Biology subjects

Palta, P.

Publications and source records attributed to Palta, P..

14 recordsLinked to original sources

bioRxiv2026

Circadian rhythms regulate refractive development across species

Myopia is a rapidly escalating global public health challenge, yet the biological mechanisms linking modern lifestyles to abnormal eye growth remain unclear. Circadian rhythms have been implicated in refractive development, but causal evidence is limited. Here, we integrate population-scale human data with an experimental animal model to determine whether circadian misalignment contributes to myopia. In >265,000 individuals from the Estonian and UK Biobanks, late chronotype was consistently associated with myopia. To assess causality, we experimentally disrupted the alignment between behavioural and environmental rhythms in mice by housing them in non-24-hour light-dark schedules. Exposure to a lengthened cycle (T26) induced a myopic shift that was, notably, reversible in early adulthood. Retinal transcriptomics revealed enrichment of mitochondrial and hypoxia-related plasticity pathways, with transcriptional changes distributed across multiple retinal cell classes. Together, these findings identify circadian misalignment as a conserved and modifiable driver of myopia, highlighting opportunities for novel preventive and therapeutic approaches.

neuroscience↗

bioRxiv2026

Local clocks within human tissues reveal widespread 24-hour rhythms in gene expression

Circadian clocks govern the 24-hour rhythmic activity of organs across the human body and these nycthemeral rhythms are critical for human physical and mental health. Here, we analysed 14,886 samples across 45 human tissues and inferred tissue-specific local phases using CHIRAL to define the rhythmic transcriptome. Local phase inference identified a median of 5,747 rhythmic transcripts across tissues, including 5,531 across brain tissues, indicating that prior donor-level approaches have substantially underestimated the extent of rhythmic transcription in the human brain. In the brain, rhythmic genes were enriched for neurotransmitter pathways, the synaptic vesicle cycle, circadian entrainment and major neurodegenerative disorders. Strikingly, more than 200 genes in Alzheimers, Parkinsons, Huntingtons and prion disease pathways displayed significant 24-hour rhythmicity across cortical, striatal and hippocampal regions, segregating into distinct diurnal and nocturnal phase clusters. These included Parkinsons disease genes SNCA and PRKN, Alzheimers disease genes PSEN1 and APP, prion disease genes PRNP and EIF2AK3 (PERK), and Huntingtons disease genes HTT and GPR52. Among these disease-associated rhythmic transcripts were several targets already under therapeutic investigation for which administration at specific times of day may improve therapeutic efficacy or tolerability. Here, we observe that human tissues, including the brain, exhibit distinct local rhythmic organisation. These findings provide novel insights into disease mechanisms and highlight opportunities for target discovery, drug development and chronotherapeutic intervention.

molecular biology↗

bioRxiv2024

Genetic variants affect diurnal glucose levels throughout the day

Circadian rhythms not only coordinate the timing of wake and sleep but also regulate homeostasis within the body, including glucose metabolism. However, the genetic variants that contribute to temporal control of glucose levels have not been previously examined. Using data from 420,000 individuals from the UK Biobank and replicating our findings in 100,000 individuals from the Estonian Biobank, we show that diurnal serum glucose is under genetic control. We discover a robust temporal association of glucose levels at the Melatonin receptor 1B (MTNR1B) (rs10830963, P = 1e-22) and a canonical circadian pacemaker gene Cryptochrome 2 (CRY2) loci (rs12419690, P = 1e-16). Furthermore, we show that sleep modulates serum glucose levels and the genetic variants have a separate mechanism of diurnal control. Finally, we show that these variants independently modulate risk of type 2 diabetes. Our findings, together with earlier genetic and epidemiological evidence, show a clear connection between sleep and metabolism and highlight variation at MTNR1B and CRY2 as temporal regulators for glucose levels.

