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Statistics and Probability

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Value Of Mendelian Laws Of Segregation In Families: Data Quality Control, Imputation, And Beyond, Elizabeth M. Blue, Lei Sun, Nathan L. Tintle, Ellen M. Wijsman Jan 2014

Value Of Mendelian Laws Of Segregation In Families: Data Quality Control, Imputation, And Beyond, Elizabeth M. Blue, Lei Sun, Nathan L. Tintle, Ellen M. Wijsman

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When analyzing family data, we dream of perfectly informative data, even whole-genome sequences (WGSs) for all family members. Reality intervenes, and we find that next-generation sequencing (NGS) data have errors and are often too expensive or impossible to collect on everyone. The Genetic Analysis Workshop 18 working groups on quality control and dropping WGSs through families using a genome-wide association framework focused on finding, correcting, and using errors within the available sequence and family data, developing methods to infer and analyze missing sequence data among relatives, and testing for linkage and association with simulated blood pressure. We found that single-nucleotide …


Assessing The Impact Of Non-Differential Genotyping Errors On Rare Variant Tests Of Association, Scott Powers, Shyam Gopalakrishnan, Nathan L. Tintle Nov 2011

Assessing The Impact Of Non-Differential Genotyping Errors On Rare Variant Tests Of Association, Scott Powers, Shyam Gopalakrishnan, Nathan L. Tintle

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Background/Aims: We aim to quantify the effect of non-differential genotyping errors on the power of rare variant tests and identify those situations when genotyping errors are most harmful. Methods: We simulated genotype and phenotype data for a range of sample sizes, minor allele frequencies, disease relative risks and numbers of rare variants. Genotype errors were then simulated using five different error models covering a wide range of error rates. Results: Even at very low error rates, misclassifying a common homozygote as a heterozygote translates into a substantial loss of power, a result that is exacerbated even further as the minor …