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Haplotype Inference through Sequenti...

Iliadis, Alexandros.

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Haplotype Inference through Sequential Monte Carlo.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Haplotype Inference through Sequential Monte Carlo./
作者:	Iliadis, Alexandros.
面頁冊數:	114 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Contained By:	Dissertation Abstracts International74-09B(E).
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3562200
ISBN:	9781303097478

Haplotype Inference through Sequential Monte Carlo.
Iliadis, Alexandros.

Haplotype Inference through Sequential Monte Carlo. - 114 p.

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.

Thesis (Ph.D.)--Columbia University, 2013.

Technological advances in the last decade have given rise to large Genome Wide Studies which have helped researchers get better insights in the genetic basis of many common diseases. As the number of samples and genome coverage has increased dramatically it is currently typical that individuals are genotyped using high throughput platforms to more than 500,000 Single Nucleotide Polymorphisms.

ISBN: 9781303097478Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Haplotype Inference through Sequential Monte Carlo.
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245 1 0 $a Haplotype Inference through Sequential Monte Carlo.
300 $a 114 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
500 $a Adviser: Dimitris Anastassiou.
502 $a Thesis (Ph.D.)--Columbia University, 2013.
520 $a Technological advances in the last decade have given rise to large Genome Wide Studies which have helped researchers get better insights in the genetic basis of many common diseases. As the number of samples and genome coverage has increased dramatically it is currently typical that individuals are genotyped using high throughput platforms to more than 500,000 Single Nucleotide Polymorphisms.
520 $a At the same time theoretical and empirical arguments have been made for the use of haplotypes, i.e. combinations of alleles at multiple loci in individual chromosomes, as opposed to genotypes so the problem of haplotype inference is particularly relevant. Existing haplotyping methods include population based methods, methods for pooled DNA samples and methods for family and pedigree data.
520 $a Furthermore, the vast amount of available data pose new challenges for haplotyping algorithms. Candidate methods should scale well to the size of the datasets as the number of loci and the number of individuals are well to the thousands. In addition, as genotyping can be performed routinely, researchers encounter a number of specific new scenarios, which can be seen as hybrid between the population and pedigree inference scenarios and require special care to incorporate the maximum amount of information.
520 $a In this thesis we present a Sequential Monte Carlo framework (TDS) and tailor it to address instances of haplotype inference and frequency estimation problems. Specifically, we first adjust our framework to perform haplotype inference in trio families resulting in a methodology that demonstrates an excellent tradeoff between speed and accuracy. Consequently, we extend our method to handle general nuclear families and demonstrate the gain using our approach as opposed to alternative scenarios. We further address the problem of haplotype inference in pooling data in which we show that our method achieves improved performance over existing approaches in datasets with large number of markers. We finally present a framework to handle the haplotype inference problem in regions of CNV/SNP data. Using our approach we can phase datasets where the ploidy of an individual can vary along the region and each individual can have different breakpoints.
590 $a School code: 0054.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Engineering, General. $3 1020744
650 4 $a Biology, Genetics. $3 1017730
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792 $a 2013
793 $a English
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