Association detection between multiple traits and rare variants based on family data via a nonparametric method
Background The rapid development of next-generation sequencing technologies allow people to analyze human complex diseases at the molecular level. It has been shown that rare variants play important roles for human diseases besides common variants. Thus, effective statistical methods need to be prop...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-09-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/16040.pdf |
| _version_ | 1797423794814451712 |
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| author | Jinling Chi Meijuan Xu Xiaona Sheng Ying Zhou |
| author_facet | Jinling Chi Meijuan Xu Xiaona Sheng Ying Zhou |
| author_sort | Jinling Chi |
| collection | DOAJ |
| description | Background The rapid development of next-generation sequencing technologies allow people to analyze human complex diseases at the molecular level. It has been shown that rare variants play important roles for human diseases besides common variants. Thus, effective statistical methods need to be proposed to test for the associations between traits (e.g., diseases) and rare variants. Currently, more and more rare genetic variants are being detected throughout the human genome, which demonstrates the possibility to study rare variants. Yet complex diseases are usually measured as a variety of forms, such as binary, ordinal, quantitative, or some mixture of them. Therefore, the genetic mapping problem can be attributable to the association detection between multiple traits and multiple loci, with sufficiently considering the correlated structure among multiple traits. Methods In this article, we construct a new non-parametric statistic by the generalized Kendall’s τ theory based on family data. The new test statistic has an asymptotic distribution, it can be used to study the associations between multiple traits and rare variants, which broadens the way to identify genetic factors of human complex diseases. Results We apply our method (called Nonp-FAM) to analyze simulated data and GAW17 data, and conduct comprehensive comparison with some existing methods. Experimental results show that the proposed family-based method is powerful and robust for testing associations between multiple traits and rare variants, even if the data has some population stratification effect. |
| first_indexed | 2024-03-09T07:52:02Z |
| format | Article |
| id | doaj.art-b799b5f6c4754b99a7c3341689f756aa |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:52:02Z |
| publishDate | 2023-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-b799b5f6c4754b99a7c3341689f756aa2023-12-03T01:32:52ZengPeerJ Inc.PeerJ2167-83592023-09-0111e1604010.7717/peerj.16040Association detection between multiple traits and rare variants based on family data via a nonparametric methodJinling Chi0Meijuan Xu1Xiaona Sheng2Ying Zhou3Department of Statistics, Heilongjiang University, Harbin, ChinaDepartment of Statistics, Heilongjiang University, Harbin, ChinaSchool of Information Engineering, Harbin University, Harbin, ChinaDepartment of Statistics, Heilongjiang University, Harbin, ChinaBackground The rapid development of next-generation sequencing technologies allow people to analyze human complex diseases at the molecular level. It has been shown that rare variants play important roles for human diseases besides common variants. Thus, effective statistical methods need to be proposed to test for the associations between traits (e.g., diseases) and rare variants. Currently, more and more rare genetic variants are being detected throughout the human genome, which demonstrates the possibility to study rare variants. Yet complex diseases are usually measured as a variety of forms, such as binary, ordinal, quantitative, or some mixture of them. Therefore, the genetic mapping problem can be attributable to the association detection between multiple traits and multiple loci, with sufficiently considering the correlated structure among multiple traits. Methods In this article, we construct a new non-parametric statistic by the generalized Kendall’s τ theory based on family data. The new test statistic has an asymptotic distribution, it can be used to study the associations between multiple traits and rare variants, which broadens the way to identify genetic factors of human complex diseases. Results We apply our method (called Nonp-FAM) to analyze simulated data and GAW17 data, and conduct comprehensive comparison with some existing methods. Experimental results show that the proposed family-based method is powerful and robust for testing associations between multiple traits and rare variants, even if the data has some population stratification effect.https://peerj.com/articles/16040.pdfFamily-based designMultiple traitsRare variantsThe generalized Kendall’s τ |
| spellingShingle | Jinling Chi Meijuan Xu Xiaona Sheng Ying Zhou Association detection between multiple traits and rare variants based on family data via a nonparametric method PeerJ Family-based design Multiple traits Rare variants The generalized Kendall’s τ |
| title | Association detection between multiple traits and rare variants based on family data via a nonparametric method |
| title_full | Association detection between multiple traits and rare variants based on family data via a nonparametric method |
| title_fullStr | Association detection between multiple traits and rare variants based on family data via a nonparametric method |
| title_full_unstemmed | Association detection between multiple traits and rare variants based on family data via a nonparametric method |
| title_short | Association detection between multiple traits and rare variants based on family data via a nonparametric method |
| title_sort | association detection between multiple traits and rare variants based on family data via a nonparametric method |
| topic | Family-based design Multiple traits Rare variants The generalized Kendall’s τ |
| url | https://peerj.com/articles/16040.pdf |
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