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PubMed2004

Quantitative genomics: exploring the genetic architecture of complex trait predisposition.

Most phenotypes with agricultural or biomedical relevance are multifactorial traits controlled by complex contributions of genetics and environment. Genetic predisposition results from combinations of relatively small effects due to variations within a large number of genes, known as QTL. Well over 200 QTL have been reported for growth and body composition traits in the mouse, which likely represent at least 50 to 100 distinct genes. Molecular biology has yielded significant advances in understanding these traits at the metabolic and physiological levels; however, little has been learned regarding the identity and nature of the underlying polygenes. In addition to the significantly poor precision inherent to QTL localization, it is very difficult to differentiate between co-localization and coincidence when comparing QTL with other QTL and with potential candidate genes. The wide gap between our knowledge of physiological mechanisms underlying complex traits and the nature of genetic predisposition significantly impairs discovery of genes underlying QTL. Identification and genetic mapping of key transcriptional, proteomic, metabolomic, and endocrine events will uncover large lists of significant positional candidate genes for growth and body composition. However, integration of experimental approaches to jointly evaluate predisposition and physiology will increase success of QTL identification by merging the power of recombination with functional analysis. Measuring physiologically relevant subphenotypes within a structured QTL mapping population will not only facilitate pathway-specific prioritization among candidate genes, but may also directly identify genes underlying QTL. This would advance our understanding of the genetic architecture of complex traits by testing the central hypothesis that genes controlling predisposition to a quantitative trait are primarily involved in trans-regulation of the primary physiological pathways that regulate the trait.

Animals↗
PubMed1999

Mapping complex traits in diseases of the hair and skin.

The past decade has witnessed the ascendance of human genetics in modern medicine, and at the forefront of this movement is the identification of genetic factors underlying inherited diseases. The methods of genetic mapping and positional cloning have made the discovery of genes with alleles that cause simple Mendelian diseases commonplace. The elucidation of the genetic basis of such disorders has vitalized both human genetics and the entire medical community as the field has gained prominence. The fact remains, however, that diseases resulting from the action of alleles of a single gene comprise only a minor percentage of traits that are medically relevant to humanity. The majority of these are multifactorial "complex traits", which result from the aggregate contribution of an unknown number of genes interacting with each other and with the environment. The current challenge has become one of parlaying successes in the mapping of Mendelian diseases into the discovery of genes whose alleles predispose the development of a complex disease. In light of this challenge, this review summarizes the methods and addresses some of the central issues of complex trait mapping, while using examples from dermatologically-relevant complex traits such as psoriasis and alopecia. Additionally, current technical and theoretical advances as well as the potential impact of the Human Genome Project will be discussed.

Animals↗
PubMed2006

Evolutionary persistence of phenotypic integration: influence of developmental and functional relationships on complex trait evolution.

Examination of historical persistence of integration patterns provides an important insight into understanding the origin and evolution of complex traits. Specifically, the distinct effects of developmental and functional integration on the evolution of complex traits are often overlooked. Because patterns of functional integration are commonly shaped by selection exerted by the external environment, whereas patterns of developmental integration can be determined by relatively environment-independent selection for developmental homeostasis, examination of historical persistence of morphological integration patterns among species should reveal the relative importance of current selection in the evolution of complex traits. We compared historical persistence of integration patterns produced by current developmental versus ecological requirements by examining the evolution of complex mandibular structures in nine species of soricid shrews. We found that, irrespective of phylogenetic relatedness of species, patterns of developmental and functional integration were highly concordant, suggesting that strong selection for developmental homeostasis favors concordant channeling of both internal and external variation. Overall, our results suggest that divergence in mandible shape among species closely follows variation in functional demands and ecological requirements regardless of phylogenetic relatedness among species.

Animals↗
PubMed2006

Identification of genes for a complex trait: examples from hypertension.

