5 results on '"Jiayan Zhou"'
Search Results
2. Novel EDGE encoding method enhances ability to identify genetic interactions.
- 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, and Marylyn D Ritchie
- Subjects
Genetics ,QH426-470 - 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.
- Published
- 2021
- Full Text
- View/download PDF
3. Investigation of gene-gene interactions in cardiac traits and serum fatty acid levels in the LURIC Health Study.
- Author
-
Jiayan Zhou, Kristin Passero, Nicole E Palmiero, Bertram Müller-Myhsok, Marcus E Kleber, Winfried Maerz, and Molly A Hall
- Subjects
Medicine ,Science - Abstract
Epistasis analysis elucidates the effects of gene-gene interactions (G×G) between multiple loci for complex traits. However, the large computational demands and the high multiple testing burden impede their discoveries. Here, we illustrate the utilization of two methods, main effect filtering based on individual GWAS results and biological knowledge-based modeling through Biofilter software, to reduce the number of interactions tested among single nucleotide polymorphisms (SNPs) for 15 cardiac-related traits and 14 fatty acids. We performed interaction analyses using the two filtering methods, adjusting for age, sex, body mass index (BMI), waist-hip ratio, and the first three principal components from genetic data, among 2,824 samples from the Ludwigshafen Risk and Cardiovascular (LURIC) Health Study. Using Biofilter, one interaction nearly met Bonferroni significance: an interaction between rs7735781 in XRCC4 and rs10804247 in XRCC5 was identified for venous thrombosis with a Bonferroni-adjusted likelihood ratio test (LRT) p: 0.0627. A total of 57 interactions were identified from main effect filtering for the cardiac traits G×G (10) and fatty acids G×G (47) at Bonferroni-adjusted LRT p < 0.05. For cardiac traits, the top interaction involved SNPs rs1383819 in SNTG1 and rs1493939 (138kb from 5' of SAMD12) with Bonferroni-adjusted LRT p: 0.0228 which was significantly associated with history of arterial hypertension. For fatty acids, the top interaction between rs4839193 in KCND3 and rs10829717 in LOC107984002 with Bonferroni-adjusted LRT p: 2.28×10-5 was associated with 9-trans 12-trans octadecanoic acid, an omega-6 trans fatty acid. The model inflation factor for the interactions under different filtering methods was evaluated from the standard median and the linear regression approach. Here, we applied filtering approaches to identify numerous genetic interactions related to cardiac-related outcomes as potential targets for therapy. The approaches described offer ways to detect epistasis in the complex traits and to improve precision medicine capability.
- Published
- 2020
- Full Text
- View/download PDF
4. Novel EDGE encoding method enhances ability to identify genetic interactions
- Author
-
Bertram Müller-Myhsok, John B. Harley, Patrick M. A. Sleiman, Anastasia Lucas, Murray H. Brilliant, Beibei Jiang, Jiayan Zhou, Molly A. Hall, John M. Wallace, Jason H. Moore, John McGuigan, Shefali S. Verma, Yuki Bradford, Hakon Hakonarson, Kristel Van Steen, Yanfei Zhang, Krzysztof Kiryluk, Sarah A. Pendergrass, Kristin Passero, Marylyn D. Ritchie, and Peggy L. Peissig
- Subjects
Cancer Research ,Heredity ,Eye Diseases ,Single Nucleotide Polymorphisms ,Datasets as Topic ,Genome-wide association study ,SUSCEPTIBILITY ,QH426-470 ,DISEASE ,Homozygosity ,Macular Degeneration ,0302 clinical medicine ,Medical Conditions ,Gene Frequency ,Medicine and Health Sciences ,Genetics (clinical) ,Genetics & Heredity ,0303 health sciences ,Heterozygosity ,Genomics ,BREAST-CANCER RISK ,Genetic Mapping ,ISCHEMIC-STROKE ,Phenotype ,Hypertension ,symbols ,Life Sciences & Biomedicine ,Research Article ,Single-nucleotide polymorphism ,Variant Genotypes ,Computational biology ,Biology ,Polymorphism, Single Nucleotide ,Cataract ,03 medical and health sciences ,symbols.namesake ,Genetic model ,LOCUS ,Genome-Wide Association Studies ,Genetics ,Humans ,Allele ,GENOME-WIDE ASSOCIATION ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Alleles ,030304 developmental biology ,TOOLS ,Science & Technology ,EMERGE NETWORK ,Models, Genetic ,Cataracts ,ELECTRONIC MEDICAL-RECORDS ,Biology and Life Sciences ,Computational Biology ,Human Genetics ,Glaucoma ,Genome Analysis ,POLYMORPHISM ,Minor allele frequency ,Ophthalmology ,Bonferroni correction ,Diabetes Mellitus, Type 2 ,Genetic Loci ,Lens Disorders ,Multiple comparisons problem ,030217 neurology & neurosurgery ,Genome-Wide Association Study - 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.
