Research Article: GWAS and PheWAS of red blood cell components in a Northern Nevadan cohort

Date Published: June 13, 2019

Publisher: Public Library of Science

Author(s): Robert W. Read, Karen A. Schlauch, Gai Elhanan, William J. Metcalf, Anthony D. Slonim, Ramsey Aweti, Robert Borkowski, Joseph J. Grzymski, Honghuang Lin.

http://doi.org/10.1371/journal.pone.0218078

Abstract

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 4,673 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.

Partial Text

The complete blood count (CBC) is a widely used medical diagnostic test that is a compilation of the number, size, and composition of various components of the hematopoietic system. Abnormal CBC measures may indicate illness or disease. Mean corpuscular volume (MCV), platelet count (PC), and mean platelet volume (MPV) are specific CBC characteristics (hereby called RBC components), and linked to complex disorders such as anemia, alpha thalassemia and cardiovascular disease [1–5]. Platelets are involved in vascular integrity, wound healing, immune and inflammatory responses, and tumor metastasis; the role of platelets is also paramount in hemostasis and in the pathophysiology of atherothrombosis and cancer [6–12]. Additionally, abnormally high mean platelet volumes (MPV) are considered a predictor of post event outcome in coronary disease and myocardial infarction [13].

In this study, we first performed three independent GWASs of 4,673 Healthy Nevada Project participants with 500,000 genotypes against the RBC components: platelet count, mean platelet volume and mean corpuscular volume. We followed these with two independent PheWASs for each component to identify additional phenotypic associations with each blood component-significant SNP, and phenotypic associations with measures of each blood component.

 

Source:

http://doi.org/10.1371/journal.pone.0218078

 

Leave a Reply

Your email address will not be published.