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Statistical model integrating interactions into genotype-phenotype association mapping: an application to reveal 3D-genetic basis underlying Autism
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.07.27.222364v1?rss=1
Authors: Li, Q., Cao, C., Perera, D., He, J., Chen, X., Azeem, F., Howe, A., Au, B., Yan, J., Long, Q.
Abstract:
Biological interactions are prevalent in the functioning organisms. Correspondingly, statistical geneticists developed various models to identify genetic interactions through genotype-phenotype association mapping. The current standard protocols in practice test single variants or single regions (that contain multiple local variants) sequentially along the genome, followed by functional annotations that involve various aspects including interactions. The testing of genetic interactions upfront is rare in practice due to the burden of testing a huge number of combinations, which lead to the multiple-test problem and the risk of overfitting. In this work, we developed interaction-integrated linear mixed model (ILMM), a novel model that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems associated with combination searching.
Copy rights belong to original authors. Visit the link for more info
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By Multimodal LLCLink to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.07.27.222364v1?rss=1
Authors: Li, Q., Cao, C., Perera, D., He, J., Chen, X., Azeem, F., Howe, A., Au, B., Yan, J., Long, Q.
Abstract:
Biological interactions are prevalent in the functioning organisms. Correspondingly, statistical geneticists developed various models to identify genetic interactions through genotype-phenotype association mapping. The current standard protocols in practice test single variants or single regions (that contain multiple local variants) sequentially along the genome, followed by functional annotations that involve various aspects including interactions. The testing of genetic interactions upfront is rare in practice due to the burden of testing a huge number of combinations, which lead to the multiple-test problem and the risk of overfitting. In this work, we developed interaction-integrated linear mixed model (ILMM), a novel model that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems associated with combination searching.
Copy rights belong to original authors. Visit the link for more info