- Published
- 2021
5. Investigation of gene-gene interactions in cardiac traits and serum fatty acid levels in the LURIC Health Study
- Author
-
Marcus E. Kleber, Jiayan Zhou, Winfried Maerz, Kristin Passero, Bertram Müller-Myhsok, Nicole E. Palmiero, and Molly A. Hall
- Subjects
Male ,0301 basic medicine ,Heredity ,Pulmonology ,Single Nucleotide Polymorphisms ,Genome-wide association study ,Cardiovascular Medicine ,Biochemistry ,Vascular Medicine ,Medical Conditions ,0302 clinical medicine ,Germany ,Medicine and Health Sciences ,Drug Interactions ,Prospective Studies ,Aged, 80 and over ,chemistry.chemical_classification ,Multidisciplinary ,Fatty Acids ,Software Engineering ,Genomics ,Hematology ,Venous Thromboembolism ,Middle Aged ,Prognosis ,Lipids ,Genetic Mapping ,Phenotype ,Cardiovascular Diseases ,030220 oncology & carcinogenesis ,symbols ,Medicine ,Engineering and Technology ,Female ,Algorithms ,Research Article ,Adult ,Genetic Markers ,Computer and Information Sciences ,Adolescent ,Science ,Quantitative Trait Loci ,Cardiology ,Epistasis and functional genomics ,Variant Genotypes ,Single-nucleotide polymorphism ,Computational biology ,Biology ,Quantitative trait locus ,Polymorphism, Single Nucleotide ,Computer Software ,Young Adult ,03 medical and health sciences ,symbols.namesake ,Thromboembolism ,Genetics ,Genome-Wide Association Studies ,Humans ,Blood Coagulation ,Aged ,Pharmacology ,Coagulation Disorders ,Biology and Life Sciences ,Computational Biology ,Fatty acid ,Human Genetics ,Thrombosis ,Epistasis, Genetic ,Genome Analysis ,030104 developmental biology ,Bonferroni correction ,chemistry ,Case-Control Studies ,Multiple comparisons problem ,Epistasis ,Pulmonary Embolism ,Follow-Up Studies ,Genome-Wide Association Study - Abstract
Epistasis analysis elucidates the effects of gene-gene interactions (G×G) between multiple loci for complex traits. However, the large computational demands and the high multiple testing burden impede their discoveries. Here, we illustrate the utilization of two methods, main effect filtering based on individual GWAS results and biological knowledge-based modeling through Biofilter software, to reduce the number of interactions tested among single nucleotide polymorphisms (SNPs) for 15 cardiac-related traits and 14 fatty acids. We performed interaction analyses using the two filtering methods, adjusting for age, sex, body mass index (BMI), waist-hip ratio, and the first three principal components from genetic data, among 2,824 samples from the Ludwigshafen Risk and Cardiovascular (LURIC) Health Study. Using Biofilter, one interaction nearly met Bonferroni significance: an interaction between rs7735781 in XRCC4 and rs10804247 in XRCC5 was identified for venous thrombosis with a Bonferroni-adjusted likelihood ratio test (LRT) p: 0.0627. A total of 57 interactions were identified from main effect filtering for the cardiac traits G×G (10) and fatty acids G×G (47) at Bonferroni-adjusted LRT p < 0.05. For cardiac traits, the top interaction involved SNPs rs1383819 in SNTG1 and rs1493939 (138kb from 5' of SAMD12) with Bonferroni-adjusted LRT p: 0.0228 which was significantly associated with history of arterial hypertension. For fatty acids, the top interaction between rs4839193 in KCND3 and rs10829717 in LOC107984002 with Bonferroni-adjusted LRT p: 2.28×10-5 was associated with 9-trans 12-trans octadecanoic acid, an omega-6 trans fatty acid. The model inflation factor for the interactions under different filtering methods was evaluated from the standard median and the linear regression approach. Here, we applied filtering approaches to identify numerous genetic interactions related to cardiac-related outcomes as potential targets for therapy. The approaches described offer ways to detect epistasis in the complex traits and to improve precision medicine capability.
- Published
- 2020
- Full Text
- View/download PDF
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