Past research accomplishments
During my epidemiology master and PhD studies, I designed and implemented epidemiological studies under the framework of Genetic Risk and Outcome of Psychosis (GROUP) study, a multi-site 10 years longitudinal family-based cohort study with genetic, cardiometabolic, clinical (disease-related), functional and neuropsychological data collected at three time points. My objective was to dissect the clinical heterogeneity and cardiometabolic comorbidity in schizophrenia spectrum disorders. I applied group-based trajectory modeling, polygenic risk score analysis, multidimensional data reduction analysis, and multilevel analysis using SPSS, SAS, R, and STATA software to achieve my research objectives. Almost all of my study’s findings were published in various peer-reviewed highly reputable (top 10 -15% in the respective field) journals, which have a vast number of readers and some received top publication awards. Moreover, I globally collaborated with various research groups and individual researchers to conduct preclinical, clinical and public health studies, focusing mainly on cardiometabolic disease and risk factors, maternal and child health, nutrition, TB and HIV, and mental health. The findings were novel and received several appreciation awards. My individual and collaborative efforts made me receive more than 50 co-authorships with an h-index of 23 and an i10-index of 33. Currently, I am also among the top five contributors list of systematic reviews and meta-analyses in these fields in Ethiopia. Over time, I have developed consolidated methodological and statistical knowledge and skills. These experiences gave me the skills and the knowledge that allowed me to get my first postdoctoral research and teaching position with high appreciation.
Current research projects
Currently, I am involved in three large research projects. The first project focus on developing a data-driven decision-making model to select personalized treatment for patients with depression. I am involved in acquiring and synthesizing published evidence that is relevant for building the model. In my collaboration at the department of
Epidemiology, University Medical Center Groningen, I am involved in a project focusing on investigating the genetic bases of cardiometabolic complications, social functioning and housing trajectories, and stigma and social inclusion in patients with schizophrenia spectrum disorders. We apply polygenic risk score analysis, group-based trajectory modeling, machine learning and Markov transition model to answer our research questions. Moreover, as an independent principal investigator, I am working on a project aiming to provide an overview of publication rates, authors’ collaboration networks, hotspot research topics, methodological quality and uptake of systematic reviews and meta-analyses in preclinical, clinical and public health fields in Ethiopia.
Future directions of research
I see this fellowship and the overarching theme of your genetic epidemiology research lab as an opportunity to expand my epidemiological and statistical methods knowledge and skills, and build my capacity to perform advanced genomic data analysis. I will investigate the association between maternal genetic susceptibility and abnormal fetal growth during my fellowship. Data from 2,334 Caucasian, African American, Hispanic and Asian pregnant women who participated in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies-Singletons will be used.1 To achieve my objective, I will follow the following steps. First, a single composite score will be generated from the six (estimated fetal weight, biparietal diameter, head circumference, abdominal circumference, and femur and humerus lengths) fetal growth biometrics obtained from ultrasound examination by summing or averaging the Z-score of each measure. Second, Group-based trajectory modeling (GBTM) will be applied on a composite score of fetal growth measures to identify latent subgroups with different levels of fetal growth and explore trajectories across the gestational periods.2 GBTM allows using the realtime data to classify fetal growth status instead of relying on the old 10-percentile dichotomization system, which may be liable to bias to diagnose intrauterine fetal growth restriction. Third, using identified latent subgroups in the most parsimonious GBTM model as an outcome, binomial, multinomial, or ordinal regression Genome-Wide Association Studies (GWAS) will be done.3-5 The the choice of binomial (classical), multinomial or ordinal logistic regression model depend on the nature of identified latent subgroups. Finally, based on the significant genes identified in the GWAS, whole-genome sequencing, functional annotation and enrichment analysis will be done only for the abnormal fetal growth latent subgroups to investigate maternal gene expressions and functional roles that play in abnormal fetal growth.6 The four steps will be separately implemented for each racial/ethnic group of women. The analysis will be done using R and SAS software in addition to relevant genetic analysis tools. The findings from this study can be relevant for identifying high-risk fetuses and initiating interventions to prevent late-onset cardiometabolic diseases.
Relevant references
- Grewal J, Grantz KL, Zhang C, et al. Cohort Profile: NICHD Fetal Growth StudiesSingletons and Twins. Int J Epidemiol Feb 1 2018;47(1):25-25l.
- Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol 2010;6:109-138.
- German CA, Sinsheimer JS, Klimentidis YC, Zhou H, Zhou JJ. Ordered multinomial regression for genetic association analysis of ordinal phenotypes at Biobank scale. Genet Epidemiol Apr 2020;44(3):248-260.
- Jostins L, McVean G. Trinculo: Bayesian and frequentist multinomial logistic regression for genome-wide association studies of multi-category phenotypes. Bioinformatics Jun 15 2016;32(12):1898-1900.
- Uffelmann E, Huang QQ, Munung NS, et al. Genome-wide association studies. Nature Reviews Methods Primers 2021/08/26 2021;1(1):59.
- Reed E, Nunez S, Kulp D, Qian J, Reilly MP, Foulkes AS. A guide to genome-wide association analysis and post-analytic interrogation. Stat Med Dec 10 2015;34(28):3769-3792.