Essential hypertension (EH) affects approximately 20% of the adult population, and has a multifactorial origin arising from an interaction between susceptibility genes and environmental factors. Several strategies and methods have been used to identify hypertension susceptibility genes. This review is thought to highlight current strategies for a better understanding of their limitations and strengths in a complex trait like EH. Linkage analysis is less effective at identifying common variants with modest effects typical for complex traits, and has therefore proved to be largely unsuccessful in EH. No candidate gene was assessed by a human linkage study so far. Possible redesigns of the linkage approach for complex diseases may include larger sample sizes and dense marker maps. Genetic association studies may be an effective approach to the problems posed by complex traits. With the explosion of genotyping technologies, genome-wide association studies have become feasible, and small-scale association studies have become plentiful. The different types of association studies are reviewed and issues that are important to consider when interpreting association studies of complex traits are discussed. Properly defined phenotypes, large enough sample cohorts to achieve sufficient statistical power, carefully matched samples to avoid population stratification are all integral parts of a high-quality association study. Multiple testing often results in false-positive results by chance, and inconclusive results may arise from ignoring linkage disequilibrium of the tested polymorphism, an effect avoidable by haplotype analysis. A new evolutionary development of the candidate gene approach is introduced which will extent traditional association study settings gaining better understanding of complex diseases like hypertension and might give better chances to evaluate association studies for their functional relevance.

Genetic Linkage↗
PubMed2006

Using mating designs to uncover QTL and the genetic architecture of complex traits.

Analysis of quantitative trait loci (QTL) affecting complex traits is often pursued in single-cross experiments. For most purposes, including breeding, some assessment is desired of the generalizability of the QTL findings and of the overall genetic architecture of the trait. Single-cross experiments provide a poor basis for these purposes, as comparison across experiments is hampered by segregation of different allelic combinations among different parents and by context-dependent effects of QTL. To overcome this problem, we combined the benefits of QTL analysis (to identify genomic regions affecting trait variation) and classic diallel analysis (to obtain insight into the general inheritance of the trait) by analyzing multiple mapping families that are connected via shared parents. We first provide a theoretical derivation of main (general combining ability (GCA)) and interaction (specific combining ability (SCA)) effects on F(2) family means relative to variance components in a randomly mating reference population. Then, using computer simulations to generate F(2) families derived from 10 inbred parents in different partial-diallel designs, we show that QTL can be detected and that the residual among-family variance can be analyzed. Standard diallel analysis methods are applied in order to reveal the presence and mode of action (in terms of GCA and SCA) of undetected polygenes. Given a fixed experiment size (total number of individuals), we demonstrate that QTL detection and estimation of the genetic architecture of polygenic effects are competing goals, which should be explicitly accounted for in the experimental design. Our approach provides a general strategy for exploring the genetic architecture, as well as the QTL underlying variation in quantitative traits.

Animals↗
PubMed2004

A fast algorithm for functional mapping of complex traits.

By integrating the underlying developmental mechanisms for the phenotypic formation of traits into a mapping framework, functional mapping has emerged as an important statistical approach for mapping complex traits. In this note, we explore the feasibility of using the simplex algorithm as an alternative to solve the mixture-based likelihood for functional mapping of complex traits. The results from the simplex algorithm are consistent with those from the traditional EM algorithm, but the simplex algorithm has considerably reduced computational times. Moreover, because of its nonderivative nature and easy implementation with current software, the simplex algorithm enjoys an advantage over the EM algorithm in the dynamic modeling and analysis of complex traits.

Algorithms↗
PubMed1999

Genetic analysis of late-onset type 2 diabetes in a mouse model of human complex trait.

