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PubMed2005

Genetic epidemiology of diabetes.

Conventional genetic analysis focuses on the genes that account for specific phenotypes, while traditional epidemiology is more concerned with the environmental causes and risk factors related to traits. Genetic epidemiology is an alliance of the 2 fields that focuses on both genetics, including allelic variants in different populations, and environment, in order to explain exactly how genes convey effects in different environmental contexts and to arrive at a more complete comprehension of the etiology of complex traits. In this review, we discuss the epidemiology of diabetes and the current understanding of the genetic bases of obesity and diabetes and provide suggestions for accelerated accumulation of clinically useful genetic information.

Diabetes Mellitus↗
PubMed1997

Simulation of the distribution of parental strains' genomes in RC strains of mice.

Recombinant Congenic strains (RC strains) were developed to facilitate mapping of genes influencing complex traits controlled by multiple genes. They were produced by inbreeding of the progeny derived from a second backcross from a common 'donor' inbred strain to a common 'background' inbred strain. Each RC strain contains a random subset of approximately 12.5% of genes from the donor strain and 87.5% of genes from the background strain. In this way the genetic control of a complex disease may be dissected into its individual components. We simulated the production of the RC strains to study to what extent they have to be characterized in order to obtain sufficient information about the distribution of the parental strains' genomes in these strains and to acquire insight into parameters influencing their effectiveness in mapping quantitative trait loci (QTLs). The donor strain genome in the RC strains is fragmented into many segments. Genetic characterization of these strains with one polymorphic marker per 3.3 centiMorgans (cM) is needed to detect 95% of the donor strain genome. The probability of a donor strain segment being located entirely in between two markers of background strain origin that are 3 cM apart (and hence escaping detection) is 0.003. Although the donor strain genome in the RC strains is split into many segments, the largest part still occurs in relatively long stretches that are mostly concentrated in fewer than 13 autosomes, the median being 9 autosomes. Thus, in mapping QTLs, the use of RC strains facilitates the detection of linkage.

Animals↗
PubMed2002

Gene introgression into Coffea arabica by way of triploid hybrids (C. arabica x C. canephora).

Interspecific triploid hybrid plants between the tetraploid species Coffea arabica L. and the diploid species C. canephora P. were backcrossed to C. arabica. Although characterised by a low production and an important fruit dropping, all attempted crosses (ie, 6) generated BC(1) progenies. Flow cytometric analysis of the nuclear DNA content revealed that most of the BC1 individuals were nearly tetraploid. Among the male gametes produced by the interspecific triploid hybrids, those presenting a high number of chromosomes appeared strongly favoured. Only pollen mother cells having nearly 22 chromosomes were effective, the others leading to deficient endosperm and fruit dropping. Molecular markers (ie, microsatellite and AFLP) combined with evaluations of morphological characteristics and resistance to leaf rust were applied to verify the occurrence of gene transfer from C. canephora into C. arabica, and to estimate the amount of introgression present in BC(1) individuals. The results reveal a strong deficiency in the C. canephroa alleles indicating a severe counter-selection against the introgression of genetic material from C. canephora into C. arabica by way of triploid hybrids. However, introgressants displaying desirable traits such as a high resistance to leaf rust were obtained. The low level of introgression could be an advantage by facilitating the recovery of the recurrent parent and possibly reducing the number of required backcrosses. On the other hand, this could be a limitation when attempting the transfer of a complex trait or several simply inherited traits.

Coffea↗
PubMed2005

Haplotype sharing analysis using mantel statistics.

OBJECTIVE: The potential value of haplotypes has attracted widespread interest in the mapping of complex traits. Haplotype sharing methods take the linkage disequilibrium information between multiple markers into account, and may have good power to detect predisposing genes. We present a new approach based on Mantel statistics for spacetime clustering, which is developed in order to improve the power of haplotype sharing analysis for gene mapping in complex disease. METHODS: The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes for case-only and case-control studies. The genetic similarity is measured as the shared length between haplotypes around a putative disease locus. The phenotypic similarity is measured as the mean-corrected cross-product based on the respective phenotypes. We analyzed two tests for statistical significance with respect to type I error: (1) assuming asymptotic normality, and (2) using a Monte Carlo permutation procedure. The results were compared to the chi(2) test for association based on 3-marker haplotypes. RESULTS: The results of the type I error rates for the Mantel statistics using the permutational procedure yielded pointwise valid tests. The approach based on the assumption of asymptotic normality was seriously liberal. CONCLUSION: Power comparisons showed that the Mantel statistics were better than or equal to the chi(2) test for all simulated disease models.

