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PubMed1994

Monte Carlo likelihood in the genetic mapping of complex traits.

Many of the likelihoods arising in the analysis of complex genetic traits, particularly in linkage analysis, are computationally infeasible. Where exact likelihoods cannot be computed, Monte Carlo estimates of likelihoods may provide a satisfactory alternative. Although simulation on pedigrees is straightforward, simulation conditional upon observed phenotypic data is not. However, recent advances in Markov chain Monte Carlo methods have provided a method well suited to this problem. From realizations of underlying genes, simulated under a genetic model, conditional upon observed data, a Monte Carlo estimate of this likelihood surface can be formed. Various sampler and model modifications are needed to enhance the statistical efficiency of the Monte Carlo estimator; as these methods become increasingly developed, this approach becomes a useful tool in resolving the genes contributing to the phenotypes associated with genetically complex diseases.

Algorithms↗
PubMed1995

Detection of genome similarity as an exploratory tool for mapping complex traits.

For one- and two-trait-locus models, we show that the lod score based on affected relative pairs or trios is a monotonically increasing function of the genome similarity measured by the proportion of alleles shared identical by descent (IBD) conditional on observed marker data. These results can be generalized to multitrait-locus models. Thus, we can use conditional probability of genes shared IBD as a tool to reveal chromosomal segments that are likely to harbor the genes underlying the complex traits.

Alleles↗
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↗
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↗
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↗
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↗
PubMed1997

Linkage analysis of complex traits using affected sibpairs: effects of single-locus approximations on estimates of the required sample size.

We investigated the power of the affected sibpair method for detecting a disease locus when the disease is inherited through two bi-allelic loci. The power was computed for all possible values of the gene frequencies and penetrances that lead to a given population prevalence and a given sibling relative risk. A method to generate rapidly all possible models that give a specific population prevalence and relative risk is provided. We applied it to the case of a two-locus disease with a prevalence of 10% and a low sibling relative risk of 1.5. For this particular example, regardless of the true underlying model, a sample size (N = 450 for alpha = 0.05, N = 1,500 for alpha = 0.0001) may be determined such that one would expect enough power (0.80) to detect at least one of the two disease genes. In addition to the general case, we examined a special class of models in which the marginal penetrances at each locus are either recessive or dominant. In this instance, the gene frequencies were excellent predictors of the power afforded by a particular sample size. These methods have been implemented in a C program called SIBPOWER which is freely available from the first author. With this program, investigators can perform their own power calculations for any two-locus model of their choice thus avoiding the need to use single-locus approximations that may grossly underestimate the necessary sample size.

Gene Frequency↗
PubMed1989

HLA and insulin-dependent diabetes: an overview.

The present knowledge of the HLA system and its biological function is summarized as a basis for the subsequent discussion of the associations between this system and insulin-dependent diabetes (IDDM) and some mechanisms that may explain them. Although the serologically detectable DR determinants are still the most handy markers, there is now increasing evidence from studies of restriction enzyme fragment length polymorphism (RFLP) in IDDM that DQ determinants may play a primary role in causing susceptibility and/or resistance to this disease. Thus, it is now evident that about 90% of DR4-positive diabetics carry the DQw8 determinant present in only about 65% of DR4-positive controls. Most recently, it has been claimed that an aspartic acid in position 57 of the DQB1 (DQ-beta-1) chain confers resistance to IDDM. Although this may be true, it does not explain the disproportionate decrease of DR2 or the particularly high risk of DR3/4 heterozygotes, which is still good evidence that several HLA genes are involved. Because Class II antigens show the strongest associations, the most plausible hypothesis about the mechanism(s) involves specific presentation of as yet unknown antigenic peptides to T-helper lymphocytes, which may induced the formation of both anti-islet cell antibodies and T-cytotoxic lymphocytes capable of destroying beta cells. However, T-suppressor lymphocytes also may be involved. If this hypothesis is correct, the most urgent task is to define the antigenic peptides in question, whether they are environmental (e.g., viral) or autologous.

Diabetes Mellitus, Type 1↗
PubMed1995

Segregation and linkage analysis of the complex trait Q1.

Segregation and linkage analysis of GAW9 Problem 2 quantitative trait 1 (Q1) was performed. Eight segregation models comprising all possible combinations of the environmental factor (EF), quantitative trait 2 (Q2), and quantitative trait 3 (Q3) as covariates were considered. Seven of the eight segregation models showed strong evidence for a major gene, the other model was marginal. When all genotypes are known, some evidence for linkage (lod > 2) was found to all three of the markers that affect Q1. Furthermore, four of the eight models each showed some linkage (lod > 2) to two of the three markers that affect Q1 with no false positives. Each of these segregation analysis major genes is a hybrid combination of the true multiple loci that affect Q1.

Alleles↗
PubMed1996

The genetics of pigmentation: from fancy genes to complex traits.

Genes that control mammalian pigmentation interact with each other in intricate networks that have been studied for decades using mouse coat color mutations. Molecular isolation of the affected genes and the ability to study their effects in a defined genetic background have led to surprising new insights into the potential interaction between tyrosine kinase and G-protein-coupled signaling pathways. Recent developments show that homologous genes in humans are responsible not only for rare diseases, such as albinism and piebaldism, but also for common phenotypic variations, such as red hair and fair skin.

Animals↗