2 edition of Family-based nonparametric tests of linkage and association. found in the catalog.
Family-based nonparametric tests of linkage and association.
Juan Pablo Lewinger
Written in English
We propose a general framework for constructing nonparametric tests of linkage sensitive to allelic association as well as tests of allelic association in the presence of linkage. These tests make efficient use of all information available in nuclear families, including family structure, unaffected offspring, parental phenotypes, families with both parents homozygous and families with missing parental genotypes. The non-parametric property of these tests is obtained by conditioning on sufficient statistics for the hypotheses of no linkage or no allelic association, according to the framework developed by Rabinowitz et al. . The test statistics are conditional likelihood ratios based on a parametric model of marker and trait data that includes allelic association, and where model parameters are estimated from the sufficient statistic under the null hypothesis in what is essentially a segregation analysis.Family-based tests of linkage that are sensitive to the presence of allelic association between a marker and disease loci have become a popular alternative to case-control based tests of allelic association. These tests can be more powerful than allele-sharing tests if the level of allelic association is high. Because they are not sensitive to allelic associations that do not occur in conjunction with linkage they are immune to the "population stratification problem". Many of these tests are also nonparametric tests of linkage thus providing protection against violation of assumptions commonly made in parametric linkage analysis such as random mating, Hardy-Weinberg equilibrium, monogenic disease or allelic homogeneity. The simplest and best known test of this class is the transmission disequilibrium test (TDT) introduced by Spielman et al. . Since its introduction in 1993 a large number of generalizations have been proposed to address some of the TDT"s original limitations. However most of these extensions discard valuable information.The performance of an implementation of these tests based on the standard two point linkage model is evaluated through Monte Carlo simulations, and applied in a study of hypertension. We also propose easy to implement Monte Carlo methods to compute power and p-values for a large class of family-based tests of linkage and association, including the ones we proposed.
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This unique textbook guides students and researchers of social sciences to successfully apply the knowledge of parametric and nonparametric statistics in the collection and analysis of data. This book comprehensively covers all the methods of parametric and nonparametric statistics such as correlation and regression, analysis of variance, test construction, one-sample test to k-sample tests. What would be a good book for non-parametric statistics. Not just the introduction but advanced level. There are some problems included at the end of each section; you could definitely use the book to learn nonparametric statistics. For a treatment that is much more introductory: Higgins, J. (). Kruskal-Wallis and that bundle of.
Genetic linkage and association analyses are the major tools to identify the genetic basis of diseases or traits. The primary difference between these two approaches is that linkage analysis looks at the relation between the transmission of a locus and the disease/trait within families, whereas association analysis focuses on the relation between a specific allele and the disease/trait within. Nonparametric statistics is the branch of statistics that is not based solely on parametrized families of probability distributions (common examples of parameters are the mean and variance). Nonparametric statistics is based on either being distribution-free or having a specified distribution but with the distribution's parameters unspecified. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency association tests correctly identified causal single-nucleotide polymorphisms for four genes (KDR, VEGFA, VEGFC, and FLT1).
Linkage Analysis vs Association Analysis Linkage studies are used when you have pedigrees of related individuals. Meanwhile the traits (phenotype) perform in some but not all of the family members. Thus, all the members including parents and children, no matter that wether they do or do not perform the phenotype, they have the same Size: KB. B. Weaver (Feb) Nonparametric Tests 1 Chapter 3: Nonparametric Tests Introduction Nonparametric, or distribution free tests are so-called because the assumptions underlying their use are “fewer and weaker than those associated with parametric tests” (Siegel & Castellan, , p. 34). Both linkage and association analyses were performed, with all the sibling pairs derived from the families analyzed using the sib-pair test of model-free linkage to assess linkage between SNPs and IS, with association analyses including a family-based association test (FBAT) and generalized estimating equations (GEE).Cited by: 3.
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SyntaxTextGen not activatedFamily based genome-wide association test was pdf conducted using the SNPs. Results Nonparametric linkage analysis revealed weak linkage evidence on chromosome 7, and association analysis identified SNP tsc on chromosome 3 as significantly associated with by: Am. J. Hum.
Genet. –, Family-Based Tests of Association in the Presence of Linkage Stephen L. Lake, 1Deborah Blacker,2,3 and Nan M. Laird Departments of 1Biostatistics and 2Epidemiology, Harvard School of Public Health, and 3Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston.Even when standard linkage tests fail to provide evidence, ebook, a disease locus linked to the marker may be suggested sometimes by the presence of a "disease association," usually established by a case-control study.
Sorin Istrail The Transmission Disequilibrium Test (TDT) and GWASFile Size: KB.