Description
Apply statistical genetics and bioinformatics methods to discover, quantify, and interpret genetic contributions to traits and disease using genotype, sequence, and phenotype data. Design and implement analytical pipelines, algorithms, and data resources for association testing, heritability estimation, imputation, and polygenic prediction in biomedical and population studies.
- • Recommend improvements to genetic data pipelines, QC procedures, and analysis workflows.
- • Stay current on statistical genetics methods, reference panels, and software through literature and conferences.
- • Collaborate with clinicians, biostatisticians, and product teams to define study designs and analysis requirements.
- • Partner with engineers to build, optimize, and scale genotype and sequence analysis pipelines.
- • Evaluate and benchmark tools for imputation, association testing, fine-mapping, and colocalization.
- • Provide models and code for GWAS, rare variant burden tests, and mixed-model association.
- • Generate, curate, and share GWAS and meta-analysis summary statistics.
- • Train teammates in statistical genetics concepts, tools, and reproducible workflows.
- • Improve usability of analysis dashboards, reports, and data portals for genetic results.
- • Lead and mentor analysts supporting data QC, imputation, and association analyses.
- • Develop or customize scripts, packages, and notebooks for QC, regression, and polygenic risk scoring.
- • Design data schemas and databases for variants, samples, phenotypes, and analysis results.
- • Build APIs or lightweight web tools to disseminate genetic findings securely.
- • Develop and apply methods such as linear mixed models, LD score regression, fine-mapping, and PRS modeling.
- • Create novel approaches for rare variant aggregation, gene-based tests, and causal inference.
- • Harmonize and integrate genotype, sequence, and phenotype data; incorporate eQTL or proteomic annotations.
- • Communicate findings via publications, presentations, and stakeholder reports.
- • Access, manage, and harmonize data from biobanks and public genomic resources.
- • Advise on study power, sample selection, phenotype definitions, and confounder control.
- • Analyze large-scale genomic datasets to identify associations, estimate heritability, and prioritize causal variants.
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Last reviewed: Jan 2026