genetics↗

bioRxiv2021

Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples

MotivationNon-invasive prenatal testing (NIPT) is a powerful screening method for fetal aneuploidy detection, relying on laboratory and computational analysis of cell-free DNA. Although several published computational NIPT analysis tools are available, no comprehensive and direct accuracy comparison of these tools is published. Here, we evaluate and determine the precision of five commonly used computational NIPT aneuploidy analysis tools, considering diverse sequencing depth (coverage) and fetal DNA fraction (FF) on clinically validated NIPT samples. MethodsWe evaluated computational NIPT aneuploidy analysis tools WisecondorX, NIPTeR, NIPTmer, RAPIDR, and GIPseq, on the same set of clinically validated samples, subsampled to different sequencing coverages between 1.25-20M reads per sample (RPS). These clinically validated samples consisted of 423 samples, including 19 samples with fetal chromosome 21 trisomy (T21, Down syndrome), eight trisomy 18 (T18, Edwards syndrome) and three trisomy 13 (T13, Patau syndrome) samples. For each software and sequencing coverage, we determined the number of false-negative and false-positive trisomy/euploidy calls. For a uniform trisomy detection interpretation, we defined a framework based on the percent-point function for determining the cut-off threshold for calling aneuploidy based on the sample Z-score and the reference group Z-score distribution. We also determined the effect of the naturally occurring arbitrary read placement driven uncertainty on T21 detection at very low sequencing coverage and the effect of cell-free fetal DNA fraction (FF) on the accuracy of these computational tools in the case of various sequencing coverages. ResultsThis is the first head-to-head comparison of NIPT aneuploidy detection tools for the low-coverage whole-genome sequencing approach. We determined that, with the currently available software tools, the minimum sequencing coverage with no false-negative trisomic cases was 5M RPS. Secondly, for these compared tools, the number of false-negative trisomic cases could be reduced if the trisomy call cut-off threshold considers the Z-score distribution of euploid reference samples. Thirdly, we observed that in the case of low FF, both aneuploidy Z-score and FF inference was considerably less accurate, especially in NIPT assays with 5M RPS or lower coverage. ConclusionsWe determined that all compared computational NIPT tools were affected by lower sequencing depth, resulting in systematically increasing the proportions of false-negative trisomy results as the sequencing depth decreased. Trisomy detection for lower coverage NIPT samples (e.g. 2.5M RPS) is technically possible but can increase the proportion of false-positive and false-negative trisomic cases, especially in the case of low FF.

bioinformatics↗

bioRxiv2020

Genetic analysis of obstructive sleep apnoea discovers a strong association with cardiometabolic health

There is currently only limited understanding of the genetic aetiology of obstructive sleep apnoea (OSA). The aim of our study is to identify genetic loci associated with OSA risk and to test if OSA and its comorbidities share a common genetic background. We conducted the first large-scale genome-wide association study of OSA using FinnGen Study (217,955 individuals) with 16,761 OSA patients identified using nationwide health registries. We estimated 8.3% [0.06-0.11] heritability and identified five loci associated with OSA (P < 5.0 x 10-8): rs4837016 near GTPase activating protein and VPS9 domains 1 (GAPVD1), rs10928560 near C-X-C motif chemokine receptor 4 (CXCR4), rs185932673 near Calcium/calmodulin-dependent protein kinase ID (CAMK1D) and rs9937053 near Fat mass and obesity-associated protein (FTO) - a variant previously associated with body mass index (BMI). In a BMI-adjusted analysis, an association was observed for rs10507084 near Rhabdomyosarcoma 2 associated transcript (RMST)/NEDD1 gamma-tubulin ring complex targeting factor (NEDD1). We found genetic correlations between OSA and BMI (rg=0.72 [0.62-0.83]) and with comorbidities including hypertension, type 2 diabetes (T2D), coronary heart disease (CHD), stroke, depression, hypothyroidism, asthma and inflammatory rheumatic diseases (IRD) (rg > 0.30). Polygenic risk score (PRS) for BMI showed 1.98-fold increased OSA risk between the highest and the lowest quintile and Mendelian randomization supported a causal relationship between BMI and OSA. Our findings support the causal link between obesity and OSA and joint genetic basis between OSA and comorbidities.

genetics↗

bioRxiv2020

High-resolution population-specific recombination rates and their effect on phasing and genotype imputation

Founder population size, demographic changes (eg. population bottlenecks or rapid expansion) can lead to variation in recombination rates across different populations. Previous research has shown that using population-specific reference panels has a significant effect on downstream population genomic analysis like haplotype phasing, genotype imputation and association, especially in the context of population isolates. Here, we developed a high-resolution recombination rate mapping at 10kb and 50kb scale using high-coverage (20-30x) whole-genome sequenced 55 family trios from Finland and compared it to recombination rates of non-Finnish Europeans (NFE). We tested the downstream effects of the population-specific recombination rates in statistical phasing and genotype imputation in Finns as compared to the same analyses performed by using the NFE-based recombination rates. We found that Finnish recombination rates have a moderately high correlation (Spearmans {rho} =0.67-0.79) with non-Finnish Europeans, although on average (across all autosomal chromosomes), Finnish rates (2.268{+/-}0.4209 cM/Mb) are 12-14% lower than NFE (2.641{+/-}0.5032 cM/Mb). Finnish recombination map was found to have no significant effect in haplotype phasing accuracy (switch error rates ~ 2%) and average imputation concordance rates (97-98% for common, 92-96% for low frequency and 78-90% for rare variants). Our results suggest that downstream population genomic analyses like haplotype phasing and genotype imputation mostly depend on population-specific contexts like appropriate reference panels and their sample size, but not on population-specific recombination maps or effective population sizes. Currently, available HapMap recombination maps seem robust for population-specific phasing and imputation pipelines, even in the context of relatively isolated populations like Finland.

