publication . Article . Other literature type . 2018

Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank.

Louise Millard; Tom Gaunt; Neil Davies; George Davey Smith;
Open Access
  • Published: 01 Feb 2018 Journal: International Journal of Epidemiology, volume 47, pages 29-35 (issn: 0300-5771, eissn: 1464-3685, Copyright policy)
  • Publisher: Oxford University Press (OUP)
  • Country: United Kingdom
Abstract
<p>Motivation: Epidemiological cohorts typically contain a diverse set of phenotypes such that automation of phenome scans is non-trivial, because they require highly heterogeneous models. For this reason, phenome scans have to date tended to use a smaller homogeneous set of phenotypes that can be analysed in a consistent fashion. We present PHESANT (PHEnome Scan ANalysis Tool), a software package for performing comprehensive phenome scans in UK Biobank. General features: PHESANT tests the association of a specified trait with all continuous, integer and categorical variables in UK Biobank, or a specified subset. PHESANT uses a novel rule-based algorithm to dete...
Subjects
free text keywords: /dk/atira/pure/core/keywords/jean_golding, Jean Golding, /dk/atira/pure/researchoutput/pubmedpublicationtype/D016428, Journal Article, Epidemiology, General Medicine, Software Application Profile
Related Organizations
16 references, page 1 of 2

1 Denny JC, Bastarache L, Roden DM Phenome-wide association studies as a tool to advance precision medicine. Annu Rev Genomics Hum Genet 2016;17:353–73.27147087 [OpenAIRE] [PubMed]

2 Millard LAC, Davies NM, Timpson NJ, Tilling K, Flach PA, Davey Smith G MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization. Sci Rep 2015;5:16645.26568383 [OpenAIRE] [PubMed]

3 Tzoulaki I, Patel CJ, Okamura T, A nutrient-wide association study on blood pressure. Circulation 2012;126:2456–64.23093587 [OpenAIRE] [PubMed]

4 Patel CJ, Bhattacharya J, Butte AJ An environment-wide association study (EWAS) on type 2 diabetes mellitus. PLoS One 2010;5:e10746.20505766 [OpenAIRE] [PubMed]

5 Denny JC, Ritchie MD, Basford MA, PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations. Bioinformatics 2010;26:1205–10.20335276 [OpenAIRE] [PubMed]

6 Allen N, Sudlow C, Downey P, UK Biobank: Current status and what it means for epidemiology. Health Policy Technol 2012;1:123–26.

7 Davey Smith G, Ebrahim S‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?Int J Epidemiol 2003;32:1–22.12689998 [OpenAIRE] [PubMed]

8 Locke AE, Kahali B, Berndt SI, Genetic studies of body mass index yield new insights for obesity biology. Nature 2015;518:197–206.25673413 [OpenAIRE] [PubMed]

9 Timpson NJ, Harbord R, Davey Smith G, Zacho J, Tybjærg-Hansen A, Nordestgaard BG Does greater adiposity increase blood pressure and hypertension risk? Mendelian randomization using the FTO/MC4R genotype. Hypertension 2009;54:84–90.19470880 [PubMed]

10 Holmes MV, Lange LA, Palmer T, Causal effects of body mass index on cardiometabolic traits and events: a Mendelian randomization analysis. Am J Hum Genet 2014;94:198–208.2446 2370 [OpenAIRE] [PubMed]

11 Mumby HS, Elks CE, Li S, Mendelian randomisation study of childhood BMI and early menarche. J Obes 2011;2011:180729.21773002 [OpenAIRE] [PubMed]

12 VanderWeele TJ Outcome-wide epidemiology. Epidemiology 2017;28:399–402 28166102 [OpenAIRE] [PubMed]

13 Cao Y, Rajan SS, Wei P Mendelian randomization analysis of a time-varying exposure for binary disease outcomes using functional data analysis methods. Genet Epidemiol 2016;40:744–55.27813215 [OpenAIRE] [PubMed]

14 Hemani G, Zheng J, Wade KH, MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations. bioRxiv 2016; doi: https://doi.org/10.1101/078972.

