Biomedical subjects
Publications and source records attributed to L C Lazzeroni.
The past decade produced several proposals for fine-scale gene mapping using linkage disequilibrium data. The suggested methods fall into two main groups, those that rely on pairwise statistics and those that rely on haplotypes. This paper reviews each strategy's development from a chronological perspective.
This paper proposes a novel approach for fine-scale mapping of disease genes that is based on the well-known linkage-disequilibrium parameter delta. Using a very simple, very general model, I show how delta can be interpreted in terms of identity-by-descent probabilities. The value of delta follows a piecewise curve along the chromosome, with the maximum occurring at the disease locus where the two pieces intersect. A semiparametric, multilocus approach is used to fit this nonlinear regression curve in order to estimate the gene location. Using the bootstrap to empirically estimate much of the probability model from the data avoids the need for many detailed population assumptions. One advantage of the approach is its use of the observed covariance structure of the data, which can be highly informative as to the gene location. I illustrate the method on the cystic fibrosis data of Kerem et al.
We have evaluated the sequence diversity of the protease human immunodeficiency virus type 1 in vivo. Our analysis of 246 protease coding domain sequences obtained from 12 subjects indicates that amino acid substitutions predicted to give rise to protease inhibitor resistance may be present in patients who have not received protease inhibitors. In addition, we demonstrated that amino acid residues directly involved in enzyme-substrate interactions may be varied in infected individuals. Several of these substitutions occurred in combination either more or less frequently than would be expected if their appearance was independent, suggesting that one substitution may compensate for the effects of another. Taken together, our analysis indicates that the human immunodeficiency virus type 1 protease has flexibility sufficient to vary critical subsites in vivo, thereby retaining enzyme function and viral pathogenicity.
Sperm typing was used to measure recombination fractions among pseudoautosomal markers and the beginning of the X/Y-specific sequences located at the pseudoautosomal boundary. These experiments included primer-extension preamplification and PCR followed by allele typing using gel electrophoresis. A newly developed data-analysis program allowed the construction of the first multipoint-linkage sperm-typing map, using results obtained on seven loci from three individuals. The large sample size not only confirmed the increased recombination activity of the pseudoautosomal region but allowed an estimate of interference of recombination to be made. The coefficient of coincidence was calculated to be .26 over a physical distance of only approximately 1,800 kb. The observation of a few sperm presumably resulting from double recombination argues that more than one crossover event can occur in this region during male meiosis.
This paper explains how multipoint likelihoods can be computed for sperm-typing data. Experimental errors such as multiple sperm per tube, inadequate amplification, and contamination by exogenous DNA are explicitly taken into account. By limiting the number of sperm theoretically possible per tube to a predetermined maximum and by assuming no chiasma interference, maximum-likelihood estimation can be carried out rapidly using the theory of hidden Markov chains.
The transmission/disequilibrium test (TDT) of Terwilliger and Ott [Hum Hered 1992;42:337-346] and Spielman et al. [Am J Hum Genet 1993;52:506-516] is widely used to detect linkage and/or association between a genetically influenced disease and the alleles of a codominant marker locus. The TDT was specifically designed to avoid the spurious population associations produced by ethnic stratification of a sample of affected people. In this paper, we describe permutation extensions of the TDT that share this advantage. Our conditional inference framework permits extensions to multiple alleles, multiple loci, unaffected siblings, and genotypic rather than allelic associations. In the case of multiple loci, the conditional perspective provides a straightforward correction for multiple tests that can be substantially more powerful than the standard Bonferroni correction.