Case-Control Studies↗
PubMed2006

Ab initio genotype-phenotype association reveals intrinsic modularity in genetic networks.

Microbial species express an astonishing diversity of phenotypic traits, behaviors, and metabolic capacities. However, our molecular understanding of these phenotypes is based almost entirely on studies in a handful of model organisms that together represent only a small fraction of this phenotypic diversity. Furthermore, many microbial species are not amenable to traditional laboratory analysis because of their exotic lifestyles and/or lack of suitable molecular genetic techniques. As an adjunct to experimental analysis, we have developed a computational information-theoretic framework that produces high-confidence gene-phenotype predictions using cross-species distributions of genes and phenotypes across 202 fully sequenced archaea and eubacteria. In addition to identifying the genetic basis of complex traits, our approach reveals the organization of these genes into generic preferentially co-inherited modules, many of which correspond directly to known enzymatic pathways, molecular complexes, signaling pathways, and molecular machines.

Bacteria↗
PubMed2003

Stability of the G-matrix in a population experiencing pleiotropic mutation, stabilizing selection, and genetic drift.

Quantitative genetics theory provides a framework that predicts the effects of selection on a phenotype consisting of a suite of complex traits. However, the ability of existing theory to reconstruct the history of selection or to predict the future trajectory of evolution depends upon the evolutionary dynamics of the genetic variance-covariance matrix (G-matrix). Thus, the central focus of the emerging field of comparative quantitative genetics is the evolution of the G-matrix. Existing analytical theory reveals little about the dynamics of G, because the problem is too complex to be mathematically tractable. As a first step toward a predictive theory of G-matrix evolution, our goal was to use stochastic computer models to investigate factors that might contribute to the stability of G over evolutionary time. We were concerned with the relatively simple case of two quantitative traits in a population experiencing stabilizing selection, pleiotropic mutation, and random genetic drift. Our results show that G-matrix stability is enhanced by strong correlational selection and large effective population size. In addition, the nature of mutations at pleiotropic loci can dramatically influence stability of G. In particular, when a mutation at a single locus simultaneously changes the value of the two traits (due to pleiotropy) and these effects are correlated, mutation can generate extreme stability of G. Thus, the central message of our study is that the empirical question regarding G-matrix stability is not necessarily a general question of whether G is stable across various taxonomic levels. Rather, we should expect the G-matrix to be extremely stable for some suites of characters and unstable for others over similar spans of evolutionary time.

Analysis of Variance↗
PubMed2004

Drosophila, an emerging model for cardiac disease.

A variety of studies that are currently underway may validate the fruit fly as an in vivo model for analyzing genes involved in cardiac function. Many mutations in conserved genetic pathways have been found, including those controlling development and physiology. Because homologous genes control early developmental events as well as functional components of the Drosophila and vertebrate hearts, the fly is the simplest existing model system that can be used to assay genes involved in human congenital heart disease (CHD). The wide variety of genetic tools available to Drosophila researchers offers many technical advantages for rapidly screening through large numbers of candidate genes. Thus, an important future and long-term direction is likely to be the use of Drosophila as a vehicle for analyzing polygenic traits as an aid in human genetics. One can anticipate a time in the not too distant future when mutant lines exist for every gene in vertebrate systems, such as mice and zebrafish. However, one of the enduring problems that will not easily be addressed by such resources will be the tracking of complex traits defined by polygenic variants. For this level of genetic analysis, simple genetic model systems including yeast, Caenorhabditis elegans, and Drosophila melanogaster will undoubtedly play a crucial ongoing role. Of them, Drosophila will be critical for examining gene networks involved in organogenesis and is clearly the system of choice for studying cardiac development, function and aging, since among the simple genetic models it is the only one with a fluid pumping heart.

