GWAS & rare-variant genetics
Genome-wide association, rare-variant burden, and fine-mapping for neurodegenerative and psychiatric traits in research cohorts.
Research Area
Neuroscience & CNS Genomics
The brain is genomics on hard mode—inaccessible tissue, exquisite cell-type diversity, complex splicing, and disease risk spread across thousands of loci. We bring the computational methods that fit it: GWAS and rare variants, single-nucleus atlases, brain QTLs, and repeat expansions, for research.
GWAS & rare variants
Single-nucleus brain
eQTL & repeat expansions
Research-use · not clinical
Illustrative sample output
odd chr
even chr
significant locus
Overview
Making sense of the genome behind the brain
CNS genetics is uniquely hard. Risk for neurodegenerative and psychiatric conditions is highly polygenic, spread across thousands of common variants of small effect and a long tail of rare and de novo mutations. The tissue is inaccessible, so most work runs on single-nucleus data and post-mortem cohorts; splicing and repeat expansions play outsized roles; and the cell type a variant acts in often matters more than the variant itself.
We bring methods built for that reality. From GWAS, rare-variant, and repeat-expansion analysis to single-nucleus brain atlases, brain eQTL mapping, and colocalization that links loci to genes and cell types, we apply established, peer-reviewed approaches against the field's reference cohorts—and return results documented for reproducibility. Genetic-risk work, including polygenic scores, is framed as cohort research, not a clinical test or an individual prediction.
Applications
What we analyse in CNS genomics
The computational analyses that connect the genome to the brain—from GWAS and repeat expansions to single-nucleus atlases and QTLs.
Single-nucleus brain atlases
Neuronal and glial cell-type identification from snRNA-seq—the standard for inaccessible, post-mortem brain tissue.
Brain eQTL & colocalization
Expression and splicing QTL mapping in brain, and colocalization with GWAS to link loci to genes and cell types.
Repeat-expansion detection
Genotyping of short- and long-read tandem repeats and flagging of pathogenic-range expansions like C9orf72 and HTT.
De novo & neurodevelopmental
De novo variant calling from trios and rare-variant analysis for autism, epilepsy, and other neurodevelopmental research.
Splicing analysis
Differential splicing and aberrant-splicing detection—central to many CNS genes and disease mechanisms.
Brain epigenomics
DNA-methylation and chromatin analysis in brain to study regulation and epigenetic contributions to CNS disease.
Polygenic risk scores
Construction and evaluation of polygenic scores for cohort research—never an individual clinical prediction.
The Pipeline
A representative CNS-genomics workflow
A transparent, brain-aware sequence from raw data to interpreted association—each step suited to the data type and documented for reproducibility.
Adapted to your study: GWAS vs. rare-variant vs. expression, bulk vs. single-nucleus, short- vs. long-read. We confirm the plan with you before any compute begins.
Intake & QC
Raw reads, count matrices, or GWAS summary statistics are quality-checked—call rates, relatedness, ancestry, and batch structure.
Tools: PLINK · FastQC · MultiQC
Processing
Genotypes are imputed, reads aligned, or nuclei called and filtered for ambient signal and doublets.
Tools: BWA-MEM2 · Cell Ranger · imputation
Variant & expression analysis
Association, rare-variant burden, repeat-expansion genotyping, or expression quantification, depending on the question.
Tools: REGENIE · ExpansionHunter · Salmon
QTL & colocalization
Expression and splicing QTLs are mapped in brain and colocalized with association signals to implicate genes.
Tools: tensorQTL · coloc · SMR
Annotation & cell-type context
Signals are annotated and mapped to cell types and pathways, using single-nucleus references and enrichment tools.
Tools: FUMA · MAGMA · snRNA references
PRS & signatures
Where relevant, polygenic scores and research signatures are constructed and evaluated within the cohort.
Tools: PRSice · LDpred2 · PRS-CS
Integration & reporting
Results become Manhattan and QTL figures, cell-type maps, tables, and reproducible methods with every tool version.
Tools: ggplot2 · LocusZoom · versioned manifest
Tools & Technologies
Established, peer-reviewed tools—matched to your data
We select from the field's standard toolkit rather than forcing every dataset through one pipeline. A representative set of what we work with:
GWAS & PRS
PLINK
REGENIE
SAIGE
PRSice
LDSC
Fine-mapping & enrichment
FUMA
MAGMA
SuSiE
FINEMAP
QTL & colocalization
tensorQTL
FastQTL
coloc
SMR
Repeat expansions
ExpansionHunter
TRGT
STRique
GangSTR
Single-nucleus
Seurat
Scanpy
Harmony
celltypist
Rare & de novo variants
GATK
DeNovoGear
TADA
VEP
Splicing & epigenomics
LeafCutter
rMATS
SpliceAI
minfi
Visualization & databases
LocusZoom
ggplot2
GWAS Catalog
GTEx
Key Databases
The neuroscience resources we draw on
CNS analysis is powered by brain-specific reference cohorts and atlases. We build on the community's authoritative, versioned resources.
