R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment

Carroll, Robert J.; Bastarache, Lisa; Denny, Joshua C.

doi:10.1093/bioinformatics/btu197

Cited by 355 publications

(367 citation statements)

References 8 publications

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“…These records can be used in a PheWAS to test the relation of a specific genetic feature to the range of phenotypes captured by the medical record (Carroll et al 2014). We used a PheWAS to test the relationship of one genetic predictor to multiple phenotypes (Denny et al 2010).…”

Section: Phewas Analysismentioning

confidence: 99%

“…International Classification of Disease version 9 (ICD.9) codes and the dates on which they were coded were downloaded for each individual in our data set. These ICD.9 codes were aggregated into PheWAS codes, using the PheWAS package in R 3.2.0 (Carroll et al 2014). An individual who had two or more ICD.9 codes on different days that contribute to the same PheWAS code was considered a case for that PheWAS code.…”

Section: Phewas Analysismentioning

confidence: 99%

“…Individuals who did not have an ICD.9 code within a PheWAS category, but who had related codes, were also exclusions for a category. We utilized PheWAS code groups that have been previously defined (Carroll et al 2014). Associations were performed using logistic regression with the individual's heterozygosity ratio as the predictor.…”

Section: Phewas Analysismentioning

confidence: 99%

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Heterozygosity Ratio, a Robust Global Genomic Measure of Autozygosity and Its Association with Height and Disease Risk

Samuels¹,

Wang²,

et al. 2016

Genetics

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Greater genetic variability in an individual is protective against recessive disease. However, existing quantifications of autozygosity, such as runs of homozygosity (ROH), have proved highly sensitive to genotyping density and have yielded inconclusive results about the relationship of diversity and disease risk. Using genotyping data from three data sets with .43,000 subjects, we demonstrated that an alternative approach to quantifying genetic variability, the heterozygosity ratio, is a robust measure of diversity and is positively associated with the nondisease trait height and several disease phenotypes in subjects of European ancestry. The heterozygosity ratio is the number of heterozygous sites in an individual divided by the number of nonreference homozygous sites and is strongly affected by the degree of genetic admixture of the population and varies across human populations. Unlike quantifications of ROH, the heterozygosity ratio is not sensitive to the density of genotyping performed. Our results establish the heterozygosity ratio as a powerful new statistic for exploring the patterns and phenotypic effects of different levels of genetic variation in populations.

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Section: Phewas Analysismentioning

confidence: 99%

Section: Phewas Analysismentioning

confidence: 99%

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Heterozygosity Ratio, a Robust Global Genomic Measure of Autozygosity and Its Association with Height and Disease Risk

Samuels¹,

Wang²,

et al. 2016

Genetics

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“…TATES analyses were conducted on ICD9 codes that define 12 broad disease categories and those that mapped to inflammatory phenotypes defined by PheCodes. 29 For individual phenotypes, TATES combines the p values obtained in a single marker test to arrive at a global p value while correcting for observed correlational structure between phenotypes. 20 30 …”

Section: Statisticsmentioning

confidence: 99%

“…Focus was on 358 ICD9 codes that define inflammatory phenotypes. 29 Five SNPs were statistically significant for cross-phenotype associations (p≤1.9E-05; assuming experimentwise α=0.05, 2692 SNPs), including rs4349859, rs4418214, rs9391846, rs12175489 and rs2844505 (figure 2). The top SNP with the most significant cross-phenotype associations was the GWAS SNP rs4349859 ( p=2.3E-14).…”

Section: Cross-phenotype Association Analysis Of the Mhc Regionmentioning

confidence: 99%

Phenome-wide association study maps new diseases to the human major histocompatibility complex region

Liu

Mayer

et al. 2016

J Med Genet

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Background Over 160 disease phenotypes have been mapped to the major histocompatibility complex (MHC) region on chromosome 6 by genome-wide association study (GWAS), suggesting that the MHC region as a whole may be involved in the etiology of many phenotypes, including unstudied diseases. The phenome-wide association study (PheWAS), a powerful and complementary approach to GWAS, has demonstrated its ability to discover and rediscover genetic associations. The objective of this study is to comprehensively investigate the MHC region by PheWAS to identify new phenotypes mapped to this genetically important region. Methods In the current study, we systematically explored the MHC region using PheWAS to associate 2692 MHC-linked variants (minor allele frequency ≥ 0.01) with 6221 phenotypes in a cohort of 7481 subjects from the Marshfield Clinic Personalized Medicine Research Project. Results Findings showed that expected associations previously identified by GWAS could be identified by PheWAS (e.g. psoriasis, ankylosing spondylitis, type I diabetes, and celiac disease) with some having strong cross-phenotype associations potentially driven by pleiotropic effects. Importantly, novel associations with 8 diseases not previously assessed by GWAS (e.g., lichen planus) were also identified and replicated in an independent population. Many of these associated diseases appear to be immune-related disorders. Further assessment of these diseases in 16,484 Marshfield Clinic twins suggests that some of these diseases, including lichen planus, may have genetic etiologies. Conclusions These results demonstrate that the PheWAS approach is a powerful and novel method to discover SNP-disease associations, and is ideal when characterizing cross-phenotype associations, and further emphasizes the importance of the MHC region in human health and disease.

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Association of Thyroid Function Genetic Predictors With Atrial Fibrillation

et al. 2019

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R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment

Abstract: This R package is freely available under the Gnu Public License (GPL-3) from http://phewascatalog.org. It is implemented in native R and is platform independent.

Cited by 355 publications

References 8 publications

Heterozygosity Ratio, a Robust Global Genomic Measure of Autozygosity and Its Association with Height and Disease Risk

Heterozygosity Ratio, a Robust Global Genomic Measure of Autozygosity and Its Association with Height and Disease Risk

Phenome-wide association study maps new diseases to the human major histocompatibility complex region

Association of Thyroid Function Genetic Predictors With Atrial Fibrillation

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