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PubMed1996

A general statistical model for detecting complex-trait loci by using affected relative pairs in a genome search.

Scanning of the human genome by use of affected relative pairs and dense sets of highly polymorphic markers or by emerging techniques such as genomic mismatch scanning. (GMS) is making it possible to identify the genetic etiology of a disease through detection of susceptibility loci. We present a general statistical model and test to detect disease genes, using affected relative pairs and either markers or GMS technologies in a genome search. There are an exact test and large-sample normal approximation that control for the elevated probability of false detection of linkage in a genome search. The approach can be used to determine the sample size needed to obtain a prespecified power to detect a disease gene in the presence of etiologic heterogeneity for a single class or mixture of relative classes, with any number of markers, or clones, markers PIC values, or mapping function. The approach is used to examine differences in performance of markers and GMS technologies in a common statistical framework and to provide practical information for designing studies of complex traits.

Alleles↗
PubMed2025

Orthobunyavirus neurovirulence is a complex trait involving all three genome segments.

La Crosse orthobunyavirus (LACV) is a tri-segmented negative sense RNA virus and is the leading cause of pediatric arboviral encephalitis in the USA. The viral factors that mediate LACV's ability to replicate and cause damage and disease in the brain (neurovirulence) are not fully understood. We previously characterized the neurovirulence of LACV and closely related Inkoo virus (INKV) and discovered they have opposing neurovirulence phenotypes in mice and human neuronal cells: LACV has high neurovirulence and INKV has low neurovirulence. We therefore generated reassortant viruses between LACV and INKV to map the genome segments that mediate LACV's high neurovirulence phenotype. We recovered all six possible reassortant viruses of the L, M, and S genome segments using coinfection and reverse genetics approaches. We evaluated the neurovirulence of these reassortant viruses in mice in vivo and in human neuronal cells in vitro. Our results show that no single LACV genome segment alone was sufficient to cause wildtype LACV-like neurological disease in mice, and in fact all six reassortant viruses were attenuated from wildtype LACV. We found that the LACV M and S segments together were the primary drivers of neurological disease in mice, whereas the LACV L segment played a minor role. Our in vitro results indicate that the LACV M segment is crucial for efficient replication in neurons, but the LACV L segment appears to mediate slightly more efficient neuronal replication than the INKV L segment. The LACV M and S segments together induced wildtype LACV-like levels of neuronal death, indicating the LACV M and S are the primary mediators of neuronal death, and the L segment is not required. Together, these results indicate that LACV neurovirulence is a complex trait mediated by viral proteins on all three genome segments.

Journal Article↗
PubMed1999

Genetic mapping of complex traits.

Statistical genetic mapping methods are powerful tools for finding genes that contribute to complex human traits. Mapping methods combine knowledge of the biological mechanisms of inheritance and the randomness inherent in those mechanisms to locate, with increasing precision, trait genes on the human genome. We provide an overview of the two major classes of mapping methods, genetic linkage analysis and linkage disequilibrium analysis, and related concepts of genetic inheritance.

Chromosome Mapping↗
PubMedDate not supplied

Gene-environment interaction and the mapping of complex traits: some statistical models and their implications.

The manifestation of many complex diseases or traits is very likely the result of an inextricable interplay of the biological and the environmental. Yet the role of environmental effect has traditionally been played down, for various reasons. In this paper, some simple statistical models that incorporate gene-environment interaction (GEI) have been proposed and their behavior and implications investigated. These implications concern the conditional independence assumption in likelihood calculation of pedigree data, the fine-tuning of the sib pair method for mapping quantitative traits, apportioning of disease or trait variation due to specific causes. In addition, they concern properties of gene mapping methods that do not take GEI into account, and they bring into question the utility of commonly used measures of genetic effects such as recurrence risk ratio for relative pairs, twin concordance rates, and heritability coefficients. In the presence of GEI, all these measures are functions not only of genetic effects and gene frequency, but also of environmental effects, the distribution of environmental factors in the population, and of GEI. Above all, these measures are all measures of familial aggregation, since they can be significant even in the absence of any genetic component of the disease. Thus their use as indicators of the genetic basis of complex diseases is cast into doubt.

Chromosome Mapping↗
PubMed2000

Complex trait analysis in the mouse: The strengths, the limitations and the promise yet to come.

