RESEARCH DISCIPLINE
Statistical Analysis for Genetics & Genomics Research
Consulting for Association & Sequencing Studies
Genetics and genomics statistical analysis for genome-wide association, exome and genome sequencing, and family-based studies, with ancestry, relatedness and genotyping batch handled in the design and results prepared to your journal's standard.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
Statistical Analysis for Genetics & Genomics Research
Genetics and genomics statistical analysis is the statistical design, analysis and reporting of studies that relate inherited variation to a trait or disease, usually from genotyping arrays or exome and genome sequencing together with the phenotypes recorded for the same individuals. A single study tests hundreds of thousands to millions of variants, the participants may be related or drawn from structured populations, and cases and controls can be genotyped on different plates or in different runs, so a workflow that tests each variant at a nominal threshold without these controls reports associations that reflect chance, ancestry, kinship or batch rather than biology. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides genomic data analysis through in-house PhD-level statisticians, who settle the sample, ancestry, batch and multiple-testing plan with you before any association is tested.
WHEN TO ENGAGE
Statistical Support at Different Stages of Genetics & Genomics Research
The questions in a genetic study tend to surface at four moments: before genotyping, once variant calls arrive, during write-up and after review.
Planning an Association Study You are designing a case-control, cohort or family study and need the sample size, the phenotype definition and the spread of cases and controls across genotyping plates settled before samples are run.
Analyzing Genotype or Sequence Data Genotypes or variant calls are in hand, and the quality control, the control for ancestry and relatedness and the association models need to be chosen, run and checked.
Writing Up Genome-Wide Signals Signals have been found, and the multiple-testing threshold, the replication evidence and each effect estimate need to be written up and reported to the standard of your target journal.
Answering a Reviewer on Stratification A reviewer or advisor has questioned test-statistic inflation, hidden relatives, batch or the significance threshold, and the analysis needs a defensible response from the data you collected.
RESEARCH CONTEXT
Research Designs and Data We Work With in Genetics & Genomics
Genetic data arrive as genotype or variant tables linked to phenotypes, and the study design behind each decides how relatedness, ancestry and rare variants are handled.
01
Genome-Wide Association Studies
Case-control or quantitative-trait samples genotyped on arrays and imputed, where every variant is tested and the results must be calibrated across the genome.
02
Exome and Whole-Genome Sequencing
Variant calls from sequenced individuals, where most variants are rare and are usually tested in gene or region groups, not one at a time.
03
Candidate-Gene Studies
A short list of variants chosen for prior evidence and tested in one sample, with the hypothesis fixed beforehand and replication in a second sample.
04
Family-Based Studies
Pedigrees, sibling pairs and parent-offspring trios, where individuals share genotypes by descent and the relatedness has to be modeled directly.
05
Polygenic Score Studies
Scores built from published effect sizes and tested in a target sample that must be separate from the discovery sample used to estimate them.
06
Mixed and Admixed Ancestry Cohorts
Participants whose allele frequencies and trait distributions differ by ancestry, so ancestry has to be estimated and modeled, not assumed uniform.
METHODOLOGICAL CONSIDERATIONS
Statistical Issues We Consider in Genetics & Genomics Research
These are the checks made before any signal is interpreted, because ancestry, relatedness and batch can create a convincing association that has nothing to do with the trait.
Population Stratification Allele frequencies and trait values both differ by ancestry, which creates false associations. Principal components or a mixed model are used, and inflation of the test statistics is examined.
Relatedness and Duplicates Relatives share alleles, so kinship is estimated from the genotypes, hidden relatives and duplicated samples are found, and a mixed model or a pruned sample is used for the tests.
Threshold for Multiple Testing Testing millions of variants at a nominal level yields many false positives, so a genome-wide threshold or false discovery rate control is chosen in advance and justified by the independent tests.
Plate, Array and Sample Swaps When cases and controls sit on different plates or runs, status is confounded with batch. Allocation across batches is checked, and sex or relatedness mismatches reveal swapped samples.
Rare Variants and Power A single-variant test has little power for a rare variant, so variants are collapsed into prespecified gene or region sets, and the power the sample gives is stated before results are read.
Replication and Overestimated Effects Effect sizes of the first signals are usually too large, so the replication sample and its interval are planned, and discovery estimates are not reported as the final effect.
STATISTICAL METHODS
Methods Selected for the Research Question and Study Design
Each method family is chosen for the trait, the relatedness and the number of variants in your study, and the line under each title explains why it is used with genetic data.
Regression & Predictive Modeling
Association with a binary or quantitative trait is tested with logistic or linear models adjusted for ancestry components, and sparse genotype classes call for penalized estimation.
Longitudinal & Multilevel Analysis
Relatives and repeated trait measurements are correlated, which mixed models represent with a family or kinship term, and generalized estimating equations with a working correlation.
Comparative & Hypothesis Testing
Genotype counts in small candidate-gene studies and Hardy-Weinberg checks use chi-square and exact tests, with the allelic contrast stated and an odds ratio with its interval.
Clinical, Survival & Epidemiological
Genotype and disease risk are expressed as odds or hazard ratios, and age at onset is analyzed as a time-to-event outcome with censoring, not dichotomized at an arbitrary age.
Bayesian & Computational Methods
Missing phenotypes and covariates are handled by multiple imputation, and bootstrap intervals describe derived quantities, such as a polygenic score's explained variance.
