			Home About us Contact

Composite Likelihood (composite + likelihood)

Distribution by Scientific Domains

Life Sciences	100%

Selected Abstracts

Case-control single-marker and haplotypic association analysis of pedigree data

GENETIC EPIDEMIOLOGY, Issue 2 2005
Sharon R. Browning
Abstract Related individuals collected for use in linkage studies may be used in case-control linkage disequilibrium analysis, provided one takes into account correlations between individuals due to identity-by-descent (IBD) sharing. We account for these correlations by calculating a weight for each individual. The weights are used in constructing a composite likelihood, which is maximized iteratively to form likelihood ratio tests for single-marker and haplotypic associations. The method scales well with increasing pedigree size and complexity, and is applicable to both autosomal and X chromosomes. We apply the approach to an analysis of association between type 2 diabetes and single-nucleotide polymorphism markers in the PPAR-, gene. Simulated data are used to check validity of the test and examine power. Analysis of related cases has better power than analysis of population-based cases because of the increased frequencies of disease-susceptibility alleles in pedigrees with multiple cases compared to the frequencies of these alleles in population-based cases. Also, utilizing all cases in a pedigree rather than just one per pedigree improves power by increasing the effective sample size. We demonstrate that our method has power at least as great as that of several competing methods, while offering advantages in the ability to handle missing data and perform haplotypic analysis. Genet. Epidemiol. 28:110,122, 2005. © 2004 Wiley-Liss, Inc. [source]

Candidate-gene association studies with pedigree data: Controlling for environmental covariates

GENETIC EPIDEMIOLOGY, Issue 4 2003
S.L. Slager
Abstract Case-control studies provide an important epidemiological tool to evaluate candidate genes. There are many different study designs available. We focus on a more recently proposed design, which we call a multiplex case-control (MCC) design. This design compares allele frequencies between related cases, each of whom are sampled from multiplex families, and unrelated controls. Since within-family genotype correlations will exist, statistical methods will need to take this into account. Moreover, there is a need to develop methods to simultaneously control for potential confounders in the analysis. Generalized estimating equations (GEE) are one approach to analyze this type of data; however, this approach can have singularity problems when estimating the correlation matrix. To allow for modeling of other covariates, we extend our previously developed method to a more general model-based approach. Our proposed methods use the score statistic, derived from a composite likelihood. We propose three different approaches to estimate the variance of this statistic. Under random ascertainment of pedigrees, score tests have correct type I error rates; however, pedigrees are not randomly ascertained. Thus, through simulations, we test the validity and power of the score tests under different ascertainment schemes, and an illustration of our methods, applied to data from a prostate cancer study, is presented. We find that our robust score statistic has estimated type I error rates within the expected range for all situations we considered whereas the other two statistics have inflated type I error rates under nonrandom ascertainment schemes. We also find GEE to fail at least 5% of the time for each simulation configuration; at times, the failure rate reaches above 80%. In summary, our robust method may be the only current regression analysis method available for MCC data. Genet Epidemiol 24:273,283, 2003. © 2003 Wiley-Liss, Inc. [source]

Using linked markers to infer the age of a mutation

HUMAN MUTATION, Issue 2 2001
Bruce Rannala
Abstract Advances in sequencing and genotyping technologies over the last decade have enabled geneticists to easily characterize genetic variation at the nucleotide level. Hundreds of genes harboring mutations associated with genetic disease have now been identified by positional cloning. Using variation at closely linked genetic markers, it is possible to predict the times in the past at which particular mutations arose. Such studies suggest that many of the rare mutations underlying human genetic disorders are relatively young. Studies of variation at genetic markers linked to particular mutations can provide insights into human geographic history, and historical patterns of natural selection and disease, that are not available from other sources. We review two approaches for estimating allele age using variation at linked genetic markers. A phylogenetic approach aims to reconstruct the gene tree underlying a sample of chromosomes carrying a particular mutation, obtaining a "direct" estimate of allele age from the age of the root of this tree. A population genetic approach relies on models of demography, mutation, and/or recombination to estimate allele age without explicitly reconstructing the gene tree. Phylogenetic methods are best suited for studies of ancient mutations, while population genetic methods are better suited for studies of recent mutations. Methods that rely on recombination to infer the ages of alleles can be fine-tuned by choosing linked markers at optimal map distances to maximize the information available about allele age. A limitation of methods that rely on recombination is the frequent lack of a fine-scale linkage map. Maximum likelihood and Bayesian methods for estimating allele age that rely on intensive numerical computation are described, as well as "composite" likelihood and moment-based methods that lead to simple estimators. The former provide more accurate estimates (particularly for large samples of chromosomes) and should be employed if computationally practical. Hum Mutat 18:87,100, 2001. © 2001 Wiley-Liss, Inc. [source]

Mapping quantitative effects of oligogenes by allelic association

ANNALS OF HUMAN GENETICS, Issue 3 2002
W. ZHANG
Regression analysis of a quantitative trait as a function of a single diallelic polymorphism has been extended to allelic association by composite likelihood under the Malecot model for multiple markers. We applied the method to 10 single nucleotide polymorphisms (SNPs) spanning 27 kb of the angiotensin-I converting enzyme (ACE) gene in British families, localising a causal SNP between G2530A and 4656(CT)3/2 in the 3, region, at a distance of 21.6±0.9 kb from the most proximal SNP T-5491C. Neither they nor the I/D polymorphism is causal. To clarify genetic parameters we applied combined segregation, linkage and association analysis. Stronger evidence for the 3, region was obtained, with significant evidence of a lesser 5, effect as reported in French and Nigerian families. However, rigorous confirmation requires that the causal SNPs be identified. Both Malecot and parametric analysis appear to have high power by comparison with alternative methods for localizing oligogenes and their causal polymorphisms. [source]