Courses

The following is the final list of courses and instructors for the Summer Institutes to be held in Atlanta from June 1st – June 12th, 2026.

There will be four streams: Population and Evolution, Integrative Genomics, Quantitative Genetics, and Statistical Analysis, as well as an Introductory module on Programming. These will be offered in four half-week sessions. The streams are merely guides, though they do reflect to some degree historical patterns of which courses students take successively. Most students take two or three modules, but you may take one (or four). Only nominate one course for each time bloc.

June 1 – June 3, 2026

CoursesInstructors
INTRO to Programming in R and PythonSini Nagpal, Patrick McGrath
PE1: Population GeneticsNadia Singh, Dahlia Nielsen
IG1: Genetics, Genomics and BiobanksGreg Gibson, Joanne Cole, King Jordan
QG1: Quantitative GeneticsBruce Walsh, Guilherme Rosa
ST1: Bayesian StatisticsKen Rice, Steve Qin

June 3 – June 5, 2026

CoursesInstructors
PE2: Statistical GeneticsBruce Weir, Sean Anderson
IG2: Epigenetics & Gene RegulationKaren Conneely, Jingjing Yang
QG2: Mixed Models in Quantitative GeneticsBruce Walsh, Guilherme Rosa
ST2: Regression and RegularizationIan Dworkin, Arbel Harpak

June 8 – June 10, 2026

CoursesInstructors
PE3: Artificial Intelligence/ML for GeneticsSudhir Kumar, Mindy Shi
IG3: Gene Expression & Single Cell GenomicsPeng Qiu, Saumya Jain
QG3: Association MappingJoelle Mbatchou, Moeen Riaz
ST3: Forensic GeneticsMike Coble, Sanne Aalbers, Bruce Weir

June 10 – June 12, 2026

CoursesInstructors
PE4: Molecular EvolutionJoe Lachance, Rebekah Rogers
IG4: Pathway and Network AnalysisAlison Motsinger-Reif, Greg Gibson
QG4: Whole Genome Sequence AnalysisLaura Raffield, Matt Conomos
ST4: Multivariate AnalysisJan Graffelman

The Summer Institute in Statistical Genetics (SISG) has introduced geneticists to modern methods of statistical analysis and statisticians to the challenges posed by modern genetic data. The goal of SISG is to strengthen the statistical and genetic proficiency and career preparation of scholars from all backgrounds, including those from groups historically underrepresented in STEM such as racial and ethnic minority groups, low income, first generation college students, and individuals with disabilities.