In 1990, David Baltimore predicted that the 1990s would be the decade of the mouse (). This certainly proved to be true: The mouse has contributed immensely to biological research through transgenic, embryonic stem cell (ES) knockout, and classical genetic technologies. But its usefulness as a model organism is by no means over; indeed it is still rising to its peak: The mouse as a model mammalian organism still has much to offer. This article reviews use of the mouse to dissect complex genetic traits using quantitative trait analysis, with a particular emphasis on medically important diseases.

Animals↗
PubMed2000

Twins. Novel uses to study complex traits and genetic diseases.

The challenge faced by research into the genetic basis of complex disease is to identify genes of small relative effect against a background of substantial genetic and environmental variation. This has focused interest on a classical epidemiological design: the study of twins. Through their precise matching for age, the common family environment and background environmental variation, studying diseases in non-identical twins provides a means to enhance the power of conventional strategies to detect genetic influence through linkage and association. The unique matching of identical twins provides researchers with ways to isolate the function of individual genes involved in disease together with approaches to understanding how genes and the environment interact.

Diseases in Twins↗
PubMed2003

Using haplotype blocks to map human complex trait loci.

Understanding of linkage disequilibrium (LD) in human populations could facilitate the discovery of genes that influence complex human diseases. The "HapMap" project is now underway to characterize patterns of LD in the human genome. A pilot study showed "haplotype blocks" in 51 regions scattered throughout the genome. These intriguing results raise important questions about the nature of recombination, and highlight practical issues of marker collection, the influence of statistical modelling on apparent block structure, and the levels of genotyping necessary for studies of common diseases. Knowledge of local disequilibrium patterns may help identify common polymorphisms involved in complex disease, but completely new analytical methods and experimental designs will be required to identify important rare variants.

Chromosome Mapping↗
PubMed1996

Modern molecular genetic approaches to complex traits: implications for psychiatric disorders.

The majority of common psychiatric disorders pose problems for geneticists because of their complex and non-Mendelian modes of inheritance. Early attempts to map genes for mental illness have so far largely overlooked this and sought genes of major effect in multiplex families using the lod score method of linkage analysis. However it seems that major genes are likely to be at best rare causes of common mental disorders, and the majority of cases probably reflect the interaction of several and perhaps many genes of comparatively small effect. There are two complementary sets of strategies that allow such genes to be identified. The first is to perform linkage analysis based on allele sharing in pairs of affected relatives. The second is to carry out association studies on samples of unrelated individuals. These methods and their applicability to psychiatric disorders are described. Psychiatric genetics has traditionally focussed on categorical phenotypes, but if valid continuous measures can be developed, powerful quantitative trait loci (QTL) approaches may also become feasible. Another important area is likely to be the study of relevant models in animals such as rodents in which genetic studies have many advantages. Finally we should not overlook the possibility that there are molecular explanations for irregular patterns of transmission such as mitochondrial inheritance, genomic imprinting and dynamic mutations.

Genetic Diseases, Inborn↗
PubMed2003

Automated detection of informative combined effects in genetic association studies of complex traits.

There is a growing body of evidence suggesting that the relationships between gene variability and common disease are more complex than initially thought and require the exploration of the whole polymorphism of candidate genes as well as several genes belonging to biological pathways. When the number of polymorphisms is relatively large and the structure of the relationships among them complex, the use of data mining tools to extract the relevant information is a necessity. Here, we propose an automated method for the detection of informative combined effects (DICE) among several polymorphisms (and nongenetic covariates) within the framework of association studies. The algorithm combines the advantages of the regressive approaches with those of data exploration tools. Importantly, DICE considers the problem of interaction between polymorphisms as an effect of interest and not as a nuisance effect. We illustrate the method with three applications on the relationship between (1). the P-selectin gene and myocardial infarction, (2). the cholesteryl ester transfer protein gene and plasma high-density-lipoprotein cholesterol concentration, and (3). genes of the renin-angiotensin-aldosterone system and myocardial infarction. The applications demonstrated that the method was able to recover results already found using other approaches, but in addition detected biologically sensible effects not previously described.

Algorithms↗
PubMed2003

Genetic analysis of a complex trait in the Utah Genetic Reference Project: a major locus for PTC taste ability on chromosome 7q and a secondary locus on chromosome 16p.

