Evaluating the impact of genotype errors on rare variant tests of association
The new class of rare variant tests has usually been evaluated assuming perfect genotype information. In reality, rare variant genotypes may be incorrect, and so rare variant tests should be robust to imperfect data. Errors and uncertainty in SNP genotyping are already known to dramatically impact s...
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| Format: | Article |
| Language: | en_US |
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Frontiers Research Foundation
2014
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| Online Access: | http://hdl.handle.net/1721.1/88050 |
| _version_ | 1811089029991497728 |
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| author | Cook, Kaitlyn Benitez, Alejandra Fu, Casey L. Tintle, Nathan |
| author2 | Massachusetts Institute of Technology. Department of Mathematics |
| author_facet | Massachusetts Institute of Technology. Department of Mathematics Cook, Kaitlyn Benitez, Alejandra Fu, Casey L. Tintle, Nathan |
| author_sort | Cook, Kaitlyn |
| collection | MIT |
| description | The new class of rare variant tests has usually been evaluated assuming perfect genotype information. In reality, rare variant genotypes may be incorrect, and so rare variant tests should be robust to imperfect data. Errors and uncertainty in SNP genotyping are already known to dramatically impact statistical power for single marker tests on common variants and, in some cases, inflate the type I error rate. Recent results show that uncertainty in genotype calls derived from sequencing reads are dependent on several factors, including read depth, calling algorithm, number of alleles present in the sample, and the frequency at which an allele segregates in the population. We have recently proposed a general framework for the evaluation and investigation of rare variant tests of association, classifying most rare variant tests into one of two broad categories (length or joint tests). We use this framework to relate factors affecting genotype uncertainty to the power and type I error rate of rare variant tests. We find that non-differential genotype errors (an error process that occurs independent of phenotype) decrease power, with larger decreases for extremely rare variants, and for the common homozygote to heterozygote error. Differential genotype errors (an error process that is associated with phenotype status), lead to inflated type I error rates which are more likely to occur at sites with more common homozygote to heterozygote errors than vice versa. Finally, our work suggests that certain rare variant tests and study designs may be more robust to the inclusion of genotype errors. Further work is needed to directly integrate genotype calling algorithm decisions, study costs and test statistic choices to provide comprehensive design and analysis advice which appropriately accounts for the impact of genotype errors. |
| first_indexed | 2024-09-23T14:12:43Z |
| format | Article |
| id | mit-1721.1/88050 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:12:43Z |
| publishDate | 2014 |
| publisher | Frontiers Research Foundation |
| record_format | dspace |
| spelling | mit-1721.1/880502022-10-01T19:45:05Z Evaluating the impact of genotype errors on rare variant tests of association Cook, Kaitlyn Benitez, Alejandra Fu, Casey L. Tintle, Nathan Massachusetts Institute of Technology. Department of Mathematics Fu, Casey L. The new class of rare variant tests has usually been evaluated assuming perfect genotype information. In reality, rare variant genotypes may be incorrect, and so rare variant tests should be robust to imperfect data. Errors and uncertainty in SNP genotyping are already known to dramatically impact statistical power for single marker tests on common variants and, in some cases, inflate the type I error rate. Recent results show that uncertainty in genotype calls derived from sequencing reads are dependent on several factors, including read depth, calling algorithm, number of alleles present in the sample, and the frequency at which an allele segregates in the population. We have recently proposed a general framework for the evaluation and investigation of rare variant tests of association, classifying most rare variant tests into one of two broad categories (length or joint tests). We use this framework to relate factors affecting genotype uncertainty to the power and type I error rate of rare variant tests. We find that non-differential genotype errors (an error process that occurs independent of phenotype) decrease power, with larger decreases for extremely rare variants, and for the common homozygote to heterozygote error. Differential genotype errors (an error process that is associated with phenotype status), lead to inflated type I error rates which are more likely to occur at sites with more common homozygote to heterozygote errors than vice versa. Finally, our work suggests that certain rare variant tests and study designs may be more robust to the inclusion of genotype errors. Further work is needed to directly integrate genotype calling algorithm decisions, study costs and test statistic choices to provide comprehensive design and analysis advice which appropriately accounts for the impact of genotype errors. National Human Genome Research Institute (U.S.) (R15HG006915) 2014-06-20T15:45:00Z 2014-06-20T15:45:00Z 2014-04 2013-10 Article http://purl.org/eprint/type/JournalArticle 1664-8021 http://hdl.handle.net/1721.1/88050 Cook, Kaitlyn, Alejandra Benitez, Casey Fu, and Nathan Tintle. “Evaluating the Impact of Genotype Errors on Rare Variant Tests of Association.” Frontiers in Genetics 5 (April 1, 2014). en_US http://dx.doi.org/10.3389/fgene.2014.00062 Frontiers in Genetics Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Frontiers Research Foundation Frontiers Research Foundation |
| spellingShingle | Cook, Kaitlyn Benitez, Alejandra Fu, Casey L. Tintle, Nathan Evaluating the impact of genotype errors on rare variant tests of association |
| title | Evaluating the impact of genotype errors on rare variant tests of association |
| title_full | Evaluating the impact of genotype errors on rare variant tests of association |
| title_fullStr | Evaluating the impact of genotype errors on rare variant tests of association |
| title_full_unstemmed | Evaluating the impact of genotype errors on rare variant tests of association |
| title_short | Evaluating the impact of genotype errors on rare variant tests of association |
| title_sort | evaluating the impact of genotype errors on rare variant tests of association |
| url | http://hdl.handle.net/1721.1/88050 |
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