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PubMed2003

Haplotype sharing analysis with SNPs in candidate genes: the Genetic Analysis Workshop 12 example.

Haplotype sharing analysis was used to investigate the association of affection status with single nucleotide polymorphism (SNP) haplotypes within candidate gene 1 in one sample each from the isolated and the general population of Genetic Analysis Workshop (GAW) 12 simulated data. Gene 1 has direct influence on affection and harbors more than 70 SNPs. Haplotype sharing analysis depends heavily on previous haplotype estimation. Using GENEHUNTER haplotypes, strong evidence was found for most SNPs in the isolated population sample, thus providing evidence for an involvement of this gene, but the maximum -log(10)(p) values for the haplotype sharing statistics (HSS) test statistic did not correspond to the location of the true variant in either population. In comparison, transmission disequilibrium test (TDT) analysis showed the strongest results at the disease-causing variant in both populations, and these were outstanding in the general population. In this example, TDT analysis appears to perform better than HSS in identifying the disease-causing variant, using SNPs within a candidate gene in an outbred population. Simulations showed that the performance of HSS is hampered by closely spaced SNPs in strong linkage disequilibrium with the functional variant and by ambiguous haplotypes.

Chromosome Mapping↗
PubMed2003

Analysis of metabolic syndrome phenotypes in Framingham Heart Study families from Genetic Analysis Workshop 13.

Twelve teams of investigators constituted a group which analyzed phenotypes related to metabolic syndrome, making use of the available longitudinal measurements from the family component of the Framingham Heart Study or the simulated data, as distributed by Genetic Analysis Workshop 13 (GAW13). Body mass index, obesity, lipid abnormalities, glucose, or combinations of these traits were analyzed by this group. A wide variety of approaches were taken to construct phenotypes from the longitudinal measurements, including considering single or multiple cross-sectional time points, single ages, minimum values, maximum values, means, other lifetime values, ever/never dichotomy, or age at onset of some threshold value. Approaches also differed in the family structures utilized (sib pairs to full extended pedigrees), the genetic data considered (two-point or multipoint), and the statistics calculated (model-free and parametric), and led to a diverse set of analyses being performed. Inferences were made about heritability, and attempts were made to map underlying genes. Over 40 genome-wide linkage analyses were conducted. Despite the broad range of approaches, several regions of the genome were repeatedly identified across multiple analyses.

Cardiovascular Diseases↗
PubMed2003

Genetic analysis workshop 13: Summary of analyses of alcohol and cigarette use phenotypes in the Framingham Heart Study.

Data from the Framingham Heart Study were provided for Genetic Analysis Workshop 13. This paper summarizes six contributed papers that focused on the analysis of the longitudinal alcohol and/or cigarette use phenotypes available in these data, with the goal of detecting genes influencing these traits. For several of these contributions, the primary phenotype was maximum daily substance use reported over the longitudinal data. Others focused on a cross section by taking the daily use reported at a fixed time interval for all individuals, and others considered dichotomous phenotypes defined by thresholds of use. A variety of covariates were considered, including age (or year of birth) and sex. Most studies included unexposed individuals (those reporting no alcohol/cigarette use at all available assessments) in the linkage analyses, though one study compared results when defining the phenotype for unexposed individuals to be zero vs. defining the phenotype for unexposed individuals to be unknown. Linkage findings varied across studies. However, there was some concordance of evidence on chromosome 9 for alcohol traits, and of weaker evidence on chromosome 20 for cigarette traits. Analytical issues arose which may be crucial for genetic studies of substance use and dependence, including the choice of how to handle unexposed or substance-naive individuals, use of dichotomous vs. quantitative traits, and consideration of transformations and covariates.

Alcohol Drinking↗
PubMed1984

Genetic Analysis Workshop II: segregation and three-locus linkage analysis.

Data stimulated for Genetic Analysis Workshop II were analyzed using PAP. Segregation analysis showed a simple recessive mode of inheritance for data set 2 while no conclusions could be made about the mode of inheritance for data set 3. Pairwise linkage analysis suggested three linkage groups, but three-locus analysis did not provide strong evidence for the gene order within these groups. For three of the four three-locus comparisons performed, three-locus analysis suggested the simulated order. In only one case did the pairwise analysis suggest the simulated order, indicating the necessity for multi-locus analysis for gene order.

Chromosome Mapping↗
PubMed1984

Genetic Analysis Workshop II: results of segregation analyses using POINTER and linkage analyses using LIPED.

Genetic Analysis Workshop II Problems 2 and 3 were analyzed using the segregation analysis program, POINTER and the linkage analysis program LIPED. Results of the segregation analyses were acceptable with respect to both parameter estimation and hypothesis testing. Results of the linkage analyses were also good. Although it was noted that the linkage and population association data were sometimes compatible with more than one hypothesis, the correct relationships among the trait and marker loci were generally among those found compatible with the data.

Alleles↗
PubMed1984

Genetic Analysis Workshop II: sib pair screening tests for linkage.

