Internship in Statistical Genetics

Background: A large body of neuroimaging research has documented a variety of abnormalities related to complex neuropsychiatric disorders and psychological traits. Building on these results, the study of neuroimaging phenotypes and their associated genetic and environmental factors has become a central component of ongoing research, with a strong potential for improving our understanding of disease etiologies and identifying promising therapeutic targets¹. Genetic studies have arisen as an area of particular interest in this context. Family-based and genome-wide association studies (GWAS) have shown that both mental disorders and neuroanatomical phenotypes are highly heritable^2,3, and highlighted strong genetic correlations between neuroimaging phenotypes and mental health outcomes^4,5. However, our knowledge about the genetic variations influencing human brain structure and function remains limited. In particular, questions remain on how to best design robust, non-biased descriptors of brain MRI (magnetic resonance imaging) phenotypes to better understand the underlying biological pathways and support the development of biomarkers addressing the lack of gold standards in mental health diagnosis⁶.

Objectives: The goal of the internship is to conduct pilot analyses to investigate the potential of machine learning  approaches to infer latent neuroimaging phenotypes displaying maximum fit with the genetics of mental health outcomes. The project build on readily available data and approaches developed by others and within the team^7–9.

Missions: Understand the objectives, constraints and data used for the proposed topic. Assess the suitability and implement existing machine learning tools to achieve the objective, and summarize this work to the team.

Qualifications: Applicants should have advanced expertise in Statistics, Bioinformatics, Computer Science or other relevant disciplines with a strong quantitative background. Previous experience working with large scale data are welcome.

Contact: Interested applicants should send their curriculum vitae, a cover letter explaining their motivation, and contact information for two references to Dr. Hugues Aschard (hugues.aschard@pasteur.fr) and Léo Henches (leo.henches@pasteur.fr).

1. Lui, S., Zhou, X. J., Sweeney, J. A. & Gong, Q. Psychoradiology: The Frontier of Neuroimaging in Psychiatry. Radiology 281, 357–372 (2016).
2. Toro, R. et al. Genomic architecture of human neuroanatomical diversity. Mol. Psychiatry 20, 1011–1016 (2015).
3. Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018).
4. Park, M. T. M. et al. Neuroanatomical phenotypes in mental illness: identifying convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia. J. Psychiatry Neurosci. JPN 43, 201–212 (2018).
5. Grasby, K. L. et al. The genetic architecture of the human cerebral cortex. Science 367, eaay6690 (2020).
6. Abi-Dargham, A. & Horga, G. The search for imaging biomarkers in psychiatric disorders. Nat. Med. 22, 1248–1255 (2016).
7. Nisar, S. & Haris, M. Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder. Mol. Psychiatry 28, 4995–5008 (2023).
8. Auvergne, A. et al. Multitrait analysis to decipher the intertwined genetic architecture of neuroanatomical phenotypes and psychiatric disorders. Biol. Psychiatry Cogn. Neurosci. Neuroimaging S2451-9022(24)00266–0 (2024) doi:10.1016/j.bpsc.2024.08.018.
9. Traut, N. et al. Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery. NeuroImage 255, 119171 (2022).