The ability to taste phenylthiocarbamide (PTC) shows complex inheritance in humans. We obtained a quantitative measure of PTC tasting ability in 267 members of 26 large three-generation families that were part of a set of CEPH families that had been used for genetic mapping. Significant bimodality was found for the distribution of age and gender adjusted scores (P<0.001), with estimated means of 3.16 (SD=1.80) and 9.26 (SD=1.54). Using the extensive genotyping available in these families from the genetic mapping efforts, we performed a genome scan by using 1324 markers with an average spacing of 4 cM. Analyses were first carried out with a recessive genetic model that has traditionally been assumed for the trait, and a threshold score of 8.0 delineating tasters from non-tasters. In this qualitative analysis, the maximum genome-wide lod score was 4.74 at 246 cM on chromosome 7; 17 families showed segregation of the dichotomous PTC phenotype. No other lod scores were significant; the next highest score was on chromosome 10 (lod=1.64 at 85 cM), followed by chromosome 3 (lod=1.29 at 267 cM). Because PTC taste ability exhibited substantial quantitative variation, the quantitative trait was also analyzed by using a variance components approach in SOLAR. The maximum quantitative genome-wide lod score was 8.85 at 246 cM on chromosome 7. Evidence for other possible quantitative loci was found on chromosomes 1 (lod=2.31 at 344 cM) and 16 (lod=2.01 at 14 cM). A subsequent two-locus whole-genome scan conditional on the chromosome 7 quantitative trait locus identified the chromosome 16 locus (two-locus lod=3.33 at 14 cM).

Adolescent↗
PubMed1997

Unravelling a complex trait: the genetics of insulin-dependent diabetes mellitus.

Insulin-dependent diabetes mellitus (IDDM), also known as type 1 or juvenile diabetes, is one of the first disorders with a complex genetic basis that researchers have begun to unravel. More than 20 years ago, the HLA region was found to contain a major locus that influences predisposition to IDDM, and a decade ago a locus with a smaller effect was identified in the insulin-gene region. With the advent of numerous microsatellite markers suitable for genome screening, an additional 6 loci that influence susceptibility to IDDM have been reported since late 1994. This paper summarizes that progress, with particular emphasis on research conducted by Field and associates. Some of the new loci appear to predispose people to IDDM independently of HLA and may be important factors in families with IDDM who lack strong HLA susceptibility. Other loci may interact to cause susceptibility, and specific combinations may be especially diabetogenic. Although isolating the actual predisposing genes in IDDM is more difficult than isolating those involved in single-locus genetic disorders, the fact that the genes can be identified with the use of a reasonable number of families is very encouraging for future research on other genetically complex disorders.

Diabetes Mellitus, Type 1↗
PubMed1999

Alcoholism as a complex trait: comparison of genetic models and role of epidemiological risk factors.

A genetic component for alcoholism appears likely, but the genes involved have yet to be identified. The mode of inheritance is probably more complex than for traditional mendelian disorders. In particular, there are marked differences in alcoholism by gender, parent-of-origin effects, ethnicity, and other epidemiological factors. We investigated the evidence for the presence of susceptibility genes for alcohol dependence in the Collaborative Study on the Genetics of Alcoholism data set.

Adolescent↗
PubMed1999

Exploring genetic analysis of complex traits through the paradigm of alcohol dependence: summary of GAW11 contributions.

We discuss the Genetic Analysis Workshop 11 analyses of data from the Collaborative Study on the Genetics of Alcoholism from a methodological perspective, concentrating on approaches and issues relevant to linkage and association studies of complex human phenotypes. Genome screening by parametric linkage, nonparametric linkage, association, and combined linkage/association methods are discussed. Issues particular to complex disease include etiologic heterogeneity, multivariate phenotype modeling, and parent-of-origin effects. Other methodological topics discussed are new and enhanced methods, ascertainment, weighting of nonindependent sib pairs, and data cleaning and validation.

Alcoholism↗
PubMed2000

Use of population isolates for mapping complex traits.

Geneticists have repeatedly turned to population isolates for mapping and cloning Mendelian disease genes. Population isolates possess many advantages in this regard. Foremost among these is the tendency for affected individuals to share ancestral haplotypes derived from a handful of founders. These haplotype signatures have guided scientists in the fine mapping of scores of rare disease genes. The past successes with Mendelian disorders using population isolates have prompted unprecedented interest among medical researchers in both the public and private sectors. Despite the obvious genetic and environmental complications, geneticists have targeted several population isolates for mapping genes for complex diseases.

