BIOGRAPHICAL SKETCH

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NAME: Wei Qxi Deng

eRA COMMONS USER NAME (credential, e.g., agency login): DENGWQ

POSITION TITLE: Assistant Professor of Psychiatry and Behavioural Neurosciences; Genomic Lead at Peter Boris Centre for Addictions Research

EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable. Add/delete rows as necessary.)

INSTITUTION AND LOCATION	DEGREE (if applicable)	Completion Date MM/YYYY	FIELD OF STUDY
University of Toronto	PhD	06/2021	Statistics, Statistical Genetics
McMaster University	MSc	08/2013	Health Research Methods
McMaster University	Bachelor	05/2011	Mathematics and Statistics

 

1. Personal Statement 

I am an early-career investigator, examining the genetics of self-regulation and related endophenotypes of addiction. The proposed research was in part inspired by the complex yet robust relationships between impulsive delay discounting and a wide range of psychiatric symptoms, and emergent research on the brain correlates of delay discounting.

 

Scientifically, I am the right candidate because my research program is built on the foundation of using statistical genetics methods to elucidate the genetics of complex diseases/traits, with a strong emphasis on biologically plausible intermediate phenotypes (e.g. impulsivity and related phenotypes from theoretical frameworks) and causal mechanism (e.g. through Mendelian randomization and longitudinal studies).

 

The proposed research represents a core component of my research program, with the overarching goal to understand the genetics of transdiagnostic phenotypes such as delay discounting and their implications for psychiatric conditions through statistical genetics and open science datasets. As an early-career investigator, I have published 20 peer-reviewed journal articles (7 as first or corresponding author). I have independently developed 4 software packages and co-developed 2 others, further advancing the open and reproducible science. I have extensive expertise in the methods and analysis of big biobank data, such as UK Biobank. My statistical genetics expertise was recognized by the International Genetic Epidemiology Society, selecting my work as the “Research Highlight of the Month” and invited me to deliver a polygenic risk score workshop at its 2021 annual meeting.

 

Currently, I am the Genomics Lead at the Peter Boris Centre for Addictions Research, directed by Dr. MacKillop, my mentor for this proposal. I have made significant contributions uncovering the genomic correlates of self-control and provided high-resolution insights into behavioral mechanisms underlying alcohol-related risk. We have also generated

 

This body of work directly informs the proposed investigation, which is committed to advancing precision medicine by elucidating the genetic basis of deep-phenotyped, high-resolution psychological and behavioral risk factors.

 

Key publications:

Deng W. Q., Belisario K, Doggett A, Pigeyre M, Paré G, Munafò MR, MacKillop J. (2025) Externalizing as a common genetic influence for a broad spectrum of substance use and behavioral conditions. Addiction.

 

Deng W. Q., Elsayed M, Belisario KL, Sanchez-Roige S, Palmer AA, MacKillop J. (2025) Genome-Wide Association Studies of Delay Discounting and Impulsive Personality Traits in Children From the Adolescent Behavior and Cognitive Development Study. Genes Brain Behavior.

 

Deng, W. Q., Belisario, K., Munafò, M. R., & MacKillop, J. (2024). Longitudinal characterization of impulsivity phenotypes boosts signal for genomic correlates and heritability. Molecular Psychiatry.

 

Deng, W. Q., Belisario, K., Gray, J. C., Levitt, E. E., & MacKillop, J. (2024). A high-resolution PheWAS approach to alcohol-related polygenic risk scores reveals mechanistic influences of alcohol reinforcing value and drinking motives. Alcohol and Alcoholism.

 

Deng, W. Q., Belisario, K., Gray, J. C., Levitt, E. E., Mohammadi-Shemirani, P., Singh, D., Pare, G., & MacKillop, J. (2023). Leveraging related health phenotypes for polygenic prediction of impulsive choice, impulsive action, and impulsive personality traits in 1534 European ancestry community adults. Genes, brain, and behavior.

 

 

1. Positions, Scientific Appointments, and Honors 

Positions and Employment:

 

• 2022–Present: Assistant Professor, Department of Psychiatry and Behavioural Neurosciences, McMaster University

 

• 2021–2022: Lecturer, Department of Psychiatry and Behavioural Neurosciences, McMaster University

 

• 2020–2021: Research Associate, The Centre for Addiction and Mental Health, University of Toronto

 

Honors:

 

• 2024: Constantine Douketis New Researcher Award

 

 

1. Contributions to Science 

A common theme in my work is to use statistical and statistical genetics techniques to understand complex traits and ultimately advance human health. This involves integrating diverse data types, from genomic and environmental factors to behavioral data, to identify patterns and relationships that contribute to diseases. My research focuses on both identifying genetic risk factors and also more theoretical work of methods development.

 

1. Genomic Contributions to Behavioral and Addiction Mechanisms. Many psychological and behavioral mechanisms have been robustly shown to contribute to the development and persistence of addictive behaviors. These mechanisms, including traits such as impulsivity, externalizing behaviors, and certain personality characteristics, are increasingly recognized as having a genetic basis. Numerous genes associated with these traits have been identified, many of which overlap with genes linked to chronic diseases like obesity and type 2 diabetes, as well as substance use behaviors, including alcohol and tobacco use. I have examined the genetics of impulsivity both in cross-sectional design in the general population, as well as a longitudinal design of young adults.