genomics↗

bioRxiv2019

An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease

Multivariate methods are known to increase the statistical power of association detection, but they have lacked essential follow-up analysis tools necessary for understanding the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses. We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 putative causal variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p < 1x10-4). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the putative causal variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics.

genetics↗

bioRxiv2019

Polygenic Hyperlipidemias and Coronary Artery Disease Risk

BackgroundHyperlipidemia is a highly heritable risk factor for coronary artery disease (CAD). Monogenic familial hypercholesterolemia associates with higher increase in CAD risk than expected from a single LDL-C measurement, likely due to lifelong cumulative exposure to high LDL-C. It remains unclear to what extent a high polygenic load of LDL-C or TG-increasing variants associates with increased CAD risk.\n\nMethods and ResultsWe derived polygenic risk scores (PRS) with [~]6M variants for LDL-C and TG with weights from a UK biobank-based genome-wide association study with [~]500K samples. We evaluated the impact of polygenic hypercholesterolemia and hypertriglyceridemia to lipid levels in 27 039 individuals from the FINRISK cohort, and to CAD risk in 135 300 individuals (13 695 CAD cases) from the FinnGen project.\n\nIn FINRISK, LDL-C ranged from 2.83 (95% CI 2.79-2.89) to 3.80 (3.72-3.88) and TG from 0.99 (0.95-1.01) to 1.52 (1.48-1.58) mmol/l between the lowest and highest 5% of the respective PRS distributions. The corresponding CAD prevalences ranged from 8.2% to 12.7% for the LDL-C PRS and from 8.2% to 12.1% for the TG PRS in FinnGen. Furthermore, CAD risk was 1.36-fold higher (OR, 95% CI 1.24-1.49) for the LDL-C PRS and 1.31-fold higher (1.20-1.44) for the TG PRS for those with the PRS >95th percentile vs those without. These estimates were only slightly attenuated when adjusting for a CAD PRS (OR 1.26 [95% CI 1.15-1.39] for LDL-C and 1.21 [1.10-1.32] for TG PRS).\n\nConclusionsThe CAD risk associated with a high polygenic load for lipid-increasing variants was proportional to their impact on lipid levels and mostly independent of a CAD PRS. In contrast with a PRS for CAD, the lipid PRSs point to known and directly modifiable risk factors providing more direct guidance for clinical translation.

genomics↗

bioRxiv2019

Polygenic and clinical risk scores and their impact on age at onset of cardiometabolic diseases and common cancers

BackgroundPolygenic risk scores (PRS) have shown promise in predicting susceptibility to common diseases. However, the extent to which PRS and clinical risk factors act jointly and identify high-risk individuals for early onset of disease is unknown.\n\nMethodsWe used large-scale biobank data (the FinnGen study; n=135,300), with up to 46 years of prospective follow-up, and the FINRISK study with standardized clinical risk factor measurements to build genome-wide PRSs with >6M variants for coronary heart disease (CHD), type 2 diabetes (T2D), atrial fibrillation (AF), and breast and prostate cancer. We evaluated their associations with first disease events, age at disease onset, and impact together with routinely used clinical risk scores for predicting future disease.\n\nResultsCompared to the 20-80th percentiles, a PRS in the top 2.5% translated into hazard ratios (HRs) for incident disease ranging from 2.03 to 4.28 (p-values 1.96x10-59 to <1.00x10-100) and the bottom 2.5% into HRs ranging from 0.20 to 0.61. The estimated difference in age at disease onset between top and bottom 2.5% of PRSs was 6 to 13 years. Among early-onset cases, 21.3-32.9% had a PRS in the highest decile and in CHD and AF.\n\nConclusionsThe properties of PRS were similar in all five diseases. PRS identified a considerable proportion early-onset cases, and for all ages the performance of PRS was comparable to established clinical risk scores. These findings warrant further clinical studies on application of polygenic risk information for stratified screening or for guiding lifestyle and preventive medical interventions.

