Showing papers by "Michael Boehnke published in 2000"

Improved Inference of Relationship for Pairs of Individuals

Abstract: Linkage analyses of genetic diseases and quantitative traits generally are performed using family data. These studies assume the relationships between individuals within families are known correctly. Misclassification of relationships can lead to reduced or inappropriately increased evidence for linkage. Boehnke and Cox (1997) presented a likelihood-based method to infer the most likely relationship of a pair of putative sibs. Here, we modify this method to consider all possible pairs of individuals in the sample, to test for additional relationships, to allow explicitly for genotyping error, and to include X-linked data. Using autosomal genome scan data, our method has excellent power to differentiate monozygotic twins, full sibs, parent-offspring pairs, second-degree (2°) relatives, first cousins, and unrelated pairs but is unable to distinguish accurately among the 2° relationships of half sibs, avuncular pairs, and grandparent-grandchild pairs. Inclusion of X-linked data improves our ability to distinguish certain types of 2° relationships. Our method also models genotyping error successfully, to judge by the recovery of MZ twins and parent-offspring pairs that are otherwise misclassified when error exists. We have included these extensions in the latest version of our computer program RELPAIR and have applied the program to data from the Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus (FUSION) study.

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. I. An autosomal genome scan for genes that predispose to type 2 diabetes.

Abstract: We performed a genome scan at an average resolution of 8 cM in 719 Finnish sib pairs with type 2 diabetes. Our strongest results are for chromosome 20, where we observe a weighted maximum LOD score (MLS) of 2.15 at map position 69.5 cM from pter and secondary weighted LOD-score peaks of 2.04 at 56.5 cM and 1.99 at 17.5 cM. Our next largest MLS is for chromosome 11 (MLS = 1.75 at 84.0 cM), followed by chromosomes 2 (MLS = 0.87 at 5.5 cM), 10 (MLS = 0.77 at 75.0 cM), and 6 (MLS = 0.61 at 112.5 cM), all under an additive model. When we condition on chromosome 2 at 8.5 cM, the MLS for chromosome 20 increases to 5.50 at 69.0 cM (P=.0014). An ordered-subsets analysis based on families with high or low diabetes-related quantitative traits yielded results that support the possible existence of disease-predisposing genes on chromosomes 6 and 10. Genomewide linkage-disequilibrium analysis using microsatellite marker data revealed strong evidence of association for D22S423 (P=.00007). Further analyses are being carried out to confirm and to refine the location of these putative diabetes-predisposing genes.

A Multipoint Method for Detecting Genotyping Errors and Mutations in Sibling-Pair Linkage Data

Abstract: The identification of genes contributing to complex diseases and quantitative traits requires genetic data of high fidelity, because undetected errors and mutations can profoundly affect linkage information The recent emphasis on the use of the sibling-pair design eliminates or decreases the likelihood of detection of genotyping errors and marker mutations through apparent Mendelian incompatibilities or close double recombinants In this article, we describe a hidden Markov method for detecting genotyping errors and mutations in multilocus linkage data Specifically, we calculate the posterior probability of genotyping error or mutation for each sibling-pair-marker combination, conditional on all marker data and an assumed genotype-error rate The method is designed for use with sibling-pair data when parental genotypes are unavailable Through Monte Carlo simulation, we explore the effects of map density, marker-allele frequencies, marker position, and genotype-error rate on the accuracy of our error-detection method In addition, we examine the impact of genotyping errors and error detection and correction on multipoint linkage information We illustrate that even moderate error rates can result in substantial loss of linkage information, given efforts to fine-map a putative disease locus Although simulations suggest that our method detects ≤50% of genotyping errors, it generally flags those errors that have the largest impact on linkage results For high-resolution genetic maps, removal of the errors identified by our method restores most or nearly all the lost linkage information and can be accomplished without generating false evidence for linkage by removing incorrectly identified errors

The Finland–United States Investigation of Non–Insulin-Dependent Diabetes Mellitus Genetics (FUSION) Study. II. An Autosomal Genome Scan for Diabetes-Related Quantitative-Trait Loci

