Novel EDGE encoding method enhances ability to identify genetic interactions.
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 analy...
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oai:doaj.org-article:b37fdfc15813491fa1c98bc3372f6d222021-12-02T20:02:58ZNovel EDGE encoding method enhances ability to identify genetic interactions.1553-73901553-740410.1371/journal.pgen.1009534https://doaj.org/article/b37fdfc15813491fa1c98bc3372f6d222021-06-01T00:00:00Zhttps://doi.org/10.1371/journal.pgen.1009534https://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404Assumptions 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.Molly A HallJohn WallaceAnastasia M LucasYuki BradfordShefali S VermaBertram Müller-MyhsokKristin PasseroJiayan ZhouJohn McGuiganBeibei JiangSarah A PendergrassYanfei ZhangPeggy PeissigMurray BrilliantPatrick SleimanHakon HakonarsonJohn B HarleyKrzysztof KirylukKristel Van SteenJason H MooreMarylyn D RitchiePublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 17, Iss 6, p e1009534 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Genetics QH426-470 |
| spellingShingle |
Genetics QH426-470 Molly A Hall John Wallace Anastasia M Lucas Yuki Bradford Shefali S Verma Bertram Müller-Myhsok Kristin Passero Jiayan Zhou John McGuigan Beibei Jiang Sarah A Pendergrass Yanfei Zhang Peggy Peissig Murray Brilliant Patrick Sleiman Hakon Hakonarson John B Harley Krzysztof Kiryluk Kristel Van Steen Jason H Moore Marylyn D Ritchie Novel EDGE encoding method enhances ability to identify genetic interactions. |
| description |
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. |
| format |
article |
| author |
Molly A Hall John Wallace Anastasia M Lucas Yuki Bradford Shefali S Verma Bertram Müller-Myhsok Kristin Passero Jiayan Zhou John McGuigan Beibei Jiang Sarah A Pendergrass Yanfei Zhang Peggy Peissig Murray Brilliant Patrick Sleiman Hakon Hakonarson John B Harley Krzysztof Kiryluk Kristel Van Steen Jason H Moore Marylyn D Ritchie |
| author_facet |
Molly A Hall John Wallace Anastasia M Lucas Yuki Bradford Shefali S Verma Bertram Müller-Myhsok Kristin Passero Jiayan Zhou John McGuigan Beibei Jiang Sarah A Pendergrass Yanfei Zhang Peggy Peissig Murray Brilliant Patrick Sleiman Hakon Hakonarson John B Harley Krzysztof Kiryluk Kristel Van Steen Jason H Moore Marylyn D Ritchie |
| author_sort |
Molly A Hall |
| title |
Novel EDGE encoding method enhances ability to identify genetic interactions. |
| title_short |
Novel EDGE encoding method enhances ability to identify genetic interactions. |
| title_full |
Novel EDGE encoding method enhances ability to identify genetic interactions. |
| title_fullStr |
Novel EDGE encoding method enhances ability to identify genetic interactions. |
| title_full_unstemmed |
Novel EDGE encoding method enhances ability to identify genetic interactions. |
| title_sort |
novel edge encoding method enhances ability to identify genetic interactions. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/b37fdfc15813491fa1c98bc3372f6d22 |
| work_keys_str_mv |
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