Type 2 diabetes is a complex trait with both genes and environmental factors contributing to susceptibility. Except for rare subtypes with monogenic inheritance, the genetic basis of type 2 diabetes is unknown because of the complex and heterogeneous nature of the disease. By using the NSY mouse, an inbred mouse model of type 2 diabetes, we genetically dissected late-onset type 2 diabetes and demonstrated age-dependent changes in the genetic control of type 2 diabetes as well as polygenic inheritance. Three major loci (Nidd1nsy, Nidd2nsy, Nidd3nsy) were mapped on mouse chromosomes (Chr) 11, 14, and 6, respectively. The existence of a fourth locus (Nidd4nsy) with an age-dependent effect was suggested by longitudinal, but not cross-sectional, analysis of linkage data. Nidd1nsy and Nidd4nsy appear to affect insulin secretion, whereas Nidd2nsy and Nidd3nsy appear to affect insulin sensitivity. A locus on Chr 6 was significantly linked to epididymal fat weight. A candidate disease gene (Tcf2) on Chr 11, encoding hepatic nuclear factor-1beta, was shown to have a rare sequence variant in the DNA binding domain in the model. The mouse model we used will serve as a useful model for future studies on the etiology of late-onset polygenic type 2 diabetes in humans.

Adipose Tissue↗
PubMed2002

Finding genes that underlie complex traits.

Phenotypic variation among organisms is central to evolutionary adaptations underlying natural and artificial selection, and also determines individual susceptibility to common diseases. These types of complex traits pose special challenges for genetic analysis because of gene-gene and gene-environment interactions, genetic heterogeneity, low penetrance, and limited statistical power. Emerging genome resources and technologies are enabling systematic identification of genes underlying these complex traits. We propose standards for proof of gene discovery in complex traits and evaluate the nature of the genes identified to date. These proof-of-concept studies demonstrate the insights that can be expected from the accelerating pace of gene discovery in this field.

Alleles↗
PubMed2001

Animal genetic models for complex traits of physical capacity.

Animal genetic models for complex traits of physical capacity. Exerc. Sport Sci. Rev., Vol. 29, No. 1, pp 7-14, 2001. Understanding the genetic basis for variance in complex physical traits such as aerobic capacity has become an attainable goal. A starting point is the development or identification of animal genetic models that contrast the low and high values for the trait of interest. Genes that cause natural trait variation can ultimately be determined from animal models via genetic linkage.

Animals↗
PubMed2005

Complex phenotypes and complex genetics: an introduction to genetic studies of complex traits.

There is currently intense interest in the genetic factors contributing to many diseases with cardiovascular complications. Diseases like atherosclerosis, diabetes, and hypertension are referred to as complex traits because multiple genes contribute to the phenotype either individually or through interactions with each other or the environment. Enabled and energized by the striking successes over the past 20 years in identifying genes that are responsible for single gene traits, many geneticists have turned to the investigation of methods that will allow for the dissection of complex traits. There have already been some successes, so there is no reason to consider the problem as inherently intractable. However, it is important to reflect on what conditions are necessary for the identification of genes that operate in complex traits. A recurring theme in this research area has been difficulty in repeating and validating research findings, and this most often can be attributed to limitations in study design. It is also important to consider that any particular research strategy can only hope to describe a portion of factors that contribute to variation in the population; therefore, the genetic approach cannot be a panacea. New efficient technologies for genotyping and public databases describing the fine structure of genetic correlations in the genome should aid many aspects of the gene discovery process.

Genetic Predisposition to Disease↗
PubMed2026

Genetic and environmental interactions outweigh mitonuclear coevolution for complex traits in Drosophila.

