2026 Summer Intern - Human Genetics

2026 Summer Intern - Human Genetics

Department Summary

The Human Genetics Department is a deeply collaborative and interdisciplinary group dedicated to leveraging public and internal genetic data to elucidate causal biological mechanisms driving human disease for the purpose of drug discovery and development. By utilizing large-scale genetic analyses in ancestrally diverse populations and biobank-scale datasets (such as UK Biobank and All Of Us), the department drives target and biomarker discovery for prioritized diseases and therapies. Research ranges from computational methods development to targeted analyses of disease-relevant phenotypes. Integrating genetic, genomic, transcriptomic, and proteomic information with rich clinical data, we characterize the relationships between genetic variation, molecular phenotypes, and clinical outcomes.

This internship position is located in South San Francisco, on-site.

The Opportunity

Methods for Understanding Immune-Associated Genetic Variation: Genetic association studies have resulted in important insights into disease risk and progression. Most have focused on simple genetic variants, such as single nucleotide polymorphisms. Much genetic variation is complex, in that it may have high levels of allelic diversity or may be variable in copy number. The project will focus on developing methods for better understanding such complex variation, focusing on immune-associated genetic variants and their associations to disease. The Major Histocompatibility Complex (MHC), which includes the Human Leukocyte Antigen (HLA) genes, and the Killer Immunoglobulin-like Receptor (KIR) genes will be the main interest.

The intern will have the following main responsibilities:

• Probabilistic Association Models: Develop and implement statistical association tests designed to better handle extensive allelic diversity and account for errors in genotyping or uncertainty in imputed data, as well as population structure.

• Integration of Copy Number Variation (CNV): Refine association models to explicitly incorporate KIR copy number variation-a defining feature of the region-ensuring that the analysis accounts for structural variation alongside allelic diversity.

• Benchmarking: Apply the methods to existing large cohort datasets (e.g., GWAS). Compare the results against standard approaches to quantify improvements in statistical power and accuracy.

• Targeted Epistasis Testing: Create a workflow that restricts interaction testing to biologically plausible candidates, specifically focusing on the co-evolutionary relationship between KIR alleles and their corresponding HLA class I ligands.

Program Highlights

• Intensive 12-weeks, full-time (40 hours per week) paid internship.

• Program start dates are in May/June 2026.

• A stipend, based on location, will be provided to help alleviate costs associated with the internship.

• Ownership of challenging and impactful business-critical projects.

• Work with some of the most talented people in the biotechnology industry.

Who You Are (Required)

Required Education:

• Must be pursuing a PhD (enrolled student).

Required Majors: Statistical Genetics, Genetic Epidemiology, Biostatistics, Computational Biology, Bioinformatics, Applied Statistics (focusing on biological data) or related fields with an interest in genetics.

Required Skills:

• Statistics/Statistical Genetics: A good knowledge of statistics and the methods used in statistical genetics.

• Computational Proficiency: Proficiency in programming (R, Python, or C++) to implement complex algorithms and manage high computational burden.

• Problem solving skills: This includes forming new hypotheses and the design, implementation and testing of novel methods.

• Software: The ability to produce usable, tested software. The final deliverable of this project will be scalable, user-friendly software for running analyses.

Preferred Knowledge, Skills, and Qualifications

• Excellent communication, collaboration, and interpersonal skills.

• Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

• A strong grasp of GWAS and association testing methodologies would be an advantage.

• Experience handling outputs from genotyping arrays/sequencing platforms and managing data from large national biobanks or case-control cohorts.

• Proficient in software engineering practices, including version control using Git.

• (Desirable, but not necessary) Familiarity with the biological complexity of MHC and KIR regions.

Relocation benefits are not available for this job posting.

The expected salary range for this position based on the primary location of California is $50.00 hour. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for paid holiday time off benefits.

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.