Testing an optimally weighted combination of common and/or rare variants with multiple traits.
Recently, joint analysis of multiple traits has become popular because it can increase statistical power to identify genetic variants associated with complex diseases. In addition, there is increasing evidence indicating that pleiotropy is a widespread phenomenon in complex diseases. Currently, most...
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Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0201186&type=printable |
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| author | Zhenchuan Wang Qiuying Sha Shurong Fang Kui Zhang Shuanglin Zhang |
| author_facet | Zhenchuan Wang Qiuying Sha Shurong Fang Kui Zhang Shuanglin Zhang |
| author_sort | Zhenchuan Wang |
| collection | DOAJ |
| description | Recently, joint analysis of multiple traits has become popular because it can increase statistical power to identify genetic variants associated with complex diseases. In addition, there is increasing evidence indicating that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single genetic variant. However, these methods by analyzing one variant at a time may not be ideal for rare variant association studies because of the allelic heterogeneity as well as the extreme rarity of rare variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study. |
| format | Article |
| id | doaj-art-c701fde085f445f0940e0c29c9f8f368 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-c701fde085f445f0940e0c29c9f8f3682025-08-20T02:45:50ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01137e020118610.1371/journal.pone.0201186Testing an optimally weighted combination of common and/or rare variants with multiple traits.Zhenchuan WangQiuying ShaShurong FangKui ZhangShuanglin ZhangRecently, joint analysis of multiple traits has become popular because it can increase statistical power to identify genetic variants associated with complex diseases. In addition, there is increasing evidence indicating that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single genetic variant. However, these methods by analyzing one variant at a time may not be ideal for rare variant association studies because of the allelic heterogeneity as well as the extreme rarity of rare variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0201186&type=printable |
| spellingShingle | Zhenchuan Wang Qiuying Sha Shurong Fang Kui Zhang Shuanglin Zhang Testing an optimally weighted combination of common and/or rare variants with multiple traits. PLoS ONE |
| title | Testing an optimally weighted combination of common and/or rare variants with multiple traits. |
| title_full | Testing an optimally weighted combination of common and/or rare variants with multiple traits. |
| title_fullStr | Testing an optimally weighted combination of common and/or rare variants with multiple traits. |
| title_full_unstemmed | Testing an optimally weighted combination of common and/or rare variants with multiple traits. |
| title_short | Testing an optimally weighted combination of common and/or rare variants with multiple traits. |
| title_sort | testing an optimally weighted combination of common and or rare variants with multiple traits |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0201186&type=printable |
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