15 Emdin CA, Khera AV, Natarajan P, Phenotypic characterization of genetically lowered human lipoprotein (a) levels. J Am Coll Cardiol 2016;68:2761–72.28007139 [OpenAIRE] [PubMed]

16 references, page 1 of 2
Abstract
<p>Motivation: Epidemiological cohorts typically contain a diverse set of phenotypes such that automation of phenome scans is non-trivial, because they require highly heterogeneous models. For this reason, phenome scans have to date tended to use a smaller homogeneous set of phenotypes that can be analysed in a consistent fashion. We present PHESANT (PHEnome Scan ANalysis Tool), a software package for performing comprehensive phenome scans in UK Biobank. General features: PHESANT tests the association of a specified trait with all continuous, integer and categorical variables in UK Biobank, or a specified subset. PHESANT uses a novel rule-based algorithm to dete...
Subjects
free text keywords: /dk/atira/pure/core/keywords/jean_golding, Jean Golding, /dk/atira/pure/researchoutput/pubmedpublicationtype/D016428, Journal Article, Epidemiology, General Medicine, Software Application Profile
Related Organizations
16 references, page 1 of 2

1 Denny JC, Bastarache L, Roden DM Phenome-wide association studies as a tool to advance precision medicine. Annu Rev Genomics Hum Genet 2016;17:353–73.27147087 [OpenAIRE] [PubMed]

2 Millard LAC, Davies NM, Timpson NJ, Tilling K, Flach PA, Davey Smith G MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization. Sci Rep 2015;5:16645.26568383 [OpenAIRE] [PubMed]

3 Tzoulaki I, Patel CJ, Okamura T, A nutrient-wide association study on blood pressure. Circulation 2012;126:2456–64.23093587 [OpenAIRE] [PubMed]

4 Patel CJ, Bhattacharya J, Butte AJ An environment-wide association study (EWAS) on type 2 diabetes mellitus. PLoS One 2010;5:e10746.20505766 [OpenAIRE] [PubMed]

5 Denny JC, Ritchie MD, Basford MA, PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations. Bioinformatics 2010;26:1205–10.20335276 [OpenAIRE] [PubMed]

6 Allen N, Sudlow C, Downey P, UK Biobank: Current status and what it means for epidemiology. Health Policy Technol 2012;1:123–26.

7 Davey Smith G, Ebrahim S‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?Int J Epidemiol 2003;32:1–22.12689998 [OpenAIRE] [PubMed]

8 Locke AE, Kahali B, Berndt SI, Genetic studies of body mass index yield new insights for obesity biology. Nature 2015;518:197–206.25673413 [OpenAIRE] [PubMed]

9 Timpson NJ, Harbord R, Davey Smith G, Zacho J, Tybjærg-Hansen A, Nordestgaard BG Does greater adiposity increase blood pressure and hypertension risk? Mendelian randomization using the FTO/MC4R genotype. Hypertension 2009;54:84–90.19470880 [PubMed]

10 Holmes MV, Lange LA, Palmer T, Causal effects of body mass index on cardiometabolic traits and events: a Mendelian randomization analysis. Am J Hum Genet 2014;94:198–208.2446 2370 [OpenAIRE] [PubMed]

11 Mumby HS, Elks CE, Li S, Mendelian randomisation study of childhood BMI and early menarche. J Obes 2011;2011:180729.21773002 [OpenAIRE] [PubMed]

12 VanderWeele TJ Outcome-wide epidemiology. Epidemiology 2017;28:399–402 28166102 [OpenAIRE] [PubMed]

13 Cao Y, Rajan SS, Wei P Mendelian randomization analysis of a time-varying exposure for binary disease outcomes using functional data analysis methods. Genet Epidemiol 2016;40:744–55.27813215 [OpenAIRE] [PubMed]

14 Hemani G, Zheng J, Wade KH, MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations. bioRxiv 2016; doi: https://doi.org/10.1101/078972.

15 Emdin CA, Khera AV, Natarajan P, Phenotypic characterization of genetically lowered human lipoprotein (a) levels. J Am Coll Cardiol 2016;68:2761–72.28007139 [OpenAIRE] [PubMed]

16 references, page 1 of 2
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