NGS allele counts versus called genotypes for testing genetic association
RNA sequence data are commonly summarized as read counts. By contrast, so far there is no alternative to genotype calling for investigating the relationship between genetic variants determined by next-generation sequencing (NGS) and a phenotype of interest. Here we propose and evaluate the direct an...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022002951 |
| _version_ | 1797978234337361920 |
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| author | Rosa González Silos Christine Fischer Justo Lorenzo Bermejo |
| author_facet | Rosa González Silos Christine Fischer Justo Lorenzo Bermejo |
| author_sort | Rosa González Silos |
| collection | DOAJ |
| description | RNA sequence data are commonly summarized as read counts. By contrast, so far there is no alternative to genotype calling for investigating the relationship between genetic variants determined by next-generation sequencing (NGS) and a phenotype of interest. Here we propose and evaluate the direct analysis of allele counts for genetic association tests. Specifically, we assess the potential advantage of the ratio of alternative allele counts to the total number of reads aligned at a specific position of the genome (coverage) over called genotypes. We simulated association studies based on NGS data from HapMap individuals. Genotype quality scores and allele counts were simulated using NGS data from the Personal Genome Project. Real data from the 1000 Genomes Project was also used to compare the two competing approaches. The average proportions of probability values lower or equal to 0.05 amounted to 0.0496 for called genotypes and 0.0485 for the ratio of alternative allele counts to coverage in the null scenario, and to 0.69 for called genotypes and 0.75 for the ratio of alternative allele counts to coverage in the alternative scenario (9% power increase). The advantage in statistical power of the novel approach increased with decreasing coverage, with decreasing genotype quality and with decreasing allele frequency – 124% power increase for variants with a minor allele frequency lower than 0.05. We provide computer code in R to implement the novel approach, which does not preclude the use of complementary data quality filters before or after identification of the most promising association signals. Author summary: Genetic association tests usually rely on called genotypes. We postulate here that the direct analysis of allele counts from sequence data improves the quality of statistical inference. To evaluate this hypothesis, we investigate simulated and real data using distinct statistical approaches. We demonstrate that association tests based on allele counts rather than called genotypes achieve higher statistical power with controlled type I error rates. |
| first_indexed | 2024-04-11T05:19:44Z |
| format | Article |
| id | doaj.art-a150a0dc7f72477ea63ddeccb9beef9f |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:19:44Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-a150a0dc7f72477ea63ddeccb9beef9f2022-12-24T04:53:25ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012037293733NGS allele counts versus called genotypes for testing genetic associationRosa González Silos0Christine Fischer1Justo Lorenzo Bermejo2Institute of Medical Biometry, University of Heidelberg, 69120, GermanyInstitute of Human Genetics, University of Heidelberg, 69120, GermanyInstitute of Medical Biometry, University of Heidelberg, 69120, Germany; Corresponding author.RNA sequence data are commonly summarized as read counts. By contrast, so far there is no alternative to genotype calling for investigating the relationship between genetic variants determined by next-generation sequencing (NGS) and a phenotype of interest. Here we propose and evaluate the direct analysis of allele counts for genetic association tests. Specifically, we assess the potential advantage of the ratio of alternative allele counts to the total number of reads aligned at a specific position of the genome (coverage) over called genotypes. We simulated association studies based on NGS data from HapMap individuals. Genotype quality scores and allele counts were simulated using NGS data from the Personal Genome Project. Real data from the 1000 Genomes Project was also used to compare the two competing approaches. The average proportions of probability values lower or equal to 0.05 amounted to 0.0496 for called genotypes and 0.0485 for the ratio of alternative allele counts to coverage in the null scenario, and to 0.69 for called genotypes and 0.75 for the ratio of alternative allele counts to coverage in the alternative scenario (9% power increase). The advantage in statistical power of the novel approach increased with decreasing coverage, with decreasing genotype quality and with decreasing allele frequency – 124% power increase for variants with a minor allele frequency lower than 0.05. We provide computer code in R to implement the novel approach, which does not preclude the use of complementary data quality filters before or after identification of the most promising association signals. Author summary: Genetic association tests usually rely on called genotypes. We postulate here that the direct analysis of allele counts from sequence data improves the quality of statistical inference. To evaluate this hypothesis, we investigate simulated and real data using distinct statistical approaches. We demonstrate that association tests based on allele counts rather than called genotypes achieve higher statistical power with controlled type I error rates.http://www.sciencedirect.com/science/article/pii/S2001037022002951Genotype callingNext generation sequencingAllele countsGenetic association testsStatistical power |
| spellingShingle | Rosa González Silos Christine Fischer Justo Lorenzo Bermejo NGS allele counts versus called genotypes for testing genetic association Computational and Structural Biotechnology Journal Genotype calling Next generation sequencing Allele counts Genetic association tests Statistical power |
| title | NGS allele counts versus called genotypes for testing genetic association |
| title_full | NGS allele counts versus called genotypes for testing genetic association |
| title_fullStr | NGS allele counts versus called genotypes for testing genetic association |
| title_full_unstemmed | NGS allele counts versus called genotypes for testing genetic association |
| title_short | NGS allele counts versus called genotypes for testing genetic association |
| title_sort | ngs allele counts versus called genotypes for testing genetic association |
| topic | Genotype calling Next generation sequencing Allele counts Genetic association tests Statistical power |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022002951 |
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