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  Novel EDGE encoding method enhances ability to identify genetic interactions

Hall, M. A., Wallace, J., Lucas, A. M., Bradford, Y., Verma, S. S., Mueller-Myhsok, B., et al. (2021). Novel EDGE encoding method enhances ability to identify genetic interactions. PLOS GENETICS, 17(6): e1009534. doi:10.1371/journal.pgen.1009534.

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Hall, Molly A., Author
Wallace, John, Author
Lucas, Anastasia M., Author
Bradford, Yuki, Author
Verma, Shefali S., Author
Mueller-Myhsok, Bertram1, Author           
Passero, Kristin, Author
Zhou, Jiayan, Author
McGuigan, John, Author
Jiang, Beibei1, Author           
Pendergrass, Sarah A., Author
Zhang, Yanfei, Author
Peissig, Peggy, Author
Brilliant, Murray, Author
Sleiman, Patrick, Author
Hakonarson, Hakon, Author
Harley, John B., Author
Kiryluk, Krzysztof, Author
Van Steen, Kristel, Author
Moore, Jason H., Author
Ritchie, Marylyn D., Author more..
1RG Statistical Genetics, Max Planck Institute of Psychiatry, Max Planck Society, ou_2040288              


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 Abstract: Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)-rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.
Author summary Although traditional genetic encodings are widely implemented in genetics research, including in genome-wide association studies (GWAS) and epistasis, each method makes assumptions that may not reflect the underlying etiology. Here, we introduce a novel encoding method that estimates and assigns an individualized data-driven encoding for each single nucleotide polymorphism (SNP): the elastic data-driven genetic encoding (EDGE). With simulations, we demonstrate that this novel method is more accurate and robust than traditional encoding methods in estimating heterozygous genotype values, reducing the type I error, and detecting SNP-SNP interactions. We further applied the traditional encodings and EDGE to biomedical data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes, and EDGE identified a novel interaction for age-related cataract not detected by traditional methods, which replicated in data from the UK Biobank. EDGE provides an alternative approach to understanding and modeling diverse SNP models and is recommended for studying complex genetics in common human phenotypes.


 Dates: 2021
 Publication Status: Published online
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Source Genre: Journal
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Pages: - Volume / Issue: 17 (6) Sequence Number: e1009534 Start / End Page: - Identifier: ISSN: 1553-7404