Animals↗
PubMed2026

Analysis of deep-resequencing data of 984 soybean accessions reveals structural variations underlying agronomic traits.

Genomic structural variants (SVs) are major sources of genetic variation and have profound impacts on phenotypic traits. However, their functional effects remain largely unexplored in soybean. Here, we resequence 940 soybean accessions. Together with 44 publicly available datasets, we identify 602,281 SVs. Using a graph-based genome, we detect an additional 58,760 presence/absence variations (PAVs) that broadly affect gene expression. Population genomic analyses reveal that SVs serve as a core driving force for soybean domestication and improvement. Integrating SVs with QTLs for oil and protein content, and performing GWAS on 27 traits, we identify key functional SVs. These include transposable element insertions altering seed coat color, multiple insertions within a cytochrome P450 gene modifying flower and hypocotyl color, and a GmMATE1 deletion enhancing seed size. Together, our study establishes a comprehensive SV map of soybean, offering a valuable resource for dissecting the genetic basis of complex traits to accelerate molecular breeding.

Glycine max↗
PubMed2001

Quantitative-trait loci influencing body-mass index reside on chromosomes 7 and 13: the National Heart, Lung, and Blood Institute Family Heart Study.

Obesity is a risk factor for many chronic diseases, including glucose intolerance, lipid disorders, hypertension, and coronary heart disease. Even though the body-mass index (BMI) is a heterogeneous phenotype reflecting the amount of fat, lean mass, and body build, several studies have provided evidence of one or two major loci contributing to the variation in this complex trait. We sought to identify loci with potential influence on BMI in the data obtained from National Heart, Lung, and Blood Institute Family Heart Study. Two complementary samples were studied: (a) 1,184 subjects in 317 sibships, with 243 markers typed by the Utah Molecular Genetics Laboratory (UMGL) and (b) 3,027 subjects distributed among 401 three-generation families, with 404 markers typed by the Mammalian Genotyping Service (MGS). A genome scan using a variance-components-based linkage approach was performed for each sample, as well as for the combined sample, in which the markers from each analysis were placed on a common genetic map. There was strong evidence for linkage on chromosome 7q32.3 in each sample: the maximum multipoint LOD scores were 4.7 (P<10-5) at marker GATA43C11 and 3.2 (P=.00007) at marker D7S1804, for the MGS and UMGL samples, respectively. The linkage result is replicated by the consistent evidence from these two complementary subsets. Furthermore, the evidence for linkage was maintained in the combined sample, with a LOD score of 4.9 (P<10-5) for both markers, which map to the same location. This signal is very near the published location for the leptin gene, which is the most prominent candidate gene in this region. For the combined-sample analysis, evidence of linkage was also found on chromosome 13q14, with D13S257 (LOD score 3.2, P=.00006), and other, weaker signals (LOD scores 1.5-1.9) were found on chromosomes 1, 2, 3, 5, 6, 14, and 15.

Body Mass Index↗
PubMed2001

Genetic strategies for the personalization of antipsychotic treatment.

Pharmacogenetic research into complex traits, such as response to antipsychotic treatment has proved a difficult task. Nevertheless, investigation of drug metabolic enzymes has revealed polymorphisms in specific cytochrome P450 genes responsible for treatment-induced toxic reactions. However, the picture becomes more complicated when drug target sites are investigated in search of genetic influence. Most antipsychotic drugs are multitarget, denoting a complex mechanism of action. Although individual genes have been reported to influence antipsychotic response, no single gene can account for the variability observed in treatment response. Current investigations focus on single gene variants that may be associated with particular side effects or symptoms as well as contributing to general response. The scope of this article is to review recent advances of pharmacogenetic research on antipsychotic drugs and the strategies under development for the individualization of treatment.

Antipsychotic Agents↗
PubMed2004

Application of chromosomal substitution techniques in gene-function discovery.

