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Genetic Variation Contributes to Gene Expression Response in Ischemic Stroke: an Eqtl Study

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Genetic Variation Contributes to Gene Expression Response in Ischemic Stroke: an Eqtl Study UC Davis UC Davis Previously Published Works Title Genetic variation contributes to gene expression response in ischemic stroke: an eQTL study. Permalink https://escholarship.org/uc/item/3v01424z Journal Annals of clinical and translational neurology, 7(9) ISSN 2328-9503 Authors Amini, Hajar Shroff, Natasha Stamova, Boryana et al. Publication Date 2020-09-01 DOI 10.1002/acn3.51154 Peer reviewed eScholarship.org Powered by the California Digital Library University of California RESEARCH ARTICLE Genetic variation contributes to gene expression response in ischemic stroke: an eQTL study Hajar Amini , Natasha Shroff, Boryana Stamova, Eva Ferino, Paulina Carmona-Mora, Xinhua Zhan, Preston P. Sitorus, Heather Hull, Glen C. Jickling, Frank R. Sharp & Bradley P. Ander Department of Neurology, University of California at Davis, Sacramento, California, 95817 Correspondence Abstract Hajar Amini and Bradley P. Ander, Objective Department of Neurology, University of : Single nucleotide polymorphisms (SNPs) contribute to complex dis- California at Davis, MIND Institute orders such as ischemic stroke (IS). Since SNPs could affect IS by altering gene Biosciences Building, 2805 50th Street, Room expression, we studied the association of common SNPs with changes in mRNA 2434, Sacramento, CA 95817. Phone: 916- expression (i.e. expression quantitative trait loci; eQTL) in blood after IS. 703-0384; Fax: 916-703-0369; E-mail: Methods: RNA and DNA were isolated from 137 patients with acute IS and [email protected], [email protected] 138 vascular risk factor controls (VRFC). Gene expression was measured using Affymetrix HTA 2.0 microarrays and SNP variants were assessed with Axiom Funding Information Biobank Genotyping microarrays. A linear model with a genotype (SNP) 9 These studies were supported by grants from diagnosis (IS and VRFC) interaction term was fit for each SNP-gene pair. the National Institutes of Health (NS097000, Results: The eQTL interaction analysis revealed significant genotype 9 diagno- NS101718, NS075035, NS079153, sis interaction for four SNP-gene pairs as cis-eQTL and 70 SNP-gene pairs as NS106950 to FRS, BSS, BPA, GCJ) and grants trans-eQTL. Cis-eQTL involved in the inflammatory response to IS included from the American Heart Association (GCJ, rs56348411 which correlated with neurogranin expression (NRGN), rs78046578 BSS). National Institute of Neurological CXCL10 Disorders and Stroke which correlated with expression, rs975903 which correlated with SMAD4 expression, and rs62299879 which correlated with CD38 expression. Received: 28 February 2020; Revised: 27 These four genes are important in regulating inflammatory response and BBB June 2020; Accepted: 13 July 2020 stabilization. SNP rs148791848 was a strong trans-eQTL for anosmin-1 (ANOS1) which is involved in neural cell adhesion and axonal migration and Annals of Clinical and Translational may be important after stroke. Interpretation: This study highlights the contri- Neurology 2020; 7(9): 1648–1660 bution of genetic variation to regulating gene expression following IS. Specific inflammatory response to stroke is at least partially influenced by genetic varia- doi: 10.1002/acn3.51154 tion. This has implications for progressing toward personalized treatment strategies. Additional research is required to investigate these genes as therapeu- tic targets. Introduction assessed the effects of single nucleotide polymorphisms (SNPs) on gene expression (mRNA levels) following IS. Gene expression studies of blood have shown different SNPs that affect RNA expression are called expression gene profiles for ischemic stroke (IS) compared to con- quantitative trait loci (eQTL). These are widespread in trols,1 and different profiles for IS compared to intracere- the genome and account for part of the genetic effects bral hemorrhage.2 There are different profiles for varying that contribute to complex genetic diseases. eQTLs are causes of IS3 that can predict causes of cryptogenic divided into those with local effects (cis-eQTLs), where strokes where the cause is not otherwise known.4 More- the genetic variant is located within 1 megabase (Mb) of over gene expression profiles in blood of IS patients prior the affected gene, and those with distant effects (trans- to administration of tPA predict those who develop hem- eQTLs), where the genetic variant is further away or on a orrhagic transformation one day later.5 These data raise different chromosome.7 Analysis of eQTL in large cohorts the question of whether some changes of gene expression (e.g., GTEx) has shown many diseases associated loci reg- might be genetically