Integrating EMR-Linked and <i>In Vivo</i> Functional Genetic Data to Identify New Genotype-Phenotype Associations

<div><p>The coupling of electronic medical records (EMR) with genetic data has created the potential for implementing reverse genetic approaches in humans, whereby the function of a gene is inferred from the shared pattern of morbidity among homozygotes of a genetic variant. We explored the feasibility of this approach to identify phenotypes associated with low frequency variants using Vanderbilt's EMR-based BioVU resource. We analyzed 1,658 low frequency non-synonymous SNPs (nsSNPs) with a minor allele frequency (MAF)<10% collected on 8,546 subjects. For each nsSNP, we identified diagnoses shared by at least 2 minor allele homozygotes and with an association p<0.05. The diagnoses were reviewed by a clinician to ascertain whether they may share a common mechanistic basis. While a number of biologically compelling clinical patterns of association were observed, the frequency of these associations was identical to that observed using genotype-permuted data sets, indicating that the associations were likely due to chance. To refine our analysis associations, we then restricted the analysis to 711 nsSNPs in genes with phenotypes in the On-line Mendelian Inheritance in Man (OMIM) or knock-out mouse phenotype databases. An initial comparison of the EMR diagnoses to the known <i>in vivo</i> functions of the gene identified 25 candidate nsSNPs, 19 of which had significant genotype-phenotype associations when tested using matched controls. Twleve of the 19 nsSNPs associations were confirmed by a detailed record review. Four of 12 nsSNP-phenotype associations were successfully replicated in an independent data set: thrombosis (<i>F5</i>,rs6031), seizures/convulsions (<i>GPR98</i>,rs13157270), macular degeneration (<i>CNGB3</i>,rs3735972), and GI bleeding (<i>HGFAC</i>,rs16844401). These analyses demonstrate the feasibility and challenges of using reverse genetics approaches to identify novel gene-phenotype associations in human subjects using low frequency variants. As increasing amounts of rare variant data are generated from modern genotyping and sequence platforms, model organism data may be an important tool to enable discovery.</p></div

Association statistics for the 12 candidate nsSNPs.

<p>For each nsSNP, clinical phenotypes were constructed using diagnosis codes that closely approximated the phenotype descriptions in the OMIM and KO mouse databases. Shown are the subject counts and results of exact logistic regression analyses comparing minor allele homozygotes to matched common allele homozygotes. The common allele homozygotes were matched for age, race, gender and data set.</p

Overview of the nsSNP selection process.

<p>There was no difference in number of diagnoses significantly associated with the 1,658 nsSNPs when compared to genotype-permuted data. Hence, a nsSNP selection strategy that compared to diagnoses to those reported in either OMIM or the KO Mouse data was used. A multi-step selection and review process identified 12 candidate nsSNPs.</p

Population characteristics.

<p>Population characteristics.</p

Characteristics of the selected nsSNPs.

<p>OMIM/KO mouse phenotypes are associated at the gene level, not the specific nsSNP. Minor allele frequencies (MAF) are based on the frequencies observed in this study population. Chromosome and position are from Human Annotation Release 104.</p

Replication analyses.

<p>Replication analyses for nsSNP-phenotype associations using an additive logistic regression model adjusting for age, gender and principal components. A (—) indicates that less than 50 cases (i.e., individuals with the given phenotype) were available for analyses.</p

Supplemental material for Racial differences in patients referred for right heart catheterization and risk of pulmonary hypertension

<p>Supplemental material for Racial differences in patients referred for right heart catheterization and risk of pulmonary hypertension by Bin Q. Yang, Tufik R. Assad, Jared M. O'Leary, Meng Xu, Stephen J. Halliday, Reid W. D'Amico, Eric H. Farber-Eger, Quinn S. Wells, Anna R. Hemnes and Evan L. Brittain in Pulmonary Circulation</p

Mechanistic Phenotypes: An Aggregative Phenotyping Strategy to Identify Disease Mechanisms Using GWAS Data

<div><p>A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases. We hypothesized that these disease mechanisms could be identified using low minor allele frequency (MAF<0.1) non-synonymous SNPs (nsSNPs) associated with “mechanistic phenotypes”, comprised of collections of related diagnoses. We studied two mechanistic phenotypes: (1) thrombosis, evaluated in a population of 1,655 African Americans; and (2) four groupings of cancer diagnoses, evaluated in 3,009 white European Americans. We tested associations between nsSNPs represented on GWAS platforms and mechanistic phenotypes ascertained from electronic medical records (EMRs), and sought enrichment in functional ontologies across the top-ranked associations. We used a two-step analytic approach whereby nsSNPs were first sorted by the strength of their association with a phenotype. We tested associations using two reverse genetic models and standard additive and recessive models. In the second step, we employed a hypothesis-free ontological enrichment analysis using the sorted nsSNPs to identify functional mechanisms underlying the diagnoses comprising the mechanistic phenotypes. The thrombosis phenotype was solely associated with ontologies related to blood coagulation (Fisher's p = 0.0001, FDR p = 0.03), driven by the <i>F5, P2RY12</i> and <i>F2RL2</i> genes. For the cancer phenotypes, the reverse genetics models were enriched in DNA repair functions (p = 2×10−5, FDR p = 0.03) (<i>POLG/FANCI, SLX4/FANCP, XRCC1, BRCA1, FANCA, CHD1L</i>) while the additive model showed enrichment related to chromatid segregation (p = 4×10−6, FDR p = 0.005) (<i>KIF25, PINX1</i>). We were able to replicate nsSNP associations for <i>POLG/FANCI, BRCA1, FANCA</i> and <i>CHD1L</i> in independent data sets. Mechanism-oriented phenotyping using collections of EMR-derived diagnoses can elucidate fundamental disease mechanisms.</p></div

Enriched ontologies for genes associated with the cancer phenotypes in whites.

<p> All ontologies with an FDR p<0.05 are shown.</p><p> (1) The association p-value cut-off that gave the strongest enrichment.</p><p> (2) Number of genes with p-values below the p-value cut-off.</p><p> (3) Number of genes with the ontology.</p

Overview of the nsSNP association approaches.

<p>Panel (a) describes key features of the SNP association approaches used. Panel (b) shows, for a single hypothetical SNP, how assignment of affection status for homozygotes for the minor allele (HZMAs) varies by the approaches. The table lists cancer codes present among the HZMAs, the number of HZMAs that have the cancer code and the Fisher's p-value comparing the proportion of affected HZMAs with the cancer to the proportion in the common allele homozygotes. For this example, all of the listed cancers are assumed to be constituents of the mechanistic phenotype. For the standard genetic models, all subjects with any of the cancers are classified as cases. In contrast, the 2 reverse genetics approaches only analyze subsets of these subjects with cancers meeting pre-specified criteria, as designated by the brackets.</p