 

1. Deng W. Q., Belisario K, Doggett A, Pigeyre M, Paré G, Munafò MR, MacKillop J. (2025) Externalizing as a common genetic influence for a broad spectrum of substance use and behavioral conditions. Addiction.
2. Deng, W. Q., Belisario, K., Munafò, M. R., & MacKillop, J. (2024). Longitudinal characterization of impulsivity phenotypes boosts signal for genomic correlates and heritability. Molecular Psychiatry, 1-11.
3. Deng, W. Q., Belisario, K., Gray, J. C., Levitt, E. E., & MacKillop, J. (2024). A high-resolution PheWAS approach to alcohol-related polygenic risk scores reveals mechanistic influences of alcohol reinforcing value and drinking motives. Alcohol and Alcoholism, 59(2), agad093.
4. Deng, W. Q., Belisario, K., Gray, J. C., Levitt, E. E., Mohammadi-Shemirani, P., Singh, D., Pare, G., & MacKillop, J. (2023). Leveraging related health phenotypes for polygenic prediction of impulsive choice, impulsive action, and impulsive personality traits in 1534 European ancestry community adults. Genes, brain, and behavior, 22(3), e12848

 

2. Statistical Genetics. In statistical genetics, several key methods are used to understand genetic influences on traits. I have been actively contributing to the development of methods for polygenic risk scores, heritability estimation, and the X chromosome.

 

1245. Pathan, N., Deng, W. Q., Di Scipio, M., Khan, M., Mao, S., Morton, R. W., … & Paré, G. (2024). A method to estimate the contribution of rare coding variants to complex trait heritability. Nature Communications, 15(1), 1245.
1246. Deng, W. Q., Mao, S., Kalnapenkis, A., Esko, T., Mägi, R., Paré, G., & Sun, L. (2019). Analytical strategies to include the X‐chromosome in variance heterogeneity analyses: Evidence for trait‐specific polygenic variance structure. Genetic epidemiology, 43(7), 815-830. (IGES paper of the month)
1247. Paré, G., Mao, S., & Deng, W. Q. (2018). A robust method to estimate regional polygenic correlation under misspecified linkage disequilibrium structure. Genetic Epidemiology, 42(7), 636-647.
1248. Paré, G., Mao, S., & Deng, W. Q. (2017). A machine-learning heuristic to improve gene score prediction of polygenic traits. Scientific reports, 7(1), 12665.
1249. Pare, G., Mao, S., & Deng, W. Q. (2016). A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics. Scientific reports, 6, 27644. https://doi.org/10.1038/srep27644

 

3. Statistical Models and Methods. This line of work addresses difficult problems in understanding high-dimensional data structure and the need to reduce data dimension for inference, as well as proposing new statistical tests that explore difference in variability among groups.

 

297. Deng, W. Q., & Craiu, R. V. (2023). Exploring dimension learning via a penalized probabilistic principal component analysis. Journal of Statistical Computation and Simulation, 93(2), 266-297.
298. Deng, W. Q., Craiu, R. V., & Sun, L. (2022). Measuring the severity of multi-collinearity in high dimensions. arXiv preprint arXiv:2203.10360.
299. Deng, W. Q., Asma, S., & Paré, G. (2014). Meta-analysis of SNPs involved in variance heterogeneity using Levene’s test for equal variances. European journal of human genetics : EJHG, 22(3), 427–430. https://doi.org/10.1038/ejhg.2013.166

 

4. Software development In keeping with open and reproducible science, many of the work I conduct would result in new methods and protocols that need to be disseminated via software packages. These often focus on fast computational methods that can handle large-scale data.

 

49. Deng, W. Q., & Sun, L. (2022). gJLS2: an R package for generalized joint location and scale analysis in X-inclusive genome-wide association studies. G3, 12(4), jkac049.
50. Shungin, D.*, Deng, W. Q.*, Varga, T. V., Luan, J., Mihailov, E., Metspalu, A., GIANT Consortium, Morris, A. P., Forouhi, N. G., Lindgren, C., Magnusson, P. K. E., Pedersen, N. L., Hallmans, G., Chu, A. Y., Justice, A. E., Graff, M., Winkler, T. W., Rose, L. M., Langenberg, C., Cupples, L. A., … Franks, P. W. (2017). Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions. PLoS genetics, 13(6), e1006812.(*co-first author)
51. Deng, W. Q., & Paré, G. (2011). A fast algorithm to optimize SNP prioritization for gene-gene and gene-environment interactions. Genetic epidemiology, 35(7), 729–738.

 

 

5. Epigenetics. Epigenetics refers to changes in gene expression that do not involve alterations to the DNA sequence. For the moment, my research focuses exclusively on DNA methylation, which is the most commonly studied epigenetic mechanism. These changes are dynamic and can be influenced by environmental exposures like diet, stress, pollution, and toxins. For example, DNA methylation has been shown to accurately capture biological aging. My current work focuses on the influence of maternal environment on children health, with the long-term goal to gravitate towards using epigenetics to capture exposure to substances and the role of epigenetic changes on addiction.

 

93260. Deng, W. Q., Cawte, N., Campbell, N., Azab, S. M., de Souza, R. J., Lamri, A., … & Anand, S. S. (2024). Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry. Elife, 13, RP93260.
93261. Deng, W. Q., Pigeyre, M., Azab, S. M., Wilson, S. L., Campbell, N., Cawte, N., … & Anand, S. S. (2024). Consistent cord blood DNA methylation signatures of gestational age between South Asian and white European cohorts. Clinical Epigenetics, 16(1), 74.