Abstract: Type 2 diabetes mellitus is a complex disorder encompassing multiple metabolic defects. We report results from an autosomal genome scan for type 2 diabetes-related quantitative traits in 580 Finnish families ascertained for an affected sibling pair and analyzed by the variance components-based quantitative-trait locus (QTL) linkage approach. We analyzed diabetic and nondiabetic subjects separately, because of the possible impact of disease on the traits of interest. In diabetic individuals, our strongest results were observed on chromosomes 3 (fasting C-peptide/glucose: maximum LOD score [MLS] = 3.13 at 53.0 cM) and 13 (body-mass index: MLS = 3.28 at 5.0 cM). In nondiabetic individuals, the strongest results were observed on chromosomes 10 (acute insulin response: MLS = 3.11 at 21.0 cM), 13 (2-h insulin: MLS = 2.86 at 65.5 cM), and 17 (fasting insulin/glucose ratio: MLS = 3.20 at 9.0 cM). In several cases, there was evidence for overlapping signals between diabetic and nondiabetic individuals; therefore we performed joint analyses. In these joint analyses, we observed strong signals for chromosomes 3 (body-mass index: MLS = 3.43 at 59.5 cM), 17 (empirical insulin-resistance index: MLS = 3.61 at 0.0 cM), and 19 (empirical insulin-resistance index: MLS = 2.80 at 74.5 cM). Integrating genome-scan results from the companion article by Ghosh et al., we identify several regions that may harbor susceptibility genes for type 2 diabetes in the Finnish population.

A look at linkage disequilibrium.

Abstract: The extent of linkage disequilibrium critically determines the efficiency of strategies to identify genetic variants that predispose to human disease. New data indicate that the extent of disequilibrium is highly variable across the genome, and that differences in disequilibrium levels between isolated and mixed populations are modest.

Analysis of Human Genetic Linkage, Third Edition. By Jurg Ott. Baltimore and London: The Johns Hopkins University Press, 1999. Pp. 405. $55.00.

Abstract: The golden age of linkage analysis continues, as disease gene mapping and cloning move forward at an unprecedented rate. Dense maps of microsatellite markers are in common use, and much denser maps of single nucleotide polymorphisms soon will be available. Although mapping genes for common familial (complex) diseases remains a daunting task, the anticipated completion of the human DNA sequence, the growing human transcript map, and the prospect of a near-complete catalog of common human genetic variants all offer hope for more rapid success. Improved statistical methods and more-powerful computational resources also are playing an important role in moving the field of gene mapping forward. With these continuing changes, the appearance of the third edition of Dr. Ott's now classic text is most welcome.The goal of the book remains the same: to provide a concise, easy-to-read introduction to human linkage analysis. Like the revised edition, the third edition includes chapters on basic genetic principles, genetic loci and genetic polymorphisms, aspects of statistical inference, basics of linkage analysis, the informativeness of family data, multipoint linkage analysis, penetrance, variability of the recombination fraction, numerical and computerized methods, and inconsistencies. Although the titles are the same, additions and modifications have been made in each of these chapters. The reference list also has been updated to reflect the substantial advances in the field in the past 8 years. A larger, more-readable typeface and judicious use of subheadings make for a more reader-friendly text.In response to the growing emphasis on complex diseases, the author has replaced the chapter on linkage analysis with disease loci with chapters on linkage analysis with Mendelian disease loci, nonparametric methods, two-locus inheritance, complex traits, and quantitative phenotypes. The result is a much more extensive treatment of sib-pair and relative-pair methods of linkage analysis (including both current nonparametric and semiparametric approaches) and inclusion of two-trait locus methods and tests of linkage disequilibrium.Just as for the first two editions, an understanding of basic algebra and equations is required. Some knowledge of single-variable calculus and probability is necessary to follow the mathematical arguments. However, the book is well signposted, so that the mathematically less inclined reader can skip the more mathematically challenging sections. The book remains informal rather than technically rigorous. The more mathematically inclined reader might wish to consult other books, including those by Lange (1997), Weir (1996), and Thompson (1986). However, none of these other books specifically focuses on linkage analysis, so that even for the mathematically inclined, Dr. Ott's book continues to fill an important niche.The author is to be commended for his efforts to improve the book not only at times of publication of a new edition but also in between, by use of the internet. He encourages reader comments and keeps a list of corrections on his web site (http://linkage.rockefeller.edu/ott/corr-ott3.htm). Also notable is a page with remarks on new developments and relevant references (http://linkage.rockefeller.edu/ott/booknotes.html).As with any book, one can quarrel with details of emphasis. Although the chapter on quantitative traits is a welcome addition, the subject is treated only briefly, and, in particular, modern variance-components methods of linkage analysis are given less than a page. Even the rather extensive new material on complex traits only scratches the surface of a very important area.In summary, a good and useful book has been updated and improved. Dr. Ott's book remains the standard reference on human gene mapping, and the new edition merits a place in the library of anyone with a serious interest in the topic.