Privé, Florian, et al. “Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores.” Human Genetics and Genomics Advances 3.4 (2022). [Open Access]
Privé, Florian. “Using the UK Biobank as a global reference of worldwide populations: application to measuring ancestry diversity from GWAS summary statistics.” Bioinformatics 38.13 (2022): 3477-3480. [Open Access]
Privé, Florian, et al. “Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort.” The American Journal of Human Genetics 109.1 (2022): 12-23. [Open Access]
Privé, Florian. “Optimal linkage disequilibrium splitting.” Bioinformatics 38.1 (2022): 255–256. [Open Access]
Privé, Florian, et al. “LDpred2: better, faster, stronger.” Bioinformatics 36.22-23 (2020): 5424-5431. [Open Access]
Privé, Florian, et al. “Efficient toolkit implementing best practices for principal component analysis of population genetic data.” Bioinformatics 36.16 (2020): 4449-4457. [Open Access]
Privé, Florian, et al. “Performing highly efficient genome scans for local adaptation with R package pcadapt version 4.” Molecular Biology and Evolution 37.7 (2020): 2153-2154. [Open access]
Privé, Florian, et al. “Making the most of Clumping and Thresholding for polygenic scores.” The American Journal of Human Genetics 105.6 (2019): 1213-1221. [Open access]
Privé, Florian, et al. “Efficient implementation of penalized regression for genetic risk prediction.” Genetics 212.1 (2019): 65-74. [Open access]
Privé, Florian, et al. “Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr.” Bioinformatics 34.16 (2018): 2781-2787. [Open access]
[invited] SMPGD 2022: Predicting complex traits and diseases from genetic data. [Slides]
APRS 2021: High-resolution portability of 245 polygenic scores when derived and applied in the same cohort. [Recording] [Slides]
e-Rum 2020: Ultra fast penalized regressions with R package {bigstatsr}. [Recording] [Slides]
Rencontres R 2018: The R package bigstatsr: Memory- and Computation-Efficient Statistical Tools for Big Matrices [Slides]
eRum 2018: An R package for statistical tools with big matrices stored on disk. [Recording] [Slides]
Recomb-Genetics 2018: Predicting complex diseases: performance and robustness. [Slides]
LIFE 2018: Predicting complex diseases: performance and robustness. [Slides]
hackseq 2017: Developing advanced R tutorials for genomic data analysis. [Website]
useR!2017: The R package bigstatsr: Memory- and Computation-Efficient Tools for Big Matrices. [Recording] [Slides]
ALT’2016: Goodness-of-fit tests for the Weibull distribution with censored data. [Slides]
You can reuse (and modify) some of my presentations as long as you give attribution (just as in open source licensing).
Memory- and Computation-Efficient Statistical Tools for Big Matrices (R-Lille RUG) [Recording]
Predicting complex traits and diseases from genetic data (Lecture at Karolinska Institute)
Efficient penalized regression methods for genetic prediction (Prediction Modelling Presentations at KCL)
Penalized methods for genetic data analysis (Lecture at King’s College London)
Efficient analysis of large-scale matrices with two R packages: bigstatsr and bigsnpr: presentation + exercise (State of The R RUG)
Efficient statistical tools for analyzing omics data, with a focus on polygenic risk prediction (Aarhus, Denmark)
High-dimensional data: a different kind of big data (Grenoble Data Club)
The R package {bigstatsr}: memory- and computation-efficient tools for big matrices stored on disk (Grenoble RUG)
advanced R course for PhD students (141H in five years); see online materials
introductory R course for PhD students (21H)
statistical methods for first year engineer students (third year after high school) at ENSIMAG (18H)
lectures and practical exercises in Mathematics for students in their first semester after high school (128H in two years)
software carpentry instructor – introductory R course (7H)