Large biological datasets do not become evidence by scale alone.
Statistical genomics depends on the chain between study design, cohort structure, population genetics, molecular data, disease biology, uncertainty, and reproducible inference. My work builds that chain across WGS statistics, rare variant analysis, multi-omics, clinical cohorts, and population-scale genomic data.
Genetic association studies need more than variant filtering. Cohort structure, ancestry, phenotype definition, missingness, and test design determine whether the result can be trusted.
Rare variant analyses need pathway, gene, variant, and burden logic that remains interpretable when events are sparse and uncertainty is large.
DNA, RNA-seq, proteomics, metabolomics, and clinical data need a statistical layer that connects molecular signal to disease biology and patient stratification.
Population evidence becomes more valuable when analyses are reproducible, versioned, visualised clearly, and reusable beyond the first manuscript or internal report.
Archipelago, variant set association visualisation for complex genomic studies.
QuantCalc. Probabilistic genomic interpretation using priors, observed evidence, and Bayesian inference.
GWAS, rare variant association testing, gene-level analysis, pathway analysis, burden tests, population stratification, ancestry inference, and cohort QC.
Regression, multivariate analysis, Bayesian inference, uncertainty quantification, Monte Carlo methods, resampling, high-dimensional modelling, and interpretable visualisation.
Whole-genome sequencing, RNA-seq, proteomics, metabolomics, phenotype-linked molecular data, EHR-linked workflows, and patient-level molecular interpretation.
R, Python, Bash, Git, Linux, Unix, high-performance computing, AWS, Azure, Docker, Singularity or Apptainer, Nextflow, Snakemake, and reproducible analytical frameworks.
Manhattan plots, regional association plots, variant set visualisation, structured reports, machine-readable outputs, cohort summaries, and decision-ready statistical figures.
Lead-author manuscripts, analytical reports, cross-functional presentations, methods documentation, multidisciplinary collaboration, and supervision in genomics and bioinformatics.
Archipelago: variant set association visualisation for complex genomic studies, published in Lawless et al (2026).
QuantBayes: standardises how evidence strength is expressed - quantifying genomic variant evidence sufficiency with Bayesian posterior intervals, preprinted in Quant Group et al (2025) .
Qualifying variant database: reusable YAML criteria for reproducible genomic variant interpretation, published in Lawless et al (2026).
ORCID record:
0000-0001-8496-3725
Archipelago method for variant set association test statistics. Genetic Epidemiology, 50(1), e70025, 2026.
Predicting the occurrence of variants in RAG1 and RAG2. Journal of Clinical Immunology, 39(7), 688-701, 2019.
Viral genetic determinants of prolonged respiratory syncytial virus infection among infants in a healthy term birth cohort. The Journal of Infectious Diseases, 227(10), 1194-1202, 2022.
Prevalence and clinical challenges among adult primary immunodeficiency patients with RAG deficiency. Journal of Allergy and Clinical Immunology, (), , 2018.
Lead computational and translational analyses within the Swiss Pediatric Sepsis Study and SwissPedHealth National Data Stream, funded at approximately CHF 1M and CHF 5M across three hospitals.
WGS, RNA-seq, proteomics, metabolomics, clinical phenotypes, EHR-linked data, approximately 1,000 children, more than 100 TB of biomedical data, rare variant and gene-level analysis.
Developed statistical genetics, computational biology, and multi-omic workflows across sepsis, tuberculosis, asthma, infectious disease, and chronic inflammatory disease studies.
Cohorts up to 5,000 participants, more than CHF 3M in competitive research support, population statistical genetics, rare variant testing, Bayesian reasoning, reproducible computational workflows.
Led genomic discovery and clinical interpretation in rare immune disease within a translational hospital setting.
Approximately 500 severe clinical cases, genome sequencing, molecular biology, immune-mediated disease, treatment-related findings, and disease mechanism interpretation.
Statistical genomics teams need analyses that support replication, review, and reuse. The useful output is not only a significant result, but a defensible chain from cohort definition to interpretable evidence.
