Biology subjects

Zekavat, S. M.

Publications and source records attributed to Zekavat, S. M..

4 recordsLinked to original sources

bioRxiv2018

Genetic association of photoplethysmography-derived arterial stiffness index with blood pressure and coronary artery disease

BackgroundArterial stiffness index (ASI) is independently associated with blood pressure and coronary artery disease (CAD) in epidemiologic studies. However, it is unknown whether these associations represent causal relationships.\n\nObjectivesHere, we assess whether genetic predisposition to increased ASI is associated with elevated blood pressure and CAD risk.\n\nMethodsGenome-wide association analysis (GWAS) of finger photoplethysmography-derived ASI was performed in 131,686 participants from the UK Biobank. Across UK Biobank participants not in the ASI GWAS, a 6-variant ASI polygenic risk score was calculated. The ASI polygenic score was associated with systolic and diastolic blood pressures (SBP, DBP, N=208,897), and with incident CAD over 10 years follow-up (N=223,061; 7,534 cases). The lack of CAD association observed was replicated among 184,305 participants (60,810 cases) from the Coronary Artery Disease Genetics Consortium (CARDIOGRAMplusC4D).\n\nResultsWe replicated prior reports of the epidemiologic association of ASI with SBP (Beta 0.55mmHg, [95% CI, 0.45-0.65], P=5.77x10-24), DBP (Beta 1.05mmHg, [95% CI, 0.99-1.11], P=7.27x10-272), and incident CAD (HR 1.08 [95% CI, 1.04-1.11], P=1.5x10-6) in multivariable models. While each SD increase in genetic predisposition to elevated ASI was highly associated with SBP (Beta 4.63 mmHg [95% CI, 2.1-7.2]; P=3.37x10-4), and DBP (Beta 2.61 mmHg [95% CI, 1.2-4.0]; P=2.85x10-4), no association was observed with incident CAD in UK Biobank (HR 1.12 [95% CI, 0.55-2.3]; P=0.75), or with prevalent CAD in CARDIOGRAMplusC4D (OR 0.56 [95% CI, 0.26-1.24]; P=0.15).\n\nConclusionsA genetic predisposition to higher ASI was associated with elevated blood pressure but not with increased risk of developing CAD.\n\nCondensed AbstractArterial stiffness index (ASI) is proposed by some as a surrogate of blood pressure and coronary artery disease (CAD) risk based on epidemiologic analyses. We tested whether genetic predisposition to increased ASI is associated with elevated blood pressure and CAD risk to assess whether these represent causal relationships. We find that a genetic predisposition to higher ASI is associated with elevated systolic (Beta 4.63 mmHg [95% CI, 2.1-7.2]) and diastolic blood pressures (Beta 2.61 mmHg [95% CI, 1.2-4.0]) in the UK Biobank, but not associated with incident CAD in the UK Biobank (P=0.75) or with prevalent CAD in CARDIOGRAMplusC4D (P=0.15). These data support a causal relationship of ASI with blood pressure but do not support the notion that ASI is a suitable surrogate for CAD risk.

bioinformatics↗

bioRxiv2018

A statistical framework for cross-tissue transcriptome-wide association analysis

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to predict (impute) gene expression levels from genotypes from samples with matched genotypes and expression levels in a specific tissue. However, it is challenging to develop robust and accurate imputation models with limited sample sizes for any single tissue. Here, we first introduce a multi-task learning approach to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average 39% improvement in imputation accuracy and generated effective imputation models for an average 120% (range 13%-339%) more genes in each tissue. We then describe a summary statistic-based testing framework that combines multiple single-tissue associations into a single powerful metric to quantify overall gene-trait association at the organism level. When our method, called UTMOST, was applied to analyze genome wide association results for 50 complex traits (Ntotal=4.5 million), we were able to identify considerably more genes in tissues enriched for trait heritability, and cross-tissue analysis significantly outperformed single-tissue strategies (p=1.7e-8). Finally, we performed a cross-tissue genome-wide association study for late-onset Alzheimers disease (LOAD) and replicated our findings in two independent datasets (Ntotal=175,776). In total, we identified 69 significant genes, many of which are novel, leading to novel insights on LOAD etiologies.

genetics↗

bioRxiv2017

Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle where apolipoprotein(a) (protein product of the LPA gene) is covalently attached to apolipoprotein B. Lp(a) is a highly heritable, causal risk factor for cardiovascular diseases and varies in concentrations across ancestries. To comprehensively delineate the inherited basis for plasma Lp(a), we performed deep-coverage whole genome sequencing in 8,392 individuals of European and African American ancestries. Through whole genome variant discovery and direct genotyping of all structural variants overlapping LPA, we quantified the 5.5kb kringle IV-2 copy number (KIV2-CN), a known LPA structural polymorphism, and developed a model for its imputation. Through common variant analysis, we discovered a novel locus (SORT1) associated with Lp(a)-cholesterol, and also genetic modifiers of KIV2-CN. Furthermore, in contrast to previous GWAS studies, we explain most of the heritability of Lp(a), observing Lp(a) to be 85% heritable among African Americans and 75% among Europeans, yet with notable inter-ethnic heterogeneity. Through analyses of aggregates of rare coding and non-coding variants with Lp(a)-cholesterol, we found the only genome-wide significant signal to be at a non-coding SLC22A3 intronic window also previously described to be associated with Lp(a); however, this association was mitigated by adjustment with KIV2-CN. Finally, using an additional imputation dataset (N=27,344), we performed Mendelian randomization of LPA variant classes, finding that genetically regulated Lp(a) is more strongly associated with incident cardiovascular diseases than directly measured Lp(a), and is significantly associated with measures of subclinical atherosclerosis in African Americans.

genomics↗

bioRxiv2017

Deep-coverage whole genome sequences and blood lipids among 16,324 individuals

Deep-coverage whole genome sequencing at the population level is now feasible and offers potential advantages for locus discovery, particularly in the analysis rare mutations in non-coding regions. Here, we performed whole genome sequencing in 16,324 participants from four ancestries at mean depth >29X and analyzed correlations of genotypes with four quantitative traits - plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. We conducted a discovery analysis including common or rare variants in coding as well as non-coding regions and developed a framework to interpret genome sequence for dyslipidemia risk. Common variant association yielded loci previously described with the exception of a few variants not captured earlier by arrays or imputation. In coding sequence, rare variant association yielded known Mendelian dyslipidemia genes and, in non-coding sequence, we detected no rare variant association signals after application of four approaches to aggregate variants in non-coding regions. We developed a new, genome-wide polygenic score for LDL-C and observed that a high polygenic score conferred similar effect size to a monogenic mutation (~30 mg/dl higher LDL-C for each); however, among those with extremely high LDL-C, a high polygenic score was considerably more prevalent than a monogenic mutation (23% versus 2% of participants, respectively).

genomics↗