معرفی
Hanna Julienne is a permanent researcher in the Statistical Genetics Unit at Institut Pasteur, where she focuses on developing and applying advanced statistical methods to understand the genetic basis of complex diseases. She is actively involved in large-scale genomic studies, particularly Genome-Wide Association Studies (GWAS), and leads key projects such as JASS (Joint Analysis of GWAS Summary Statistics) and a JOBIM 2021 pilot project on gender equity in scientific conferences.
Her research spans statistical genetics, bioinformatics, and biostatistics, with a strong emphasis on methodological innovation for handling large, complex datasets. She is particularly interested in pleiotropy, multi-trait analysis, and extending genetic studies to diverse ancestral populations to reduce bias and improve inclusivity in genomic research.
The recent publications highlight a consistent focus on GWAS methodology, missing data imputation, and multi-trait modeling, with applications in chronic obstructive pulmonary disease (COPD), COVID-19, and gender disparities in science. Her work combines computational innovation with real-world biological and public health implications.
- Gender-based disparities and biases in science: An observational study of a virtual conference.
- Multi-trait GWAS for diverse ancestries: mapping the knowledge gap.
- Identifying chronic obstructive pulmonary disease subtypes using multi-trait genetics.
She is also a key contributor to the Bioinformatics and Biostatistics HUB at Institut Pasteur, where she has co-taught in the Bioinformatics program for PhD students. Her work in the DEI executive office reflects her commitment to equity, diversity, and inclusion in science. She manages the JASS project, which enables joint analysis of GWAS datasets to detect variants missed by univariate approaches, and has contributed to software tools like RAISS and MGMM for imputation and modeling of incomplete data.
She is affiliated with interdisciplinary research teams and collaborates on projects involving microbiome, metabolomics, and epigenetics, demonstrating a broad impact across computational biology and public health genomics.