A consomic rat strain is one in which an entire chromosome is introgressed into the isogenic background of another inbred strain using marker assisted selection. The development and initial physiologic screening of two inbred consomic rat panels on two genetic backgrounds (44 strains) is well underway. The primary uses of consomic strains are: (1) to assign traits and quantitative trait loci (QTL) to chromosomes by surveying the panel of strains with substituted chromosomes; (2) to rapidly develop congenic strains over a narrow region using several approaches described in this review and perform F2 linkage studies to positionally locate QTL in a fixed genetic background. In addition, consomic strains overcome many of the problems encountered with segregating crosses where, even if linkage is found, each individual in the cross is genetically unique and the combination of genes cannot be reproduced or studied in detail. Consomic strains provide greater statistical power to detect linkage than traditional F2 crosses because of their fixed genetic backgrounds, and can produce sufficient numbers of genetically identical rats to validate the relationship between a trait and a particular chromosome. These strains allow studies to be performed in a replicative or longitudinal manner to elucidate in greater detail the sequential changes responsible for the observed phenotypes of these animals, and they enable one to assess the impact of a causal gene region in a genome by allowing comparisons of the effect of replacement of a specific chromosome upon a disease susceptible or resistant genomic background. Consomics can be used to quickly develop multiple chromosome substitution models to investigate gene-gene interactions of complex traits or diseases. Finally, they often provide the best available inbred control strain for particular physiological comparisons with the inbred parental strains. Consomic rat strains are proving to be a unique scientific resource that greatly extends our understanding of genes and complex normal and pathological function.

Animals↗
PubMed1997

Markov chain Monte Carlo segregation and linkage analysis for oligogenic models.

A new method for segregation and linkage analysis, with pedigree data, is described. Reversible jump Markov chain Monte Carlo methods are used to implement a sampling scheme in which the Markov chain can jump between parameter subspaces corresponding to models with different numbers of quantitative-trait loci (QTL's). Joint estimation of QTL number, position, and effects is possible, avoiding the problems that can arise from misspecification of the number of QTL's in a linkage analysis. The method is illustrated by use of a data set simulated for the 9th Genetic Analysis Workshop; this data set had several oligogenic traits, generated by use of a 1,497-member pedigree. The mixing characteristics of the method appear to be good, and the method correctly recovers the simulated model from the test data set. The approach appears to have great potential both for robust linkage analysis and for the answering of more general questions regarding the genetic control of complex traits.

Computer Simulation↗
PubMed2003

Expressionview: visualization of quantitative trait loci and gene-expression data in Ensembl.

We present here a software tool for combined visualization of gene-expression data and quantitative trait loci (QTL). The application is implemented as an extension to the Ensembl project and caters for a direct transition from microarray experiments of gene or protein expression levels to the genomic context of individual genes and QTL. It supports the visualization of gene clusters and the selection of functional candidate genes in the context of research on complex traits.

Animals↗
PubMed2026

Systematic Approach for Compound Angus Populations Revealing Positional Candidate Genes and Improving Prediction Accuracy in Carcass Traits.

Carcass traits, which reflect growth performance and muscle development, are economically important in beef cattle, yet their genetic determinants remain poorly characterized. Both single-population Genome-wide association studies (GWAS) methods, such as BLINK, and cross-population meta-analysis approaches are widely used to identify genetic variants, yet their comparative performance in genomic prediction for complex traits in structured populations remains underexplored. Few studies have directly compared these methods in genomic prediction. To address this gap, this study aims to (i) identify positional candidate genes associated with carcass traits and (ii) evaluate the context-dependent advantages of Covariate Adjustment (CA) and meta in genomic prediction. In this study, we analyzed carcass weight (CW), live weight (LW), and dressing percentage (DP) in 279 crossbred Angus cattle genotyped with the PHR0105_Bt140K_v1.0 SNP chip. GWAS was performed on the full population using BLINK, and results from three subpopulations were combined via meta-analysis, with significance thresholds for both approaches determined by a shuffle-based method. Candidate genes located within &#xb1;10 kb of significant SNPs were associated with different carcass traits, including STRIT1, SEL1L3, NOC4L and ANK1 for DP; SNCA and DNAH5 for CW; and GYPC, GPR158, and GUCY1A1 for LW. Prediction accuracy under MAS and MABLUP showed meta slightly outperformed BLINK in MAS, while BLINK was better with covariate adjustment; after incorporating kinship in MABLUP, meta achieved higher accuracy and population partitioning was negligible. Overall, MABLUP yielded the highest accuracy (0.52-0.79) versus MAS (0.37-0.54) in all traits. These findings provide a methodological basis for selecting appropriate GWAS strategies in structured populations and highlight candidate genes.

