Identifying Blocks and Sub populations in Noisy SNP Data 1st Edition by Gad Kimmel, Roded Sharan, Ron Shamir 9783540200765

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ISBN 13: 9783540200765

Author: Gad Kimmel, Roded Sharan; Ron Shamir 

We study several problems arising in haplotype block partitioning. Our objective function is the total number of distinct haplotypes in blocks. We show that the problem is NP-hard when there are errors or missing data, and provide approximation algorithms for several of its variants. We also give an algorithm that solves the problem with high probability under a probabilistic model that allows noise and missing data. In addition, we study the multi-population case, where one has to partition the haplotypes into populations and seek a different block partition in each one. We provide a heuristic for that problem and use it to analyze simulated and real data. On simulated data, our blocks resemble the true partition more than the blocks generated by the LD-based algorithm of Gabriel et al. [7]. On single-population real data, we generate a more concise block description than extant approaches, with better average LD within blocks. The algorithm also gives promising results on real 2-population genotype data.

Identifying Blocks and Sub populations in Noisy SNP Data 1st Table of contents:

1.   Introduction

2. Scoring Noisy Blocks

3. Minimum Blo ck Haplotypes

4. Experimental Results

5. Concluding Remarks

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