- PsychENCODE
- Functional-genomic reference for the human brain and disorders.
- GTEx (brain)
- Expression and eQTLs across brain regions and other tissues.
- BrainSpan
- Developmental transcriptome atlas of the human brain.
- ROSMAP / AMP-AD
- Aging and Alzheimer's cohorts with multi-omic brain data.
- Allen Brain Atlas
- Reference cell-type and spatial maps of the brain.
- GWAS Catalog
- Curated genome-wide association results across traits.
- gnomAD
- Population allele frequencies for rare-variant filtering.
- ClinVar
- Reported clinical significance of variants for context.
- DisGeNET
- Curated gene–disease associations for interpretation.
Choosing an Approach
GWAS vs. rare-variant vs. polygenic scores
Different genetic architectures need different approaches. A quick orientation; we will help you match it to your study.
| Dimension | GWAS | Rare-variant | Polygenic scores |
|---|---|---|---|
| Targets | Common variants | Rare & de novo variants | Aggregate genome-wide risk |
| Effect sizes | Small, additive | Larger, gene-level | Combined, distributional |
| Needs | Very large cohorts | Trios / deep sequencing | Training GWAS + target set |
| Output | Associated loci | Implicated genes | Per-sample research score |
| Best suited to | Common polygenic traits | Neurodevelopmental research | Stratification in research |
What You Receive
A complete, documented deliverable
Not just a results table dropped in a folder—a coherent picture linking genome to brain biology, documented so it reproduces.
- Association / rare-variant results with genome-wide summaries
- Repeat-expansion genotypes with pathogenic-range flags
- Brain eQTL/sQTL & colocalization linking loci to genes
- Single-nucleus cell-type maps & cell-type enrichment
- Polygenic-score distributions for cohort research, where applicable
- Manhattan, QTL & locus figures (publication-quality)
- Reproducible methods with every tool & reference version
Built for reproducibility, not just a result
CNS analysis follows documented best practices—careful QC, ancestry matching and population-structure control, proper multiple-testing correction, and reference cohorts and panel versions pinned—so an association or a colocalization is real signal, not confounding or a batch effect. Genetic-risk work, including polygenic scores, is framed honestly as cohort research: it is not a clinical test, an individual diagnosis, or a prediction of whether a person will develop a condition, and clinical interpretation stays with qualified professionals.
The practical payoff: your methods section writes itself, a reviewer can re-run the analysis, and a result from today can be reproduced a year from now. We will also tell you honestly when a design or sample size won't support the conclusion you're after.
FAQ
Neuroscience & CNS genomics questions
What neuroscience and CNS-genetics teams most often ask before starting.
Bulk and single-nucleus RNA-seq (snRNA-seq) from brain, whole-genome and whole-exome sequencing, long-read data for repeat expansions, methylation arrays, GWAS summary statistics, and spatial transcriptomics — from raw reads, matrices, or summary stats from your core or cohorts like PsychENCODE and GTEx.
Yes. Brain tissue is hard to dissociate, so single-nucleus RNA-seq is the standard — we process, cluster, and annotate neuronal and glial cell types, and can deconvolve bulk brain data against single-nucleus references.
Yes. Many neurological disorders are driven by tandem-repeat expansions (for example C9orf72 or HTT). We genotype short and long repeats from short- and long-read data and flag pathogenic-range expansions for review.
Yes. We map expression and splicing QTLs in brain and colocalize them with GWAS signals to connect associated loci to the genes and cell types they likely act through.
We build and evaluate polygenic risk scores for cohort research. These are research analyses of populations, not a clinical test or an individual diagnosis or prediction — clinical interpretation stays with qualified professionals.
Neuroscience is a research area we support through our core services — chiefly Genomics & Variant Analysis, Transcriptomics & Expression, and Biostatistics & Visualization — applied to CNS data. This page shows how they come together.
Related
Services that power neuroscience work
Have CNS data to make sense of?
Tell us your organism, data type, and question—we'll scope it honestly, including if a different design would serve you better.