Statistical genetics and genomics brings together statistics,
computing, genetics, molecular biology, clinical research, and
population health to understand how genetic variation contributes
to disease, treatment response, and differences among people.
How do researchers find meaningful biological signals within
millions of genetic measurements?
Answering that question requires more than processing DNA data.
Biostatisticians help connect biological questions, study design,
population structure, complex genomic data, statistical methods,
and responsible interpretation.
Explore how biostatisticians connect the genetics and genomics
research team
Genetics and genomics research combines biological, clinical,
computational, and population perspectives. Biostatisticians connect
those perspectives by translating scientific questions into study
designs, analytical strategies, measures of uncertainty, and
evidence that the full research team can interpret.
The connecting discipline
Biostatisticians
Biostatisticians contribute across the full genomic research
process, from defining the scientific question through the
validation and interpretation of the findings.
Translate biological questions into testable objectives
Design studies and define appropriate comparison groups
Model genetic and molecular variation
Account for ancestry, relatedness, and population structure
Control false findings across millions of comparisons
Evaluate prediction, replication, and generalizability
Communicate uncertainty and analytical limitations
Select a collaborator to see how biostatistics connects their
expertise to a genetics or genomics research question.
The biostatistical contribution:
Connect genetic, molecular, clinical, computational, and population
perspectives so that genomic research produces evidence that is
statistically valid, biologically meaningful, reproducible, and
relevant across diverse populations.
From genomic data to evidence
What does the biostatistician contribute?
Biostatisticians contribute throughout a genetics or genomics study,
not only after the data have been generated. Select a stage to explore
how statistical reasoning turns a biological question into reliable
evidence.
Use what you already know
How prerequisites become genomic-research tools
The subjects required for graduate study become practical tools for
modeling inheritance, analyzing genomic data, and evaluating evidence
about disease risk and biological mechanisms.
One health question, several approaches
Explore a complex-disease genetics question
Researchers want to understand why some people develop a complex
disease while others with similar environmental exposures do not.
Genetic data may help—but the same question can require several
different statistical approaches.
Questions explored through statistical genetics
Explore statistical-genetics and genomics research
Which genetic variants are associated with disease?
Association methods help researchers scan the genome while
accounting for chance findings, ancestry, relatedness, and other
sources of bias.
Can many genetic variants be combined to estimate risk?
Polygenic models combine many small genetic effects and evaluate
whether risk estimates are accurate, calibrated, and useful
across populations.
How does genetic variation influence gene regulation?
Statistical methods connect DNA variation with gene expression
and other molecular measurements to investigate biological
mechanisms.
Can genetics help identify causal disease pathways?
Causal and integrative methods help distinguish variants or
pathways that may contribute to disease from signals that are
merely correlated.
How can genomic data be connected with health records?
EHR-linked biobanks allow researchers to study genetic
associations across diagnoses, laboratory values, medications,
and longitudinal outcomes.
Do genomic findings work equally well across populations?
Population-aware methods evaluate representation,
transportability, and whether genetic findings or risk scores
generalize beyond the groups in which they were developed.
An illustrative pathway
How coursework can build toward statistical genetics and genomics
This example shows how mathematical and statistical foundations can
lead toward more specialized study of genetic, genomic, and
molecular data. It is not a formal concentration or guaranteed
course sequence.
Your preparation
Calculus
Linear algebra
Introductory statistics
Programming experience
Core foundation
Probability
Statistical inference
Linear regression
Generalized linear models
Advanced methods
Statistical genetics
High-dimensional methods
Machine learning
Causal inference
Application
Complex-disease genetics
Genomic risk prediction
Multi-omics integration
Precision health
Choose your own direction
What interests you most?
Frequently asked questions
Preparing to study statistical genetics and genomics
Students do not necessarily need extensive prior genetics
coursework. Strong quantitative preparation is central, while
biological and genomic knowledge can develop through
coursework, seminars, collaboration, and applied study.
Programming is highly valuable because genomic datasets are
large and complex. Students may use R, Python, command-line
tools, and specialized software to manage data, fit models,
conduct simulations, visualize findings, and create
reproducible analytical workflows.
Bioinformatics often emphasizes the computational processing,
organization, annotation, and analysis of biological data.
Statistical genetics places particular emphasis on study
design, probability models, statistical inference,
uncertainty, association, prediction, population structure,
and generalizability. In practice, the fields overlap and
frequently collaborate.
No. Michigan offers a substantial genetics and genomics
research environment, but individual research placements,
assistantships, publications, and faculty-led projects are not
guaranteed for MS students.
The Biostatistics MS emphasizes broad statistical theory and
methodology with elective flexibility. Health Data Science
places additional prescribed emphasis on computing, machine
learning, and large health datasets. Students should compare
current curricula and discuss questions with the program.
What this page can—and cannot—show
This page can help prospective students understand statistical
genetics and genomics, connect prerequisite coursework to genomic
research, and explore an illustrative Michigan academic pathway.
It does not guarantee research placement, assistantship funding,
publication, a thesis experience, or access to a particular
faculty project.
Continue exploring Michigan Biostatistics
Compare the MS programs, review prerequisite preparation, explore
additional research areas, or attend an upcoming prospective-student
event.