Biomedical subjects

George E Bonney

Publications and source records attributed to George E Bonney.

8 recordsLinked to original sources

PubMed2005

Genes, age, and alcoholism: analysis of GAW14 data.

A genetic analysis of age of onset of alcoholism was performed on the Collaborative Study on the Genetics of Alcoholism data released for Genetic Analysis Workshop 14. Our study illustrates an application of the log-normal age of onset model in our software Genetic Epidemiology Models (GEMs). The phenotype ALDX1 of alcoholism was studied. The analysis strategy was to first find the markers of the Affymetrix SNP dataset with significant association with age of onset, and then to perform linkage analysis on them. ALDX1 revealed strong evidence of linkage for marker tsc0041591 on chromosome 2 and suggestive linkage for marker tsc0894042 on chromosome 3. The largest separation in mean ages of onset of ALDX1 was 19.76 and 24.41 between male smokers who are carriers of the risk allele of tsc0041591 and the non-carriers, respectively. Hence, male smokers who are carriers of marker tsc0041591 on chromosome 2 have an average onset of ALDX1 almost 5 years earlier than non-carriers.

Age of Onset↗

PubMed2005

A study of genetic association with electrophysiological measures related to alcoholism: GAW14 data.

Recently, alcohol-related traits have been shown to have a genetic component. Here, we study the association of specific genetic measures in one of the three sets of electrophysiological measures in families with alcoholism distributed as part of the Genetic Analysis Workshop 14 data, the NTTH (non-target case of Visual Oddball experiment for 4 electrode placements) phenotypes: ntth1, ntth2, ntth3, and ntth4. We focused on the analysis of the 786 Affymetrix markers on chromosome 4. Our desire was to find at least a partial answer to the question of whether ntth1, ntth2, ntth3, and ntth4 are separately or jointly genetically controlled, so we studied the principal components that explain most of the covariation of the four quantitative traits. The first principal component, which explains 70% of the covariation, showed association but not genetic linkage to two markers: tsc0272102 and tsc0560854. On the other hand, ntth1 appeared to be the trait driving the variation in the second principal component, which showed association and genetic linkage at markers in four regions: tsc0045058, tsc1213381, tsc0055068, and tsc0051777 at map distances 53.26, 85.42, 89.31, and 172.86, respectively. These results show that the partial answer to our starting question for this brief analysis is that the NTTH phenotypes are not jointly genetically controlled. The component ntth1 displays marked genetic linkage.

Alcoholism↗

PubMed2005

Which strategy is better for linkage analysis: single-nucleotide polymorphisms or microsatellites? Evaluation by identity-by-state-identity-by-descent transformation affected sib-pair method on GAW14 data.

The central issue for Genetic Analysis Workshop 14 (GAW14) is the question, which is the better strategy for linkage analysis, the use of single-nucleotide polymorphisms (SNPs) or microsatellite markers? To answer this question we analyzed the simulated data using Duffy's SIB-PAIR program, which can incorporate parental genotypes, and our identity-by-state - identity-by-descent (IBS-IBD) transformation method of affected sib-pair linkage analysis which uses the matrix transformation between IBS and IBD. The advantages of our method are as follows: the assumption of Hardy-Weinberg equilibrium is not necessary; the parental genotype information maybe all unknown; both IBS and its related IBD transformation can be used in the linkage analysis; the determinant of the IBS-IBD transformation matrix provides a quantitative measure of the quality of the marker in linkage analysis. With the originally distributed simulated data, we found that 1) for microsatellite markers there are virtually no differences in types I and II error rates when parental genotypes were or were not used; 2) on average, a microsatellite marker has more power than a SNP marker does in linkage detection; 3) if parental genotype information is used, SNP markers show lower type I error rates than microsatellite markers; and 4) if parental genotypes are not available, SNP markers show considerable variation in type I error rates for different methods.

Chromosome Mapping↗

PubMed2005

A statistical framework for haplotype block inference.

The existence of haplotype blocks transmitted from parents to offspring has been suggested recently. This has created an interest in the inference of the block structure and length. The motivation is that haplotype blocks that are characterized well will make it relatively easier to quickly map all the genes carrying human diseases. To study the inference of haplotype block systematically, we propose a statistical framework. In this framework, the optimal haplotype block partitioning is formulated as the problem of statistical model selection; missing data can be handled in a standard statistical way; population strata can be implemented; block structure inference/hypothesis testing can be performed; prior knowledge, if present, can be incorporated to perform a Bayesian inference. The algorithm is linear in the number of loci, instead of NP-hard for many such algorithms. We illustrate the applications of our method to both simulated and real data sets.

Algorithms↗

PubMed2004

Identifying the susceptibility gene(s) in a set of trait-linked genes using genotype data.

