Computational Methods for SNPs and Haplotype Inference

Trisomic Phase Inference.- Trisomic Phase Inference.- An Overview of Combinatorial Methods for Haplotype Inference.- A Survey of Computational Methods for Determining Haplotypes.- Haplotype Inference and Its Application in Linkage Disequilibrium Mapping.- Inferring Piecewise Ancestral History from Haploid Sequences.- Haplotype Blocks in Small Populations.- Simulating a Coalescent Process with Recombination and Ascertainment.- Dynamic Programming Algorithms for Haplotype Block Partitioning and Tag SNP Selection Using Haplotype Data or Genotype Data.- Parametric Bootstrap for Assessment of Goodness of Fit of Models for Block Haplotype Structure.- A Coalescent-Based Approach for Complex Disease Mapping.- Abstracts.- Haplotyping as Perfect Phylogeny.- Exhaustive Enumeration and Bayesian Phase Inference.- How Does Choice of Polymorphism Influence Estimation of LD and Mapping?.- Haplotype Inference in Random Population Samples.- Bayesian Methods for Statistical Reconstruction of Haplotypes.- Combinatorial Approaches to Haplotype Inference.- Large Scale Recovery of Haplotypes from Genotype Data Using Imperfect Phylogeny.- Haplotype Inference and Haplotype Information.- Multi-locus Linkage Disequilibrium and Haplotype-Based Tests of Association.- The Pattern of Polymorphism on Human Chromosome 21.- Use of a Local Approximation to the Ancestral Recombination Graph for Fine Mapping Disease Genes.- Insights into Recombination from Patterns of Linkage Disequilibrium.- Joint Bayesian Estimation of Mutation Location and Age Using Linkage Disequilibrium.- Evolutionary-Based Association Analysis Using Haplotype Data.- Inferring Piecewise Ancestral History from Haploid Sequences.- Testing for Differences in Haplotype Frequencies in Case-Control Studies.- Haplotypes, Hotspots, and a Multilocus Model for Linkage Disequilibrium.- Dynamic Programming Algorithms for Haplotype Block Partition and Applications to Association Studies.- Genome Sharing in Small Populations.- Patterns of

Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.