666.10 - Haplotype Based Association Tests Biostatistics...

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Haplotype Based Association Tests Biostatistics 666 Lecture 10
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Last Lecture z Statistical Haplotyping Methods Clark’s greedy algorithm The E-M algorithm Stephens’ et al. “coalescent-based” algorithm
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Hypothesis Testing z Often, haplotype frequencies are not final outcome. z For example, we may wish to compare two groups of individuals… Are haplotypes similar in two populations? Are haplotypes similar in patients and healthy controls?
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Today … z Association tests for haplotype data z When do you think these will out-perform single marker tests? z When do you think these will be out- performed by single marker tests?
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Introduction: A Single Marker Association Test z A simple genetic association z Compare frequencies of particular alleles, or genotypes, in set of cases and controls z Typically, relies on standard contingency table tests… Chi-squared Goodness-of-Fit Test Likelihood Ratio Test Fisher’s Exact Test
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Construct Contingency Table z Rows One row for cases, another for controls z Columns One for each genotype One for each allele z Individual cells Count of observations, with double counting for allele tests
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Simple Association Study Genotype n a,22 n u,12 n u,11 Unaffecteds n a,22 n a,12 n a,11 Affecteds 2/2 1/2 1/1 Organize genotype counts in a simple table…
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Notation z Let index i iterate over rows E.g. i = 1 for affecteds, i = 2 for unaffecteds z Let index j iterate over columns E.g. j = 1 for genotype 1/1, j = 2 for genotype 2/2, etc. z Let O ij denote the observed counts in each cell Let O •• denote the grand total Let O i• and O •j denote the row and column totals z Let E ij denote the expected counts in each cell E ij = O i• O •j / O ••
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This note was uploaded on 12/26/2011 for the course BIO 666 taught by Professor Staff during the Fall '06 term at University of Michigan.

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666.10 - Haplotype Based Association Tests Biostatistics...

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