A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables.
The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmenta...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-11-01
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| Series: | PLoS Genetics |
| Online Access: | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1010464&type=printable |
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| author | Julian Hecker Dmitry Prokopenko Matthew Moll Sanghun Lee Wonji Kim Dandi Qiao Kirsten Voorhies Woori Kim Stijn Vansteelandt Brian D Hobbs Michael H Cho Edwin K Silverman Sharon M Lutz Dawn L DeMeo Scott T Weiss Christoph Lange |
| author_facet | Julian Hecker Dmitry Prokopenko Matthew Moll Sanghun Lee Wonji Kim Dandi Qiao Kirsten Voorhies Woori Kim Stijn Vansteelandt Brian D Hobbs Michael H Cho Edwin K Silverman Sharon M Lutz Dawn L DeMeo Scott T Weiss Christoph Lange |
| author_sort | Julian Hecker |
| collection | DOAJ |
| description | The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms. |
| format | Article |
| id | doaj-art-193a8e8ec1d744fcb6ff01cb7ab115e0 |
| institution | Kabale University |
| issn | 1553-7390 1553-7404 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj-art-193a8e8ec1d744fcb6ff01cb7ab115e02025-08-20T03:44:45ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042022-11-011811e101046410.1371/journal.pgen.1010464A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables.Julian HeckerDmitry ProkopenkoMatthew MollSanghun LeeWonji KimDandi QiaoKirsten VoorhiesWoori KimStijn VansteelandtBrian D HobbsMichael H ChoEdwin K SilvermanSharon M LutzDawn L DeMeoScott T WeissChristoph LangeThe identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1010464&type=printable |
| spellingShingle | Julian Hecker Dmitry Prokopenko Matthew Moll Sanghun Lee Wonji Kim Dandi Qiao Kirsten Voorhies Woori Kim Stijn Vansteelandt Brian D Hobbs Michael H Cho Edwin K Silverman Sharon M Lutz Dawn L DeMeo Scott T Weiss Christoph Lange A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables. PLoS Genetics |
| title | A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables. |
| title_full | A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables. |
| title_fullStr | A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables. |
| title_full_unstemmed | A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables. |
| title_short | A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables. |
| title_sort | robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables |
| url | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1010464&type=printable |
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