Multivariate, Scale & Reliability
Principal components summarize ancestry and hidden technical structure for use as covariates, and clustering shows whether samples separate by batch or by ancestry before any variant is tested.
DELIVERABLES
Statistical Outputs Prepared for Your Research
The outputs follow the structure of a genetic study, from the review of the sample manifest to the scripts that regenerate every association result.
Data & Study Review The sample manifest, phenotype definitions, genotyping or sequencing batches, call-rate summaries and relatedness structure are reviewed before any association is tested.
Statistical Analysis Quality control, the agreed association models with ancestry and relatedness controlled, diagnostics for inflation of the test statistics and the planned sensitivity analyses.
Effect Estimates & Uncertainty Odds ratios or per-allele effects with confidence intervals, test statistics against the chosen threshold, and the variance explained by a polygenic score where planned.
Tables & Figures Sample and quality-control tables with counts at each filter, Manhattan and quantile-quantile plots, regional association plots and replication tables.
Statistical Interpretation Each signal is described as an association with a variant or region, with sample, ancestry and replication status stated, and claims about gene function left to functional evidence.
Analysis Files R or Python scripts, SAS programs or Stata do-files, with quality-control thresholds and software versions recorded so that every result regenerates from the genotype data.
REPORTING
Reporting Genetics & Genomics Research
Genetic association, polygenic score and sequencing studies each have their own reporting items, and a manuscript cites only the ones its design calls for.
Genetic association studies report to STROBE with its STREGA extension, which adds the genotyping methods and error rates, the handling of population stratification and the Hardy-Weinberg checks. Polygenic scores are described to the PRS Reporting Standards (PRS-RS), and a score combined with other predictors into a model for an individual outcome follows TRIPOD+AI. A quality-control table gives the samples and variants removed at each filter, the genome-wide or false discovery threshold is stated before the results, and each lead variant is reported with its effect size, confidence interval and replication result. The data-sharing conditions of the journal, the funder and the consent under which samples were collected are checked before summary statistics are released.
ILLUSTRATIVE EXAMPLE
How the Analysis May Look in Practice
An illustration of how a case-control genotype dataset with a batch problem is taken from quality control to a replicated association estimate.
ILLUSTRATIVE EXAMPLE · ADULT-ONSET HEARING LOSS GENETICS RESEARCH · UNITED STATES
A hearing research program at an academic medical center wanted to know whether common variants were associated with adult-onset hearing loss in a case-control sample genotyped on an array.
Most cases had been genotyped in an earlier run than the controls, participants came from two ancestry groups, and a few were relatives. Before any association was tested, we fixed the plan: quality control within each batch, kinship estimation to remove duplicates and handle relatives, ancestry components as covariates, a mixed model for the tests, and a genome-wide threshold set in advance. Because batch and case status largely coincided, call rates and allele frequencies were compared between runs within cases and within controls, variants with run-dependent missingness were excluded, and the report stated that batch confounding could not be fully ruled out. A second cohort was reserved for replication, and the effect estimates from the first sample were treated as provisional, not final.
Outcome: The program received odds ratios with 95% confidence intervals, Manhattan and quantile-quantile plots, STROBE and STREGA flow counts, a methods paragraph and the scripts.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
SOFTWARE
Statistical Software for Genetics & Genomics Research
Genetic analyses are run in the software your team already uses, with syntax or scripts provided where applicable.
Statistical Support Within Your Research OnlineSPSS provides the agreed methodological, analytical, and statistical reporting support. The researcher or research team retains the scientific question, substantive interpretation, conclusions, and overall responsibility for the research.
FREQUENTLY ASKED QUESTIONS
Questions About Statistical Analysis for Genetics & Genomics
Short answers to what geneticists ask before sharing a study, from method choice and stratification to the significance threshold, review and confidentiality.
Which statistical methods suit a genetic association study?
Genetic association statistics usually rest on logistic or linear regression for the trait, a mixed model where participants are related, and survival models when the outcome is age at onset, chosen by the trait and the relatedness in your sample. In genetics biostatistics, the same plan also covers Hardy-Weinberg checks and the handling of rare variants. Our Statistical Methods pages show how each family is applied.
How do you deal with ancestry and related participants?
Ancestry is estimated from the genotypes and entered as principal components or handled by a mixed model, and kinship is estimated so that relatives and duplicates are identified. The inflation of the test statistics is then examined to show whether stratification was controlled. Where it was not, the model or the sample is revised before results are reported.
What significance threshold should a genome-wide analysis use?
The threshold follows the number of independent tests in your design, not a fixed convention. A conventional genome-wide threshold applies to common-variant array studies, while sequencing studies of rare variants and studies of other ancestries need thresholds matched to their own tests. False discovery rate control suits discovery lists where some false positives are tolerable. The correction for multiple tests is planned before results are seen.
Can you review a genetic analysis that has already been run?
Yes. We can review quality control, the handling of ancestry, relatedness and batch, the threshold and the replication design, and state what stands and what should change. The review can be done before submission or after reviewer comments, and the findings come with the reasoning behind them.
How is confidential genetic research data handled?
Genotype files, phenotype data and analysis files are handled confidentially, and project information is not published. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Discuss Your
Genetics & Genomics Research With Us
Share your research question, study design and available data, even if only a sample manifest or a protocol exists. We review how your samples were collected and genotyped and confirm the approach before any work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014