The interdependent relationship between mitochondrial and nuclear genomes is a powerful model for understanding how epistasis shapes the architecture and evolution of complex traits. Once considered a neutral marker, mitochondrial DNA variation is now recognized as critical to phenotypic evolution because of its epistatic interactions and history of coevolution with the nuclear genome. A central challenge in evolutionary genetics is to quantify the relative importance of stabilizing and directional selection shaping complex trait distributions within and among species. Both can act on interacting and/or co-evolving genes contributing to quantitative traits, but resolving their relative roles is complicated by the complex architecture of most traits. Here, we use a panel of 90 Drosophila mitonuclear genotypes to quantify the relative contributions of mitochondrial, nuclear, and environmental variation and their interactions to four metabolically demanding complex traits. We sample both within-species and between-species mitochondrial variation and observe stronger interaction effects attributable to within-species variation, consistent with stabilizing selection maintaining mitonuclear function. Additionally, culturing the flies on a mitochondrial Complex I inhibitor, rotenone, reveals significant genotype x environment (G×E and G×G×E) interaction effects, providing insight into how genetic variation can be maintained across changing environments. Our results have broader implications in medicine, where mitochondrial DNA donors with longer purifying selection histories may be safer for mitochondrial replacement therapies.

Journal Article↗
PubMed2002

Complex-trait genetics: emergence of multivariate strategies.

Complex traits, including many disease-related traits, are influenced by multiple genes. Bivariate approaches that associate one gene with one trait are yielding to multivariate methods to synthesize the effects of multiple genes, integrate results across independent studies, and aid in the identification of coordinated pathways and interactions between loci.

Animals↗
PubMed1995

Interval mapping of quantitative trait loci employing correlated trait complexes.

An approach to increase the resolution power of interval mapping of quantitative trait (QT) loci is proposed, based on analysis of correlated trait complexes. For a given set of QTs, the broad sense heritability attributed to a QT locus (QTL) (say, A/a) is an increasing function of the number of traits. Thus, for some traits x and y, H(xy)2(A/a) > or = H(x)2(A/a). The last inequality holds even if y does not depend on A/a at all, but x and y are correlated within the groups AA, Aa and aa due to nongenetic factors and segregation of genes from other chromosomes. A simple relationship connects H2 (both in single trait and two-trait analysis) with the expected LOD value, ELOD = -1/2N log(1-H2). Thus, situations could exist that from the inequality H(xy)2(A/a) > or = H(x)2(A/a) a higher resolution is provided by the two-trait analysis as compared to the single-trait analysis, in spite of the increased number of parameters. Employing LOD-score procedure to simulated backcross data, we showed that the resolution power of the QTL mapping model can be elevated if correlation between QTs is taken into account. The method allows us to test numerous biologically important hypotheses concerning manifold effects of genomic segments on the defined trait complex (means, variances and correlations).

Analysis of Variance↗
PubMed2004

Association genetics of complex traits in conifers.

Association studies are becoming the experimental approach of choice to dissect complex traits in many organisms from humans to model plant systems. The candidate gene based-association approach has several important advantages for complex trait dissection in many coniferous forest tree species, including random mating and unstructured populations, adequate levels of nucleotide diversity, rapid decay of linkage disequilibrium, and precise evaluation of phenotype from clonal or progeny testing. Allele discovery using association approaches should lead to more-efficient methods of marker-assisted breeding and a deeper understanding of genetic adaptation in forest trees.

Chromosome Mapping↗
PubMed1999

Replication of linkage studies of complex traits: an examination of variation in location estimates.

In linkage studies, independent replication of positive findings is crucial in order to distinguish between true positives and false positives. Recently, the following question has arisen in linkage studies of complex traits: at what distance do we reject the hypothesis that two location estimates in a genomic region represent the same gene? Here we attempt to address this question. Sampling distributions for location estimates were constructed by computer simulation. The conditions for simulation were chosen to reflect features of "typical" complex traits, including incomplete penetrance, phenocopies, and genetic heterogeneity. Our findings, which bear on what is considered a replication in linkage studies of complex traits, suggest that, even with relatively large numbers of multiplex families, chance variation in the location estimate is substantial. In addition, we report evidence that, for the conditions studied here, the standard error of a location estimate is a function of the magnitude of the expected LOD score.

Chromosome Mapping↗
PubMed2004

Convergent analysis of cDNA and short oligomer microarrays, mouse null mutants and bioinformatics resources to study complex traits.

