Haplotyping as Perfect Phylogeny

نویسنده

  • Hakan Seyalioglu
چکیده

For diploid organisms (e.g. humans), each chromosome is present in two non-exact copies and the description of all the data from a single chromosome is called a haplotype. Obtaining haplotype data is important in applications such as analyzing complex diseases, however this is a very difficult problem to solve experimentally and finding mixed genotype data is much less technically difficult and cost effective. So, while we can determine that at a specific site (if we call the two states, or alleles this state can occupy 0 and 1), an individual may have either two 0’s, two 1’s or one 0 and one 1, in the last case, distinguishing which state comes from which chromosome is very hard to discern experimentally. Therefore, we have the problem that experimentally, we may not be able to distinguish between the case where the haplotype of an individual is either of the below pairs: {( 0 0 )

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Perfect Path Phylogeny Haplotyping with Missing Data Is Fixed-Parameter Tractable

Haplotyping via perfect phylogeny is a method for retrieving haplotypes from genotypes. Fast algorithms are known for computing perfect phylogenies from complete and error-free input instances—these instances can be organized as a genotype matrix whose rows are the genotypes and whose columns are the single nucleotide polymorphisms under consideration. Unfortunately, in the more realistic setti...

متن کامل

Efficient Computation of Template Matrices

The computation of template matrices is the bottleneck of simple algorithms for perfect phylogeny haplotyping and for perfect phylogeny under mutation and constrained recombination. The fastest algorithms known so far compute them in O(nm) time. In this paper, we describe an algorithm for computing template matrices in O(nm/ log(n)) time. We also present and discuss a conjecture that implies an...

متن کامل

Haplotyping with missing data via perfect path phylogenies

Computational methods for inferring haplotype information from genotype data are used in studying the association between genomic variation and medical condition. Recently, Gusfield proposed a haplotype inference method that is based on perfect phylogeny principles. A fundamental problem arises when one tries to apply this approach in the presence of missing genotype data, which is common in pr...

متن کامل

Computational Complexity of Perfect-Phylogeny-Related Haplotyping Problems

Haplotyping, also known as haplotype phase prediction, is the problem of predicting likely haplotypes based on genotype data. This problem, which has strong practical applications, can be approached using both statistical as well as combinatorial methods. While the most direct combinatorial approach, maximum parsimony, leads to NP-complete problems, the perfect phylogeny model proposed by Gusfi...

متن کامل

On the Complexity of SNP Block Partitioning Under the Perfect Phylogeny Model

Recent technologies for typing single nucleotide polymorphisms (SNPs) across a population are producing genome-wide genotype data for tens of thousands of SNP sites. The emergence of such large data sets underscores the importance of algorithms for large-scale haplotyping. Common haplotyping approaches first partition the SNPs into blocks of high linkage-disequilibrium, and then infer haplotype...

متن کامل

Phylogeny- and Parsimony-Based Haplotype Inference with Constraints

Haplotyping, also known as haplotype phase prediction, is the problem of predicting likely haplotypes based on genotype data. One fast computational haplotyping method is based on an evolutionary model where a perfect phylogenetic tree is sought that explains the observed data. In their CPM’09 paper, Fellows et al. studied an extension of this approach that incorporates prior knowledge in the f...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010