I obtained my PhD in genetic epidemiology at Queensland University of Technology (Australia), where my research was focused on using genetic and genomic approaches to identify risk factors for endometrial cancer. During my graduate studies, I gained experience in large-scale genetic association studies and leveraging the correlation between diseases in genetic studies to identify novel genetic variants associated with endometrial cancer. I also developed expertise in various statistical genetic approaches in multi-omics data, including fine-mapping and colocalization analyses, to prioritize candidate causal variants and genes. I also gained extensive experience in genetic causal inference analysis to infer causality between risk factors and health outcomes.
My research focus since moving to Stanford has been the identification of genetic and non-genetic determinants of cardiometabolic diseases. I am currently involved in projects including large-scale genetic association studies, multi-trait analysis with correlated traits, development and validation of polygenic risk scores, integrative analyses with multi-omics data, as well as Mendelian randomization analyses to advance our understanding of the genetic and environmental factors that contribute to cardiometabolic diseases.
Honors & Awards
2021 QUT Outstanding Doctoral Thesis Award, Queensland University of Technology (Australia) (2022)
QIMR Berghofer PhD Top-up Scholarship, QIMR Berghofer Medical Research Institute, Australia (2018 - 2020)
Biomed Link Travel Grant, University of Melbourne, Australia (2018)
Australian Government Research Training Program (RTP) Scholarship, Australian Government (2017-2020)
QIMR Berghofer Masters (Coursework) Scholarship, QIMR Berghofer Medical Research Institute, Australia (2015)
Themistocles Assimes, Postdoctoral Faculty Sponsor
Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases.
2023; 20 (1): 31
Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance.We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins.We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F).Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases.
View details for DOI 10.1186/s12014-023-09421-0
View details for PubMedID 37550624
View details for PubMedCentralID PMC10405520
- Contemporary Polygenic Scores of Low-Density Lipoprotein Cholesterol and Coronary Artery Disease Predict Coronary Atherosclerosis in Adolescents and Young Adults. Circulation. Genomic and precision medicine 2023: e004047
Genetic impact of blood C-reactive protein levels on chronic spinal & widespread pain.
European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
Causal mechanisms underlying systemic inflammation in spinal & widespread pain remain an intractable experimental challenge. Here we examined whether: (i) associations between blood C-reactive protein (CRP) and chronic back, neck/shoulder & widespread pain can be explained by shared underlying genetic variants; and (ii) higher CRP levels causally contribute to these conditions.Using genome-wide association studies (GWAS) of chronic back, neck/shoulder & widespread pain (N = 6063-79,089 cases; N = 239,125 controls) and GWAS summary statistics for blood CRP (Pan-UK Biobank N = 400,094 & PAGE consortium N = 28,520), we employed cross-trait bivariate linkage disequilibrium score regression to determine genetic correlations (rG) between these chronic pain phenotypes and CRP levels (FDR < 5%). Latent causal variable (LCV) and generalised summary data-based Mendelian randomisation (GSMR) analyses examined putative causal associations between chronic pain & CRP (FDR < 5%).Higher CRP levels were genetically correlated with chronic back, neck/shoulder & widespread pain (rG range 0.26-0.36; P ≤ 8.07E-9; 3/6 trait pairs). Although genetic causal proportions (GCP) did not explain this finding (GCP range - 0.32-0.08; P ≥ 0.02), GSMR demonstrated putative causal effects of higher CRP levels contributing to each pain type (beta range 0.027-0.166; P ≤ 9.82E-03; 3 trait pairs) as well as neck/shoulder pain effects on CRP levels (beta [S.E.] 0.030 [0.021]; P = 6.97E-04).This genetic evidence for higher CRP levels in chronic spinal (back, neck/shoulder) & widespread pain warrants further large-scale multimodal & prospective longitudinal studies to accelerate the identification of novel translational targets and more effective therapeutic strategies.
View details for DOI 10.1007/s00586-023-07711-7
View details for PubMedID 37069442
View details for PubMedCentralID 8213433
Discovery of genomic loci associated with sleep apnoea risk through multi-trait GWAS analysis with snoring.
