent of the phenotypic variation are to be detected. Association mapping has started to yield insights into the genetic architecture of complex traits in plants, and future studies with greater genome coverage will help to elucidate how plants have managed to adapt to a wide variety of environmental conditions.     

A Flexible Approach for the Analysis of Rare Variants Allowing for a Mixture of Effects on Binary or Quantitative Traits

Multiple rare variants either within or across genes have been hypothesised to collectively influence complex human traits. The increasing availability of high throughput sequencing technologies offers the opportunity to study the effect of rare variants on these traits. However, appropriate and computationally efficient analytical methods are required to account for collections of rare variants that display a combination of protective, deleterious and null effects on the trait. We have developed a novel method for the analysis of rare genetic variation in a gene, region or pathway that, by simply aggregating summary statistics at each variant, can: (i) test for the presence of a mixture of effects on a trait; (ii) be applied to both binary and quantitative traits in population-based and family-based data; (iii) adjust for covariates to allow for non-genetic risk factors and; (iv) incorporate imputed genetic variation. In addition, for preliminary identification of promising genes, the method can be applied to association summary statistics, available from meta-analysis of published data, for example, without the need for individual level genotype data. Through simulation, we show that our method is immune to the presence of bi-directional effects, with no apparent loss in power across a range of different mixtures, and can achieve greater power than existing approaches as long as summary statistics at each variant are robust. We apply our method to investigate association of type-1 diabetes with imputed rare variants within genes in the major histocompatibility complex using genotype data from the Wellcome Trust Case Control Consortium.     

Combining data from multiple inbred line crosses improves the power and resolution of quantitative trait loci mapping

Rodent inbred line crosses are widely used to map genetic loci associated with complex traits. This approach has proven to be powerful for detecting quantitative trait loci (QTL); however, the resolution of QTL locations, typically approximately 20 cM, means that hundreds of genes are implicated as potential candidates. We describe analytical methods based on linear models to combine information available in two or more inbred line crosses. Our strategy is motivated by the hypothesis that common inbred strains of the laboratory mouse are derived from a limited ancestral gene pool and thus QTL detected in multiple crosses are likely to represent shared ancestral polymorphisms. We demonstrate that the combined-cross analysis can improve the power to detect weak QTL, can narrow support intervals for QTL regions, and can be used to separate multiple QTL that colocalize by chance. Moreover, combined-cross analysis can establish the allelic states of a QTL among a set of parental lines, thus providing critical information for narrowing QTL regions by haplotype analysis.     

Bayesian inference for genomic imprinting underlying developmental characteristics

The identification of imprinted genes is becoming a standard procedure in searching for quantitative trait loci (QTL) underlying complex traits. When a developmental characteristic such as growth or drug response is observed at multiple time points, understanding the dynamics of gene function governing the underlying feature should provide more biological information regarding the genetic control of an organism. Recognizing that differential imprinting can be development-specific, mapping imprinted genes considering the dynamic imprinting effect can provide additional biological insights into the epigenetic control of a complex trait. In this study, we proposed a Bayesian imprinted QTL (iQTL) mapping framework considering the dynamics of imprinting effects and model multiple iQTLs with an efficient Bayesian model selection procedure. The method overcomes the limitation of likelihood-based mapping procedure, and can simultaneously identify multiple iQTLs with different gene action modes across the whole genome with high computational efficiency. An inference procedure using Bayes factors to distinguish different imprinting patterns of iQTL was proposed. Monte Carlo simulations were conducted to evaluate the performance of the method. The utility of the approach was illustrated through an analysis of a body weight growth data set in an F(2) family derived from LG/J and SM/J mouse stains. The proposed Bayesian mapping method provides an efficient and computationally feasible framework for genome-wide multiple iQTL inference with complex developmental traits.     

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

ABSTRACT: BACKGROUND: Although many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. Single trait analysis does not take advantage of possible genetic and environmental correlations between traits. In this paper, we propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. We also develop a novel score-based method for estimating genome-wide significance level of putative QTL effects suitable for the MTMIM model. The MTMIM method is implemented in the freely available and widely used Windows QTL Cartographer software. RESULTS: Throughout the paper, we provide compelling empirical evidences that: (1) the score-based threshold maintains proper type I error rate and tends to keep false discovery rate within an acceptable level; (2) the MTMIM method can deliver better parameter estimates and power than single trait multiple interval mapping method; (3) an analysis of Drosophila dataset illustrates how the MTMIM method can better extract information from datasets with measurements in multiple traits. CONCLUSIONS: The MTMIM method represents a convenient statistical framework to test hypotheses of pleiotropic QTL versus closely linked nonpleiotropic QTL, QTL by environment interaction, and to estimate the total genotypic variance-covariance matrix between traits and to decompose it in terms of QTL-specific variance-covariance matrices, therefore, providing more details on the genetic architecture of complex traits.