GWAS and PheWAS of Red Blood Cell Components in a Northern Nevadan Cohort
Authors: Schlauch Karen A. , Read Robert W. , Elhanan Gai , Metcalf William J , Slonim Anthony D. , Aweti Ramsey , Borkowski Robert , Grzymski Joseph J. 
Published: November 2018

In this study, we perform a full genome-wide association study (GWAS) to identify statistically significantly associated single nucleotide polymorphisms (SNPs) with three red blood cell (RBC) components and follow it with two independent PheWASs to examine associations between phenotypic data (case-control status of diagnoses or disease), significant SNPs, and RBC component levels. We first identified associations between the three RBC components: mean platelet volume (MPV), mean corpuscular volume (MCV), and platelet counts (PC), and the genotypes of approximately 500,000 SNPs on the Illumina Infimum® DNA Human OmniExpress-24 BeadChip using a single cohort of 4,700 Northern Nevadans. Twenty-one SNPs in five major genomic regions were found to be statistically significantly associated with MPV, two regions with MCV, and one region with PC, with p<5x10-8. Twenty-nine SNPs and nine chromosomal regions were identified in 30 previous GWASs, with effect sizes of similar magnitude and direction as found in our cohort. The two strongest associations were SNP rs1354034 with MPV (p=2.4x10-13) and rs855791 with MCV (p=5.2x10-12). We then examined possible associations between these significant SNPs and incidence of 1,488 phenotype groups mapped from International Classification of Disease version 9 and 10 (ICD9 and ICD10) codes collected in the extensive electronic health record (EHR) database associated with Healthy Nevada Project consented participants. Further leveraging data collected in the EHR, we performed an additional PheWAS to identify associations between continuous red blood cell (RBC) component measures and incidence of specific diagnoses. The first PheWAS illuminated whether SNPs associated with RBC components in our cohort were linked with other hematologic phenotypic diagnoses or diagnoses of other nature. Although no SNPs from our GWAS were identified as strongly associated to other phenotypic components, a number of associations were identified with p-values ranging between 1x10-3 and 1x10-4 with traits such as respiratory failure, sleep disorders, hypoglycemia, hyperglyceridemia, GERD and IBS. The second PheWAS examined possible phenotypic predictors of abnormal RBC component measures: a number of hematologic phenotypes such as thrombocytopenia, anemias, hemoglobinopathies and pancytopenia were found to be strongly associated to RBC component measures; additional phenotypes such as (morbid) obesity, malaise and fatigue, alcoholism, and cirrhosis were also identified to be possible predictors of RBC component measures.

Author Summary

The combination of electronic health records and genomic data have the capability to revolutionize personalized medicine. Each separately contains invaluable data; however, combined, the two are able to identify new discoveries that may have long-term health benefits. The Healthy Nevada Project is a non-profit initiative between Renown Medical Center and the Desert Research Institute in Reno, NV. The project has so far collected a cohort of 6,500 Northern Nevadans, with extensive medical electronic health records in the Renown Health database. Combining the genotypes of these participants with the clinical data, this study’s aim is to find associations between genotypes (genes) and phenotypes (diagnoses and lab records). Here, we identify and examine clinical associations with red blood cell components such as platelet counts and mean platelet volume. These are components that have clinical relevance for several diseases, such as anemia, atherothrombosis and cancer. Our results from genome wide association studies mirror previous studies, and identify new associations. The extensive electronic health records enabled us to perform phenome wide associations to discover strong associations with hematologic components, as well as other important traits and diagnoses.