programmed, given that stroke has a ulate nearby genes, though a substantial fraction of dis- heritability ranging from 0.16 to 0.40.6 Thus, this study ease associated loci still remain unexplained8 and are 1648 ª 2020 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. H. Amini et al. eQTL in Blood of Ischemic Stroke Patients likely trans-eQTL found mainly in noncoding regions of Table 1. Demographic and clinical characteristics for ischemic stroke the genome.9 (IS) patients and vascular risk factor controls (VRFC) Blood is used here in part because it is readily accessi- Vascular risk Ischemic stroke ble in humans. More importantly, studying blood follow- factor controls patients P ing stroke provides an index of the coagulation status of (n = 138) (n = 137) value each patient as well as inflammatory and immune Age, y (SD) 60.9 (13.3) 61.4 (13.2) 0.780 response mechanisms following stroke that in part deter- Sex, female, n (%) 68 (49.3) 51 (37.2) 0.051 1 mine outcome. Race/ethnicity, n (%) 0.424 In this study, we have explored the influence of SNP Caucasian 81 (58.7) 86 (62.8) genotype on expression of genes that are different African American 14 (10.1) 20 (14.6) between blood of IS and controls. These eQTLs could Latino, Hispanic 16 (11.6) 9 (6.6) provide possible mechanisms by which SNPs influence IS Asian 13 (9.4) 12 (8.8) outcomes and provide prognostic and treatment targets. Other 14 (10.1) 10 (7.3) Hypertension, n (%) 86 (62.3) 98 (71.5) 0.124 Diabetes, n (%) 24 (17.4) 36 (26.3) 0.081 Materials and Methods Hypercholesterolemia, 64 (46.4) 66 (48.2) 0.809 n (%) The research protocol was approved by institutional Cause of stroke, n review boards of the University of California at Davis, (%) University of California at San Francisco and the Univer- Cardioembolic --- 24 (17.5) sity of Alberta. All subjects provided written informed Large vessel disease --- 23 (16.8) Lacunar --- 42 (30.7) consent and RNA and DNA were isolated from blood Cryptogenic --- 44 (32.1) samples collected from 137 ischemic stroke (IS) patients Other --- 4 (2.9) and 138 vascular risk factor matched controls including Smoking status, n (%) 0.423 diabetes and/or hypertension and/or hypercholesterolemia Current 24 (17.4) 32 (23.3) (VRFC). Gene expression of all protein-coding transcripts Former 40 (28.9) 40 (29.2) was quantified by Affymetrix HTA 2.0 microarrays10 and Never 74 (53.6) 65 (47.4) variants assessed by Axiom Biobank Genotyping microar- Alcohol consumption, 0.113 n (%) rays. To identify a linear regression model with a geno- Heavy 4 (2.9) 12 (10.14) 9 type diagnosis interaction term for each SNP-gene pair Mild 63 (45.65) 52 (37.96) was utilized and tested for significance. All the analyses Former Heavy 7 (5.07) 11 (8.03) were conducted using the Matrix eQTL package in the R Never 64 (46.38) 62 (45.25) statistical environment as described previously.11 Addi- P values represent the comparison between IS and VRFC using a two- tional detailed information is provided in the Supplemen- tailed t test or Fisher’s exact test/chi-square test. tary Materials and Methods File. Alcohol consumption as heavy and mild defined as ≥3 drinks/day and ≤2 drinks/day, respectively. Results Patient characteristics Analysis of genotype (SNP) 3 diagnosis effect on gene expression Subject characteristics including age, sex, race, smoking status, alcohol consumption, and vascular risk factors The SNP-gene pair interactions show the impact of geno- (hypertension, diabetes, and hypercholesterolemia) for type (SNP) on gene expression when the interaction sig- 137 IS and 138 VRFC subjects are presented in Table 1. nificantly differs between IS and VFRC subjects. These The mean age (Æ standard deviation (SD)) of the male SNP-gene pairs from the interaction analysis can indicate (n = 86) and the female (n = 51) stroke subjects were one of three different biological properties. First, they can 59.5 Æ 12.2 and 64.6 Æ 14.2, respectively. Average ages represent eQTL in VFRC or IS but not both. Second, of the male (n = 70) and female (n = 68) VCRF subjects eQTLs can indicate an opposite directional effect between were 59.1 Æ 14.4 and 62.8 Æ 11.9, respectively. There VFRC and IS. Third, eQTL may be in the same direction were no significant differences in subject demographics but of significantly different magnitude of impact between for age, sex, race, smoking status, alcohol consumption or VFRC and IS. More formally, the interaction term vascular risk factors including diabetes and/or hyperten- assesses whether there is a significant difference in the sion and/or hypercholesterolemia between IS and VRFC slope of the genotype-expression regression line between groups (Table 1). VRFC individuals and IS patients (Figure 1). ª 2020 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association 1649 eQTL in Blood of Ischemic Stroke Patients H. Amini et al.
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