There are generally three steps to isolate a disease linkage-susceptibility gene: genome-wide scan, fine mapping, and, last, positional cloning. The last step is time consuming and involves intensive laboratory work. In some cases, fine mapping cannot proceed further on a set of markers because they are tightly linked. For years, genetic statisticians have been trying different ways to narrow the fine-mapping results to provide some guidance for the next step of laboratory work. Although these methods are practical and efficient, most of them are based on IBD data, which usually can be inferred only from the genotype data with some uncertainty. The corresponding methods thus have no greater power than one using genotype data directly. Also, IBD-based methods apply only to relative pair data. Here, using genotype data, we have developed a statistical hypothesis-testing method to pinpoint a SNP, or SNPs, suspected of responsibility for a disease trait linkage among a set of SNPs tightly linked in a region. Our method uses genotype data of affected individuals or case-control studies, which are widely available in the laboratory. The testing statistic can be constructed using any genotype-based disease-marker disequilibrium measure and is asymptotically distributed as a chi-square mixture. This method can be used for singleton data, relative pair data, or general pedigree data. We have applied the method to simulated data as well as a real data set; it gives satisfactory results.

Chromosome Mapping↗

PubMed2003

Exact test of Hardy-Weinberg equilibrium by Markov chain Monte Carlo.

The assumption of Hardy-Weinberg equilibrium (HWE) among alleles is of fundamental importance in genetic studies. There are numerous testing methods for it using genotype counts data. The exact test is used when the sample size is not large enough for asymptotic approximations. There are several numerical methods to carry out this test, such as complete enumeration, Monte Carlo and Markov chain Monte Carlo simulations. Complete enumeration is impractical in many applications, especially when the table counts are large. The Monte Carlo method is simple to use but still difficult when the table counts become large. The Markov chain Monte Carlo method, by sampling a sub-table each time, is suitable for this latter situation. Based on switches among a few (no more than four) cells, the existing Markov chain samplers are highly dependent and inefficient for large tables. Here we consider a new Markov chain sampling, in which a sub-table of user-specified size is updated at each iteration. The resulting chain is less dependent, and the sampling is flexible and efficient. The conventional test for HWE is based on a few test statistics, such as the likelihood and the chi-squared statistic. To expand the family of test statistics, we consider a class of divergence measures for the departure of HWE. Examples are given as illustrations.

Alleles↗

PubMed2002

Interactions between genetic and reproductive factors in breast cancer risk in a population-based sample of African-American families.

Incidence of breast cancer (BC) varies among ethnic groups, with higher rates in white than in African-American women. Until now, most epidemiological and genetic studies have been carried out in white women. To investigate whether interactions between genetic and reproductive risk factors may explain part of the ethnic disparity in BC incidence, a genetic epidemiology study was conducted, between 1989 and 1994, at the Howard University Cancer Center (Washington, DC), which led to the recruitment of 245 African-American families. Segregation analysis of BC was performed by use of the class D regressive logistic model that allows for censored data to account for a variable age of onset of disease, as implemented in the REGRESS program. Segregation analysis of BC was consistent with a putative dominant gene effect (P < 0.000001) and residual sister-dependence (P < 0.0001). This putative gene was found to interact significantly with age at menarche (P = 0.048), and an interaction with a history of spontaneous abortions was suggested (P = 0.08). A late age at menarche increased BC risk in gene carriers but had a protective effect in non-gene carriers. A history of spontaneous abortions had a protective effect in gene carriers and increased BC risk in non-gene carriers. Our findings agree partially with a similar analysis of French families showing a significant gene x parity interaction and a suggestive gene x age at menarche interaction. Investigating gene x risk factor interactions in different populations may have important implications for further biological investigations and for BC risk assessment.

Abortion, Spontaneous↗

PubMed2002

Two new recursive likelihood calculation methods for genetic analysis.

Recursive likelihood calculations for genetic analysis with ungenotyped pedigree data employ variations of the Elston-Stewart (ES) or the Lander-Green (LG) algorithms. With the ES algorithm, the number of loci may be limited but not the pedigree size. With the LG algorithm, the reverse is the case. We introduce two new algorithms for the computation of regressive likelihoods for pedigrees with multivariate traits. The first is an alternative formulation of our existing model, which leads to a simpler form in the binary trait, polygenic and mixed model cases. The second is an approximation model, which is computationally efficient. These methods apply to both continuous and binary traits, in the oligogenic and polygenic cases. Both methods coincide in the binary case. We considered these methods for cases in which all the traits are controlled by a single locus, with each trait controlled by one locus independent to the others. Simulation studies and analysis of a real data are presented for segregation analysis as illustrations. These methods can also be used in other model-based analyses. These methods are implemented in G.E.M.S., the genetic epidemiology models software.

Animals↗