Biology subjects

Banasik, K.

Publications and source records attributed to Banasik, K..

3 recordsLinked to original sources

bioRxiv2026

Menstrual cycle phase length variation is associated with daily symptom burden

Menstrual symptoms vary across the cycle, yet most research assumes a normative 28-day cycle with fixed phase durations, obscuring the physiological relevance of natural cycle variation. Using the mcPHASES dataset, we characterised cycle and phase length variation across 96 menstrual cycles from 37 participants, with ovulation timing estimated from daily urinary luteinizing hormone measurements using a Bayesian hierarchical model, and examined associations with daily symptoms in a subset of 64 cycles from 35 participants with complete symptom data. Twelve physical, mental, and behavioural symptom domains were modelled using Bayesian ordinal regression, with posterior uncertainty in phase-length predictors propagated via a measurement error framework. Total cycle length was not associated with daily symptom burden, except sleep disturbances. By contrast, phase length decomposition revealed systematic associations across multiple domains: longer menstrual phase length was broadly associated with greater symptom intensity spanning physical, gastrointestinal, affective, and sleep domains; longer luteal phase duration was associated with greater fatigue and more frequent headaches, but lower sore breast intensity and lower stress; and longer follicular phase duration and later ovulation were each associated with greater sore breast intensity and more frequent mood swings. These associations require knowledge of actual ovulation timing and cannot be recovered from cycle length alone, indicating that the common assumption of a fixed 14-day luteal phase introduces systematic misclassification of hormonal exposure. Daily symptom intensity was also predominantly person-specific, with cycle phase explaining little of the between-person variance across most symptoms. These findings indicate that calendar-based phase assignment is insufficient for research and clinical assessment of hormone-sensitive conditions, and that person-specific baselines, rather than population-level phase averages, are needed for clinically meaningful symptom monitoring.

physiology↗

bioRxiv2025

Population-scale analysis of inheritance patterns across 858,635 individuals reveals recent historical migration patterns across the North Sea from the Middle Ages to the Industrial Revolution

The North Seas historical migrations have impacted the genetic structure of its neighbouring populations. We analysed haplotype sharing among 858,635 modern individuals from Denmark and Britain to infer migration history from the Middle Ages to the modern day. We estimated the genetic relationship among 370,259 Danes using Danish healthcare registries and validated those with retrievable pedigree relationships from a national family registry. We also compared their haplotype sharing with 488,376 British individuals from the UK Biobank. Our analysis revealed the fine-grained population genetic history of Denmark and identified distinct coastal and island communities with a history of genetic isolation and bottlenecks. We observed a significant population decline in Jutland compared to Zealand from the late medieval period to the start of early modern period, accompanied by migration from Jutland to Zealand, corresponding to historical evidence. We identified two major haplotype sharing patterns between Denmark and Great Britain: (1) early coast-to-coast connections between South Jutland and eastern England, likely driven by migration, settlement, continuous trade and the movement of people across the North Sea from the Middle Ages through early modern times, and (2) later city-to-city connections such as those between London and Kobenhavn, likely shaped by the reorientation of economic and trading centres in Britain and Denmark, and the increased shipping from early modern times towards the Industrial Revolution. Further comparisons using other North Sea countries showed both shared and unique histories of genetic exchange with Denmark and Britain. Our study provides novel genetic evidence of migration across the North Sea from the Middle Ages to the Industrial Revolution. It highlights the power of nationwide biobanks in reconstructing fine-scale historical population movements among closely related populations.

genetics↗

bioRxiv2018

Large-scale genome-wide association meta-analysis of endometriosis reveals 13 novel loci and genetically-associated comorbidity with other pain conditions

Endometriosis is a common complex inflammatory condition characterised by the presence of endometrium-like tissue outside the uterus, mainly in the pelvic area. It is associated with chronic pelvic pain and infertility, and its pathogenesis remains poorly understood. The disease is typically classified according to the revised American Fertility Society (rAFS) 4-stage surgical assessment system, although stage does not correlate well with symptomatology or prognosis. Previously identified genetic variants mainly are associated with stage III/IV disease, highlighting the need for further phenotype-stratified analysis that requires larger datasets. We conducted a meta-analysis of 15 genome-wide association studies (GWAS) and a replication analysis, including 58,115 cases and 733,480 controls in total, and sub-phenotype analyses of stage I/II, stage III/IV and infertility-associated endometriosis cases. This revealed 27 genetic loci associated with endometriosis at the genome-wide p-value threshold (P<5x10-8), 13 of which are novel and an additional 8 novel genes identified from gene-based association analyses. Of the 27 loci, 21 (78%) had greater effect sizes in stage III/IV disease compared to stage I/II, 1 (4%) had greater effect size in stage I/II compared to stage III/IV and 17 (63%) had greater effect sizes when restricted to infertility-associated endometriosis cases compared to overall endometriosis. These results suggest that specific variants may confer risk for different sub-types of endometriosis through distinct pathways. Analyses of genetic variants underlying different pain symptoms reported in the UK Biobank showed that 7/9 had positive significant (p<1.28x103) positive genetic correlations with endometriosis, suggesting a genetic basis for sensitivity to pain in general. Additional conditions with significant positive genetic correlations with endometriosis included uterine fibroids, excessive and irregular menstrual bleeding, osteoarthritis, diabetes as well as menstrual cycle length and age at menarche. These results provide a basis for fine-mapping of the causal variants at these 27 loci, and for functional follow-up to understand their contribution to endometriosis and its potential subtypes.

genomics↗