Despite its association with severe health conditions, the aetiology of sleep apnoea remains understudied. This study sought to identify genetic variants robustly associated with sleep apnoea risk.We performed a genome-wide association study (GWAS) meta-analysis of sleep apnoea across five cohorts (NTotal=523,366), followed by a multi-trait analysis of GWAS (MTAG) to boost power, leveraging the high genetic correlation between sleep apnoea and snoring. We then adjusted our results for the genetic effects of body mass index (BMI) using multi-trait-based conditional & joint analysis (mtCOJO) and sought replication of lead hits in a large cohort of participants from 23andMe, Inc (NTotal=1,477,352; Ncases=175,522). We also explored genetic correlations with other complex traits and performed a phenome-wide screen for causally associated phenotypes using the latent causal variable method.Our sleep apnoea meta-analysis identified five independent variants with evidence of association beyond genome-wide significance. After adjustment for BMI, only one genome-wide significant variant was identified. MTAG analyses uncovered 49 significant independent loci associated with sleep apnoea risk. Twenty-nine variants were replicated in the 23andMe GWAS adjusting for BMI. We observed genetic correlations with several complex traits, including multisite chronic pain, diabetes, eye disorders, high blood pressure, osteoarthritis, chronic obstructive pulmonary disease, and BMI-associated conditions.Our study uncovered multiple genetic loci associated with sleep apnoea risk, thus increasing our understanding of the aetiology of this condition and its relationship with other complex traits.
View details for DOI 10.1093/sleep/zsac308
View details for PubMedID 36525587
A shared genetic signature for common chronic pain conditions and its impact on biopsychosocial traits.
The journal of pain
The multiple comorbidities & dimensions of chronic pain present a formidable challenge in disentangling its aetiology. Here, we performed genome-wide association studies of eight chronic pain types using UK Biobank data (N=4,037-79,089 cases; N=239,125 controls), followed by bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine (respectively) their genetic correlations and genetic causal proportion (GCP) parameters with 1,492 other complex traits. We report evidence of a shared genetic signature across chronic pain types as their genetic correlations and GCP directions were broadly consistent across an array of biopsychosocial traits. Across 5,942 significant genetic correlations, 570 trait pairs could be explained by a causal association (|GCP| > 0.6; 5% false discovery rate), including 82 traits affected by pain while 410 contributed to an increased risk of chronic pain (cf. 78 with a decreased risk) such as certain somatic pathologies (e.g., musculoskeletal), psychiatric traits (e.g., depression), socioeconomic factors (e.g., occupation) and medical comorbidities (e.g., cardiovascular disease). This data-driven phenome-wide association analysis has demonstrated a novel and efficient strategy for identifying genetically supported risk & protective traits to enhance the design of interventional trials targeting underlying causal factors and accelerate the development of more effective treatments with broader clinical utility. PERSPECTIVE: Through large-scale phenome-wide association analyses of >1,400 biopsychosocial traits, this article provides evidence for a shared genetic signature across eight common chronic pain types. It lays the foundation for further translational studies focused on identifying causal genetic variants and pathophysiological pathways to develop novel diagnostic & therapeutic technologies and strategies.
View details for DOI 10.1016/j.jpain.2022.10.005
View details for PubMedID 36252619
Dehydroepiandrosterone Sulfate and Colorectal Cancer Risk: A Mendelian Randomization Analysis.
Twin research and human genetics : the official journal of the International Society for Twin Studies
Colorectal cancer is the third most common and second most deadly type of cancer worldwide, with approximately 1.9 million cases and 0.9 million deaths worldwide in 2020. Previous studies have shown that estrogen and testosterone hormones are associated with colorectal cancer risk and mortality. However, the potential effect of their precursor, dehydroepiandrosterone sulfate (DHEAS), on colorectal cancer risk has not been investigated. Therefore, evaluating DHEAS's effect on colorectal cancer will expand our understanding of the hormonal contribution to colorectal cancer risk. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to investigate the causal effect of DHEAS on colorectal cancer. We obtained DHEAS and colorectal cancer genomewide association study (GWAS) summary statistics from the Leipzig Health Atlas and the GWAS catalog and conducted MR analyses using the TwoSampleMR R package. Our results suggest that higher DHEAS levels are causally associated with decreased colorectal cancer risk (odds ratio per unit increase in DHEAS levels z score = 0.70; 95% confidence interval [0.51, 0.96]), which is in line with previous observations in a case-control study of colon cancer. The outcome of this study will be beneficial in developing plasma DHEAS-based biomarkers in colorectal cancer. Further studies should be conducted to interpret the DHEAS-colorectal cancer association among different ancestries and populations.
View details for DOI 10.1017/thg.2022.31
View details for PubMedID 36053043