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Identification of mutations affectin...

Tzeng, Jung-Ying.

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Identification of mutations affecting liability to complex disease by the analysis of haplotypes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identification of mutations affecting liability to complex disease by the analysis of haplotypes./
Author:	Tzeng, Jung-Ying.
Description:	125 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-06, Section: B, page: 2741.
Contained By:	Dissertation Abstracts International64-06B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3095067

Identification of mutations affecting liability to complex disease by the analysis of haplotypes.
Tzeng, Jung-Ying.

Identification of mutations affecting liability to complex disease by the analysis of haplotypes. - 125 p.

Source: Dissertation Abstracts International, Volume: 64-06, Section: B, page: 2741.

Thesis (Ph.D.)--Carnegie Mellon University, 2003.

Disease mutations refer to those gene mutations affecting the risk of developing a disease. For complex disease, however, these mutations are neither sufficient nor necessary to induce the disorder. One approach toward identifying the mutations that increase the liability of contracting a disease involves population-based association analysis, i.e. case-control studies. To detect the association between the genetic variants and the disease, we use the fact that disease mutations can be identified by the excess of haplotype sharing among cases. In this thesis we introduce a new class of one degree-of-freedom statistics that are quadratic functions of the haplotype frequencies to measure haplotype similarity. We call such measures the quadratic statistics of haplotype similarity (QSHS). We then present a testing procedure based on the class of QSHS for initial genome screening. Issues involved in constructing this approach are (1) population substructure and correlated samples, (2) multiple testing, and (3) missing haplotype information (i.e., unphased genotypic data). We tackle the problem of substructure and relatedness within study population by generalizing the Genomic Control principle (Devlin and Roeder 1999) to our class of statistics. We use the False Discovery Rate procedure (Benjamini and Hochberg 1995) to adjust for multiple hypothesis testing. We show that this procedure extends naturally to genotypic data and can be robust to missing haplotype information. Besides our QSHS, the Pearson's chi-square statistic is the commonly used alternative to test for association. A power comparison between these two testing methods are performed via simulation. Our results show that these methods are powerful under different background of mutations, depending on the mutations occurred on rare or common variants. The previous results rely on an assumption that the population haplotype frequencies are bounded away from the degenerate points in the parameter space. We extend the results to non-degenerate and degenerate cases by considering the non-standard limiting distribution of the QSHS.Subjects--Topical Terms:

517247
Statistics.

Identification of mutations affecting liability to complex disease by the analysis of haplotypes.
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100 1 $a Tzeng, Jung-Ying. $3 1946632
245 1 0 $a Identification of mutations affecting liability to complex disease by the analysis of haplotypes.
300 $a 125 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-06, Section: B, page: 2741.
500 $a Adviser: Kathryn Roeder.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2003.
520 $a Disease mutations refer to those gene mutations affecting the risk of developing a disease. For complex disease, however, these mutations are neither sufficient nor necessary to induce the disorder. One approach toward identifying the mutations that increase the liability of contracting a disease involves population-based association analysis, i.e. case-control studies. To detect the association between the genetic variants and the disease, we use the fact that disease mutations can be identified by the excess of haplotype sharing among cases. In this thesis we introduce a new class of one degree-of-freedom statistics that are quadratic functions of the haplotype frequencies to measure haplotype similarity. We call such measures the quadratic statistics of haplotype similarity (QSHS). We then present a testing procedure based on the class of QSHS for initial genome screening. Issues involved in constructing this approach are (1) population substructure and correlated samples, (2) multiple testing, and (3) missing haplotype information (i.e., unphased genotypic data). We tackle the problem of substructure and relatedness within study population by generalizing the Genomic Control principle (Devlin and Roeder 1999) to our class of statistics. We use the False Discovery Rate procedure (Benjamini and Hochberg 1995) to adjust for multiple hypothesis testing. We show that this procedure extends naturally to genotypic data and can be robust to missing haplotype information. Besides our QSHS, the Pearson's chi-square statistic is the commonly used alternative to test for association. A power comparison between these two testing methods are performed via simulation. Our results show that these methods are powerful under different background of mutations, depending on the mutations occurred on rare or common variants. The previous results rely on an assumption that the population haplotype frequencies are bounded away from the degenerate points in the parameter space. We extend the results to non-degenerate and degenerate cases by considering the non-standard limiting distribution of the QSHS.
590 $a School code: 0041.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Biostatistics. $3 1018416
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773 0 $t Dissertation Abstracts International $g 64-06B.
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790 $a 0041
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3095067