Animals↗
PubMed1999

Approaches toward the genetic analysis of complex traits: asthma and atopy.

With the challenges emerging from the analysis and interpretation of the human genome, and the specific issues pertinent to pursuing the Genome Project itself, it is truly an exciting time in the development of the biological sciences. The occasion is certainly ripe for the emergence of new concepts and ideas, as the theories of complexity, natural selection, and reductionism become integrated into a new whole. We need to learn how to approach the analyses of the complex data sets that will be generated by the Genome Project and address, more generally, the problems inherent in the analysis of the complex diseases such as asthma. Finally, we need to consider how the recent advances in genetics and genomics will affect biomedical research reaching into the next millennium and beyond.

Asthma↗
PubMed1997

Approaches toward the genetic analysis of complex traits: asthma and atopy.

With the challenges emerging from the analysis and interpretation of the human genome, and the specific issues pertinent to pursuing the Genome Project itself, it is truly an exciting time in the development of the biological sciences. The occasion is certainly ripe for the emergence of new concepts and ideas, as the theories of complexity, natural selection, and reductionism become integrated into a new whole. We need to learn how to approach the analysis of the complex data sets that will be generated by the Genome Project and address, more generally, the problems inherent in the analysis of the complex diseases such as asthma. Finally, we need to consider how the recent advances in genetics and genomics will affect biomedical research into the next millennium and beyond.

Asthma↗
PubMed2025

Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations.

The broader application of polygenic risk score (PRS) is hindered by the limited transferability of PRS developed in Europeans to non-European populations. While many statistical methods have been developed to improve the performance of PRS in non-European populations, most of them focused on discrete genetic ancestry clusters and did not consider admixed individuals. Admixed individuals pose a unique challenge for PRS calculation due to the complexity of local ancestry and cross-ancestry effect sizes. Here, we present a statistical method called SDPR_admix for calculating PRS in admixed individuals. SDPR_admix characterizes the joint distribution of the effect sizes of a genetic variant with two ancestries to be both zero, ancestry enriched, or shared with correlation. SDPR_admix outperformed other methods in simulations and improved the prediction of real traits in European-African admixed individuals in UK Biobank when trained on the Population Architecture using Genomics and Epidemiology (PAGE) dataset (N = 13,000). Deployment of SDPR_admix on All of Us (N = 52,000) further increased the prediction accuracy by approximately 5-fold on average compared with training on PAGE. This enhancement was achieved with manageable computational time and cost, demonstrating the feasibility of training PRS models on large-scale All of Us data. We provided several examples demonstrating that both ancestral-enriched and shared effects, as included in the SDPR_admix prediction model, are helpful for improving polygenic prediction in admixed populations. We also applied SDPR_admix to construct PRS for admixed Americans with mixture of European and Amerindigenous ancestries and showed that SDPR_admix overall outperformed other methods.

Humans↗
PubMed2002

Bay-0 x Shahdara recombinant inbred line population: a powerful tool for the genetic dissection of complex traits in Arabidopsis.

Natural genetic variation in Arabidopsis is considerable, but has not yet been used extensively as a source of variants to identify new genes of interest. From the cross between two genetically distant ecotypes, Bay-0 and Shahdara, we generated a Recombinant Inbred Line (RIL) population dedicated to Quantitative Trait Locus (QTL) mapping. A set of 38 physically anchored microsatellite markers was created to construct a robust genetic map from the 420 F6 lines. These markers, evenly distributed throughout the five chromosomes, revealed a remarkable equilibrium in the segregation of parental alleles in the genome. As a model character, we have analysed the genetic basis of variation in flowering time in two different environments. The simultaneous mapping of both large- and small-effect QTLs responsible for this variation explained 90% of the total genotypic variance. Two of the detected QTLs colocalize very precisely with FRIGIDA and FLOWERING LOCUS C genes; we provide information on the polymorphism of genes confirming this hypothesis. Another QTL maps in a region where no QTL had been found previously for this trait. This confirms the accuracy of QTL detection using the Bay-0 x Shahdara RIL population, which constitutes the largest in size available so far in Arabidopsis. As an alternative to mutant analysis, this population represents a powerful tool which is currently being used to undertake the genetic dissection of complex metabolic pathways.

Journal Article↗