For each marker locus and for every pair of sibs with data available in the 1983 workshop data, the proportion of genes identical by descent was estimated. The mean proportions were compared between concordant and discordant sib pairs, and the mean proportion for concordantly affected pairs was compared with one half. Together with standard tests of association, these found to be sensitive screening tests for linkage.

Bayes Theorem↗
PubMed1984

Genetic Analysis Workshop II: results of incorporating a linkage disequilibrium parameter.

The corporation of a linkage disequilibrium parameter, delta, into linkage analysis is illustrated for data from Genetic Analysis Workshop II. Points from a joint likelihood surface are calculated and displayed on a recombination fraction-linkage disequilibrium grid using a simple modification of LIPED. The approach is shown to increase the power of linkage analysis and the power of tests for heterogeneity of linkage for the simulated examples.

Computer Simulation↗
PubMed1984

Genetic Analysis Workshop II: pedigree analysis of a binary trait without assuming an underlying liability.

A model for concordance in a binary measure that does not rely on the assumption of an underlying latent liability dichotomized about a threshold has been demonstrated for twin pairs [Hannah et al, 1983]. It is extended here to pedigrees of arbitrary structure by making an assumption that is, for small incidence rates, almost equivalent to postulating that relative risks are multiplicative. The model is applied to the workshop data to determine the extent to which the known structure of the simulated models can be recovered.

Alleles↗
PubMed1984

Genetic Analysis Workshop II: further consideration of segregation and linkage analyses in Problem 3.

We analyzed disease-marker associations in Problem 3 for the Genetic Analysis Workshop II, using PAP for segregation analysis and LIPED for linkage analysis. In this report we present aspects of our analyses that are not reported in the summary [MacCluer et al, 1984]. Certain features that we added to the running of LIPED to facilitate the analysis are discussed. Furthermore, we tested for Mendelian transmission in the hypothetical trait locus and we calculated modified relative risks for marker-trait genotypes. Some of the problems involved in analyzing complex associations among loci are discussed.

Alleles↗
PubMed1984

Genetic Analysis Workshop II: combined segregation, linkage, and association analysis.

A combined segregation, linkage, and association analysis using the program COMBIN was performed on the simulated pedigree data prepared for the Second Genetic Analysis Workshop. The model used in COMBIN is described and the presented results illustrate its effectiveness in the analysis of such data. Linkage analysis was performed and maps for each linkage group are presented.

Adult↗
PubMed1984

Genetic Analysis Workshop II: segregation and linkage analysis.

Familial segregation and linkage analyses were performed on two sets of the Genetic Analyses Workshop II data. The salient features of the mode of inheritance of the disease trait and its linkage/association with polymorphic markers and also marker-marker linkages were delineated using statistical-genetic techniques.

Adult↗
PubMed1984

Genetic Analysis Workshop II: multiple-locus segregation analysis incorporating linkage markers.

The workshop data were examined using a newly developed methodology (MILINK, Risch, 1984) for combined segregation, linkage, and association analysis of a complex disease trait in pedigree data. Results from problems two and three suggest that the method is powerful both for determining mode of disease inheritance and for resolution of linkage disequilibrium versus pleiotrophy (with epistasis) of marker alleles.

Alleles↗
PubMed2001

Preliminary implementation of new data mining techniques for the analysis of simulation data from Genetic Analysis Workshop 12: problem 2.

We introduce a new data mining method applicable to complex disease genetics. Our approach is suited to a broad spectrum of diseases, identifying the noteworthy sharing of combinations of alleles in unrelated affected individuals. Furthermore, this approach may be extended to comprise the common types of genotype data, including single-nucleotide polymorphisms, candidate-gene sequences, etc. Using a method derived from data-mining computer algorithms, we analyze a data set of unrelated affected individuals chosen from the simulated pedigrees of problem 2 of the Genetics Analysis Workshop 12. We observe that most marker subsets containing a flanking marker for each of six or seven of the disease-gene loci yield significant numbers of individuals manifesting substantially similar genotypes. However, initial attempts (blind to the generating model) to identify the predisposing loci have not been successful. Refining our methods so that such loci may routinely be found and validated is underway.

Algorithms↗
PubMed2001

Linkage analysis of Genetic Analysis Workshop 12 simulated data based on affected individuals only.

A variety of allele-sharing methods was used to assess linkage to disease for a whole-genome screen in each of ten replicates from the Genetic Analysis Workshop 12 simulated data. Analysis of each replicate produced many false positive results in addition to a few "true" positives. Conditioning on the strongest initial linkage and reanalyzing using a variety of conditional methods did not improve the power for detection or help discriminate between true and false positive signals.

Alleles↗
PubMed2001

Common disease analysis using Multivariate Adaptive Regression Splines (MARS): Genetic Analysis Workshop 12 simulated sequence data.