GS↗
PubMed2005

Selection of candidate genes in hypertension.

Essential hypertension is a common disease with multifactorial etiology affecting up to 10 million individuals in the United Kingdom alone. Current knowledge of the genetic contribution to this trait is restricted to a number of rare variants that produce hypertensive phenotypes in a Mendelian fashion and to genes highlighted by work on blood pressure regulation in rodent models. Recent advances in comparative genomics, genome-wide scans for linkage, transcriptomics, proteomics, and metabolomics allow a systematic approach to the prioritization of candidate genes for hypertension and other complex traits. We review the current state of play in these fields related to hypertension and show, with a particular example, how these data may help target genetic studies in the future.

Animals↗
PubMed2000

Some properties of a variance components model for fine-mapping quantitative trait loci.

Identifying etiological variants for multifactorial traits by allelic association holds promise when many markers are available in close proximity. However, evidence for or against association at any particular marker does not provide any direct information about the influence of causal variants or the frequency of the etiologic allele(s). Recently, a variance components model of linkage and association was developed for quantitative traits which is sufficiently flexible to provide some insights into these issues. We show that this combined linkage/association model provides an estimate of the additive genetic variance of a trait that is attributable to disequilibrium between the marker and QTL. We use this estimate to construct approximate boundaries of the minimum level of disequilibrium between an observed marker and unobserved QTL and to delimit the permissible range of allele frequencies at the QTL based on available data at nearby markers. This information may facilitate fine-mapping studies of complex traits that aim to localize QTLs by assessment of association with many markers in a candidate region of interest.

Alleles↗
PubMed1999

A maize map standard with sequenced core markers, grass genome reference points and 932 expressed sequence tagged sites (ESTs) in a 1736-locus map.

We have constructed a 1736-locus maize genome map containing1156 loci probed by cDNAs, 545 probed by random genomic clones, 16 by simple sequence repeats (SSRs), 14 by isozymes, and 5 by anonymous clones. Sequence information is available for 56% of the loci with 66% of the sequenced loci assigned functions. A total of 596 new ESTs were mapped from a B73 library of 5-wk-old shoots. The map contains 237 loci probed by barley, oat, wheat, rice, or tripsacum clones, which serve as grass genome reference points in comparisons between maize and other grass maps. Ninety core markers selected for low copy number, high polymorphism, and even spacing along the chromosome delineate the 100 bins on the map. The average bin size is 17 cM. Use of bin assignments enables comparison among different maize mapping populations and experiments including those involving cytogenetic stocks, mutants, or quantitative trait loci. Integration of nonmaize markers in the map extends the resources available for gene discovery beyond the boundaries of maize mapping information into the expanse of map, sequence, and phenotype information from other grass species. This map provides a foundation for numerous basic and applied investigations including studies of gene organization, gene and genome evolution, targeted cloning, and dissection of complex traits.

Chromosome Mapping↗
PubMedDate not supplied

[Methods for transcriptome and proteome research: applications for studying the biology of reproduction in cattle].

Improvements of animal health, welfare and product quality are major goals of modern animal breeding. Thus, in addition to the classical production traits, functional traits such as disease resistance, fertility and longevity moved into the center of animal breeder's interests. Due to their low heritability, the improvement of functional traits using conventional approaches of phenotypic testing and quantitative genetics is difficult. A number of studies have been conducted worldwide in various species to map quantitative trait loci (QTLs) and to identify genetic markers for health traits. This has revealed a plethora of chromosome regions which may harbor genes with relevance for animal health. Functional genome research integrates holistic investigations at the level of the genome, at the level of gene activity (transcriptome, proteome) and at various levels of phenotypic expression. The integration of all these levels of information provides the basis for the functional dissection of complex traits. This review provides an overview of the most important strategies for holistic transcriptome and proteome analyses. The successful application of these techniques is exemplified by our studies of bovine reproductive biology.

Animals↗