Phenome-Wide Association Studies: Embracing Complexity for Discovery

Pendergrass, Sarah A.; Verma, Anurag; Okula, Anna; Hall, Molly A.; Crawford, Dana C.; Ritchie, Marylyn D.

doi:10.1159/000381851

Cited by 15 publications

(8 citation statements)

References 153 publications

(144 reference statements)

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“…In a PheWAS, each genetic variant is tested for association with multiple phenotypes (the phenome) as opposed to a single phenotype, as in GWAS [30][31][32]. We used the Illumina ® Absorption, Distribution, Metabolism and Elimination (ADME) Core Panel (CA, USA) [33] to genotype 6067 European-American and 762 AfricanAmerican patients linked to deidentified electronic health records (EHRs).…”

Section: Future Science Groupmentioning

confidence: 99%

Evidence for Extensive Pleiotropy Among Pharmacogenes

et al. 2016

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Section: Future Science Groupmentioning

confidence: 99%

Evidence for Extensive Pleiotropy Among Pharmacogenes

et al. 2016

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“…However, deep phenotyping has so far not been applied to complex adiposity phenotypes. Current phenotyping of organisms is typically limited to the focus of the individual scientist, while advocates of phenomics argue that phenotypic data should instead be collected at multiple levels of granularity, from behavioural phenotypes to cell specific changes [59,60]. Phenomic level data is thought to be necessary to understand how genetic variants affect phenotypes and to provide raw data to decipher the causes of complex disease [55].…”

Section: Deep Phenotyping and Phenomics To Study Complex Quantitativmentioning

confidence: 99%

Quantitative analyses of adiposity dynamics in zebrafish

et al. 2019

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Adipose tissues often exhibit subtle, quantitative differences between individuals, leading to a graded series of adiposity phenotypes at the population level. Robust, quantitative analyses are vital for studying these differences. In this Commentary we highlight two articles from our lab that employ sensitive new methods in zebrafish capable of delineating complex and quantitative adiposity phenotypes. In the first article, we utilized in vivo imaging to systematically quantify zebrafish adipose tissues. We identified 34 regionally distinct zebrafish adipose tissues and developed statistical models to predict the size and variance of each adipose tissue over the course of zebrafish growth. We then employed these models to identify effects of strain and diet on adipose tissue growth. In the second article, we employed deep phenotyping to study complex disease-related adiposity traits. Using this methodology, we identified that adipose tissues have unique capacities to re-deposit lipid following food restriction and re-feeding. These distinct re-deposition potentials led to widespread fat distribution changes following refeeding. We discuss how these novel findings may provide relevance to health conditions such as anorexia nervosa. Together, the strategies described in these two articles can be used as unbiased and quantitative methods to uncover new relationships between genotype, diet and adiposity.

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“…Several genes currently implicated in OSA are associated with co-occurring conditions, suggesting shared etiologies 2; 34; 43–49 . Phenome-wide association studies (PheWAS), which test candidate variants for associations with a broad range of phenotypes 50–52 , is a powerful approach to help reveal pleiotropic genetic effects.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Genetic and Phenotypic Heterogeneity of Obstructive Sleep Apnea Using Electronic Health Records

Veatch

Bauer

Josyula

et al. 2019

Preprint

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38Obstructive sleep apnea (OSA) is defined by frequent episodes of reduced or complete 39 cessation of airflow during sleep and is linked to negative health outcomes. Understanding the 40 genetic factors influencing expression of OSA may lead to new treatment strategies. Electronic 41 health records can be leveraged to both validate previously reported OSA-associated genomic 42 variation and detect novel relationships between these variants and comorbidities. We identified 43 candidate single nucleotide polymorphisms (SNPs) via systematic literature review of existing 44 research. Using datasets available at Geisinger (n=39,407) and Vanderbilt University Medical 45Center (n=24,084), we evaluated associations between 48 SNPs and OSA diagnosis, defined 46 using clinical codes. We also evaluated associations between these SNPs and OSA severity 47 measures obtained from sleep reports at Geisinger (n=6,571). Finally, we used a phenome-wide 48 approach to perform discovery and replication analyses testing associations between OSA 49 candidate SNPs and other clinical codes and laboratory values. Ten SNPs were associated with 50 OSA diagnosis in at least one dataset, and one additional SNP was associated following meta-51 analysis across all datasets. Three other SNPs were solely associated in subgroups defined by 52 established risk factors (i.e., age, sex, and BMI). Five OSA diagnosis-associated SNPs, and 16 53 additional SNPs, were associated with OSA severity measures. SNPs associated with OSA 54 diagnosis were also associated with codes reflecting cardiovascular disease, diabetes, celiac 55 disease, peripheral nerve disorders and genitourinary symptoms. Results highlight robust OSA-56 associated SNPs, and provide evidence of convergent mechanisms influencing risk for co-57 occurring conditions. This knowledge can lead to more personalized treatments for OSA and 58 related comorbidities. 59 60In the United States (US), 50-70 million individuals suffer from a disorder of sleep and 62 wakefulness 1 . Among the most common sleep disorders is obstructive sleep apnea (OSA). OSA 63 is defined by frequent episodes of reduced (hypopnea) or complete cessation (apnea) of airflow 64 that occur as a consequence of upper airway obstruction during sleep. Approximately 34% of 65 men and 17% of women aged 30-70 exhibit mild OSA, and 13% and 6%, respectively, suffer 66 from moderate to severe OSA 2 . Notably, prevalence rates substantially increase by the age of 67 50 years old 2 . When left untreated, OSA represents a significant public health burden carrying a 68 higher risk of serious comorbidities such as cardiovascular disease 3-11 , cancer incidence 12 and 69 mortality, all causes 13; 14 , cognitive impairment 15 , and rate of progression of 70 neurodegeneration 16 . As such, identifying more effective diagnosis and management of OSA 71 are important areas of research. 72OSA is a complex, phenotypically heterogeneous disorder driven by both obesity-related 73 factors and other mechanisms [17][18][19] . A number of studies have ...

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Phenome-Wide Association Studies: Embracing Complexity for Discovery

Cited by 15 publications

References 153 publications

Evidence for Extensive Pleiotropy Among Pharmacogenes

Evidence for Extensive Pleiotropy Among Pharmacogenes

Quantitative analyses of adiposity dynamics in zebrafish

Characterization of Genetic and Phenotypic Heterogeneity of Obstructive Sleep Apnea Using Electronic Health Records

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