A newly developed modern analytic approach, Multivariate Adaptive Regression Splines (MARS), was used to identify both genetic and non-genetic factors involved in the etiology of a common disease. We tested this method on the simulated data provided by the Genetic Analysis Workshop (GAW) 12 in problem 2 for the isolated population. MARS simultaneously analyzes all inputs, in this case DNA sequence variants and non-genetic data, and selectively prunes away variables contributing insignificantly to fit by internal cross-validation to arrive at a generalizable predictive model of the response. The relevant factors identified, by means of an importance value computed by MARS, were assumed to be associated with risk to the disease. The application of a series of subsequent models identified the quantitative traits and a single major gene contributing directly to risk liability using five sets of 7,000 individuals.

Genetic Predisposition to Disease↗
PubMed2005

Linkage mapping methods applied to the COGA data set: presentation Group 4 of Genetic Analysis Workshop 14.

Presentation Group 4 participants analyzed the Collaborative Study on the Genetics of Alcoholism data provided for Genetic Analysis Workshop 14. This group examined various aspects of linkage analysis and related issues. Seven papers included linkage analyses, while the eighth calculated identity-by-descent (IBD) probabilities. Six papers analyzed linkage to an alcoholism phenotype: ALDX1 (four papers), ALDX2 (one paper), or a combination both (one paper). Methods used included Bayesian variable selection coupled with Haseman-Elston regression, recursive partitioning to identify phenotype and covariate groupings that interact with evidence for linkage, nonparametric linkage regression modeling, affected sib-pair linkage analysis with discordant sib-pair controls, simulation-based homozygosity mapping in a single pedigree, and application of a propensity score to collapse covariates in a general conditional logistic model. Alcoholism linkage was found with > or =2 of these approaches on chromosomes 2, 4, 6, 7, 9, 14, and 21. The remaining linkage paper compared the utility of several single-nucleotide polymorphism (SNP) and microsatellite marker maps for Monte Carlo Markov chain combined oligogenic segregation and linkage analysis, and analyzed one of the electrophysiological endophenotypes, ttth1, on chromosome 7. Linkage was found with all marker sets. The last paper compared the multipoint IBD information content of several SNP sets and the microsatellite set, and found that while all SNP sets examined contained more information than the microsatellite set, most of the information contained in the SNP sets was captured by a subset of the SNP markers with approximately 1-cM marker spacing. From these papers, we highlight three points: a 1-cM SNP map seems to capture most of the linkage information, so denser maps do not appear necessary; careful and appropriate use of covariates can aid linkage analysis; and sources of increased gene-sharing between relatives should be accounted for in analyses.

Alcoholism↗
PubMed2005

Haplotypes and haplotype-tagging single-nucleotide polymorphism: presentation Group 8 of Genetic Analysis Workshop 14.

Moderately dense maps of single-nucleotide polymorphism (SNP) markers across the human genome for both the simulated data set and data from the Collaborative Study of the Genetics of Alcoholism were available at Genetic Analysis Workshop 14 for the first time. This allowed examination of various novel and existing methods for haplotype analyses. Three contributors applied Mantel statistics in different ways for both linkage and association analysis by using the shared length between two haplotypes at a marker locus as a measure of genetic similarity. The results indicate that haplotype-sharing based on Mantel statistics can be a powerful approach and needs further methodological evaluation. Four contributors investigated haplotype-tagging SNP (htSNP) selection procedures, two contributors examined the use of multilocus haplotypes compared to single loci in association tests, and two contributors compared the accuracy of various methods for reconstructing haplotypes and estimating haplotype frequencies for both pedigree data and data from unrelated individuals. For all three different tasks, software packages and procedures gave similar results in regions of high linkage disequilibrium (LD). However, they were not as consistent in regions of moderate to low LD. One coalescence-based approach for estimating haplotype frequencies, coupled with a Markov chain Monte Carlo technique, outperformed the other haplotype frequency estimation methods in regions of low LD. In conclusion, regardless of the task, results were similar in chromosomal regions of high LD. However, based on the differing results observed here, methodological improvements are required for chromosomal regions of low to moderate LD.

Alcoholism↗
PubMed2005

Approaches to detecting gene x gene interaction in Genetic Analysis Workshop 14 pedigrees.

Whether driven by the general lack of success in finding single-gene contributions to complex disease, by increased knowledge about the potential involvement of specific biological interactions in complex disease, or by recent dramatic increases in computational power, a large number of approaches to detect locus x locus interactions were recently proposed and implemented. The six Genetic Analysis Workshop 14 (GAW14) papers summarized here each applied either existing or refined approaches with the goal of detecting gene x gene, or locus x locus, interactions in the GAW14 data. Five of six papers analyzed the simulated data; the other analyzed the Collaborative Study on the Genetics of Alcoholism data. The analytic strategies implemented for detecting interactions included multifactor dimensionality reduction, conditional linkage analysis, nonparametric linkage correlation, two-locus parametric linkage analysis, and a joint test of linkage and association. Overall, most of the groups found limited success in consistently detecting all of the simulated interactions due, in large part, to the nature of the generating model.

Alcoholism↗