Please use this identifier to cite or link to this item: http://prr.hec.gov.pk/jspui/handle/123456789/12245
Title: Statistical Tests for the Analysis of Human Genetic Linkage
Authors: Khan, Sajjad Ahmad
Keywords: Statistics
Social Sciences
Issue Date: 2010
Publisher: University of Peshawar, Peshawar.
Abstract: There are many tests of inheritance based upon sibling information for diseases that have late onset. The N-test (Green et al. 1983) is one of these tests, which utilizes information from affected siblings. The N-test is the count in affected siblings of the most frequently occurring haplotype from the father plus the analogous count from the mother. When applied to haplotypes, the N-test excludes recombinant families from the analysis and assumes that parental genotypes are heterozygous. But in real world data sets, generally in Asia and particularly in Pakistan, the case of heterogeneity of parents genotyping were not exist and therefore recombinant families are found more frequently. In this study we modified the N-test to be based on alleles instead of haplotypes. This modified allele-based N-test can include all families (recombinant as well as nonrecombinant). We carried out a simulation study to compare the power of the allele-based N-test with the powers of the S a l l and S p a i r s nonparametric statistics as computed by MERLIN. The powers of the allelebased N-test, S a l l and S p a i r s statistics are identical to each other for 
 iii
 affected sibships of size 2 and 3. For affected sibships of larger sizes, the powers of the S a l l and S p a i r s statistics are larger than the power of allelebased N-test. These simulation-based results are consistent with earlier results based on analytical computations. Gene-mapping studies regularly rely on examination for Mendelian transmission of marker alleles in a pedigree, as a way of screening for genotyping errors and mutations. For analysis of family data sets, it is usually necessary to resolve or remove the genotyping errors prior to analysis. At the Center of Inherited Disease Research (CIDR), to deal with their large-scale data flow, they formalized their data cleaning approach in a set of rules based on PedCheck output. We examine via carefully designed simulations that how well CIDR’s data cleaning rules work in practice. We found that genotype errors in siblings are detected more often than in parents for less polymorphic SNPs and vice versa for more polymorphic SNPs. Through computer simulation, we conclude that some of the CIDR’s rules work poorly in some situations and we suggest a set of modified data cleaning rules that may work better than CIDR’s rules.
Gov't Doc #: 19796
URI: http://prr.hec.gov.pk/jspui/handle/123456789/12245
Appears in Collections:PhD Thesis of All Public / Private Sector Universities / DAIs.

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