Gene expression data sets have recently been exploited to study genetic factors that modulate complex traits. However, it has been challenging to establish a direct link between variation in patterns of gene expression and variation in higher order traits such as neuropharmacological responses and patterns of behavior. Here we illustrate an approach that combines gene expression data with new bioinformatics resources to discover genes that potentially modulate behavior. We have exploited three complementary genetic models to obtain convergent evidence that differential expression of a subset of genes and molecular pathways influences ethanol-induced conditioned taste aversion (CTA). As a first step, cDNA microarrays were used to compare gene expression profiles of two null mutant mouse lines with difference in ethanol-induced aversion. Mice lacking a functional copy of G protein-gated potassium channel subunit 2 (Girk2) show a decrease in the aversive effects of ethanol, whereas preproenkephalin (Penk) null mutant mice show the opposite response. We hypothesize that these behavioral differences are generated in part by alterations in expression downstream of the null alleles. We then exploited the WebQTL databases to examine the genetic covariance between mRNA expression levels and measurements of ethanol-induced CTA in BXD recombinant inbred (RI) strains. Finally, we identified a subset of genes and functional groups associated with ethanol-induced CTA in both null mutant lines and BXD RI strains. Collectively, these approaches highlight the phosphatidylinositol signaling pathway and identify several genes including protein kinase C beta isoform and preproenkephalin in regulation of ethanol- induced conditioned taste aversion. Our results point to the increasing potential of the convergent approach and biological databases to investigate genetic mechanisms of complex traits.

Animals↗
PubMed2004

Ordered subset analysis in genetic linkage mapping of complex traits.

Etiologic heterogeneity is a fundamental feature of complex disease etiology; genetic linkage analysis methods to map genes for complex traits that acknowledge the presence of genetic heterogeneity are likely to have greater power to identify subtle changes in complex biologic systems. We investigate the use of trait-related covariates to examine evidence for linkage in the presence of heterogeneity. Ordered-subset analysis (OSA) identifies subsets of families defined by the level of a trait-related covariate that provide maximal evidence for linkage, without requiring a priori specification of the subset. We propose that examining evidence for linkage in the subset directly may result in a more etiologically homogeneous sample. In turn, the reduced impact of heterogeneity will result in increased overall evidence for linkage to a specific region and a more distinct lod score peak. In addition, identification of a subset defined by a specific trait-related covariate showing increased evidence for linkage may help refine the list of candidate genes in a given region and suggest a useful sample in which to begin searching for trait-associated polymorphisms. This method provides a means to begin to bridge the gap between initial identification of linkage and identification of the disease predisposing variant(s) within a region when mapping genes for complex diseases. We illustrate this method by analyzing data on breast cancer age of onset and chromosome 17q [Hall et al., 1990, Science 250:1684-1689]. We evaluate OSA using simulation studies under a variety of genetic models.

Breast Neoplasms↗
PubMed2003

How to model a complex trait. 2. Analysis with two disease loci.

Complex traits are often governed by more than one trait locus. The first step towards an adequate model for such diseases is a linkage analysis with two trait loci. Such an analysis can be expected to have higher power to detect linkage than a standard single-trait-locus linkage analysis. However, it is crucial to accurately specify the parameters of the two-locus model. Here, we recapitulate the general two-locus model with and without genomic imprinting. We relate heterogeneity, multiplicative, and additive two-locus models to biological or pathophysiological mechanisms, and give the corresponding averaged ("best-fitting") single-trait-locus models for each of the two loci. Furthermore, we derive the two-locus penetrances from the averaged single-locus models, under the assumption of one of the three model classes mentioned above. Using these formulae, if the best-fitting single-locus models are available, investigators may perform a two-trait-locus linkage analysis under a realistic model. This procedure will maximize the power to detect linkage for traits which are governed by two or more loci, and lead to more accurate estimates of the disease-locus positions.

Computer Simulation↗