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Activity Number:
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535
- Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #327188
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Title:
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Harnessing Relatedness for Genotyping Autopolyploids
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Author(s):
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David Gerard* and Matthew Stephens and Luis Felipe Ventorim Ferrão
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Companies:
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University of Chicago and University of Chicago and University of Florida
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Keywords:
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empirical Bayes;
sequencing;
copula;
DNA;
ploidy;
allele dosage
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Abstract:
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In order to bring the advantages of genome-wide studies to genetic and evolutionary research, many bioinformatics pipelines require the detection and quantification (or "genotyping") of differences in individual genomes. Many scientists now use reduced representation next-generation sequencing approaches for genotyping. The resulting data are inherently noisy and call for sophisticated methods to accurately genotype individuals. Much recent work tries to harness different hierarchies between the individuals to improve genotyping methods in these assays, particularly in polyploid species (those with more than two copies of their genomes), however no method generically harnesses the correlation induced between genotypes caused by varying levels of relatedness between the individuals. We exploit this correlation induced by relatedness to improve genotyping using a copula-based approach. These correlations are genome-wide parameters and thus may be accurately estimated. Our method also accounts for key-features of next-generation sequencing data that many researchers ignore: allele bias and overdispersion. We verify our method on real and simulated data.
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Authors who are presenting talks have a * after their name.