genomics↗

bioRxiv2019

Advantages of genotype imputation with ethnically matched reference panel for rare variant association analyses

Genotype imputation has become a standard procedure prior genome-wide association studies (GWASs). For common and low-frequency variants, genotype imputation can be performed sufficiently accurately with publicly available and ethnically heterogeneous reference datasets like 1000 Genomes Project (1000G) and Haplotype Reference Consortium panels. However, the imputation of rare variants has been shown to be significantly more accurate when ethnically matched reference panel is used. Even more, greater genetic similarity between reference panel and target samples facilitates the detection of rare (or even population-specific) causal variants. Notwithstanding, the genome-wide downstream consequences and differences of using ethnically mixed and matched reference panels have not been yet comprehensively explored.\n\nWe determined and quantified these differences by performing several comparative evaluations of the discovery-driven analysis scenarios. A variant-wise GWAS was performed on seven complex diseases and body mass index by using genome-wide genotype data of [~]37,000 Estonians imputed with ethnically mixed 1000G and ethnically matched imputation reference panels. Although several previously reported common (minor allele frequency; MAF > 5%) variant associations were replicated in both resulting imputed datasets, no major differences were observed among the genome-wide significant findings or in the fine-mapping effort. In the analysis of rare (MAF < 1%) coding variants, 46 significantly associated genes were identified in the ethnically matched imputed data as compared to four genes in the 1000G panel based imputed data. All resulting genes were consequently studied in the UK Biobank data.\n\nThese associations provide a solid example of how rare variants can be efficiently analysed to discover novel, potentially functional genetic variants in relevant phenotypes. Furthermore, our work serves as proof of a cost-efficient study design, demonstrating that the usage of ethnically matched imputation reference panels can enable substantially improved imputation of rare variants, facilitating novel high-confidence findings in rare variant GWAS scans.\n\nAuthor summaryOver the last decade, genome-wide association studies (GWASs) have been widely used for detecting genetic biomarkers in a wide range of traits. Typically, GWASs are carried out using chip-based genotyping data, which are then combined with a more densely genotyped reference panel to infer untyped genetic variants in chip-typed individuals. The latter method is called genotype imputation and its accuracy depends on multiple factors. Publicly available and ethnically heterogeneous imputation reference panels (IRPs) such as 1000 Genomes Project (1000G) are sufficiently accurate for imputation of common and low-frequency variants, but custom ethnically matched IRPs outperform these in case of rare variants. In this work, we systematically compare downstream association analysis effects on eight complex traits in [~]37,000 Estonians imputed with ethnically mixed and ethnically matched IRPs. We do not observe major differences in the single variant analysis, where both imputed datasets replicate previously reported significant loci. But in the gene-based analysis of rare protein-coding variants we show that ethnically matched panel clearly outperforms 1000G panel based imputation, providing 10-fold increase in significant gene-trait associations. Our study demonstrates empirically that imputed data based on ethnically matched panel is very promising for rare variant analysis - it captures more population-specific variants and makes it possible to efficiently identify novel findings.

genomics↗

bioRxiv2018

Computational framework for targeted high-coverage sequencing based NIPT

Non-invasive prenatal testing (NIPT) enables accurate detection of fetal chromosomal trisomies. The majority of existing computational methods for sequencing-based NIPT analyses rely on low-coverage whole-genome sequencing (WGS) data and are not applicable for targeted high-coverage sequencing data from cell-free DNA samples.\n\nHere, we present a novel computational framework for a targeted high-coverage sequencing based NIPT analysis. The developed methods use a hidden Markov model (HMM)-based approach in conjunction with supplemental machine learning methods, such as decision tree (DT) and support vector machine (SVM), to detect fetal trisomy and parental origin of additional fetal chromosomes. These methods were tested with simulated datasets covering a wide range of biologically relevant scenarios with various chromosomal quantities, parental origins of extra chromosomes, fetal DNA fractions and sequencing read depths. Consequently, we determined the functional feasibility and limitations of each proposed approach and demonstrated that read count-based HMM achieved the best overall classification accuracy of 0.89 for detecting fetal euploidies and trisomies. Furthermore, we show that by using the DT and SVM methods on the HMM state classification results, it was possible to increase the final trisomy classification accuracy to 0.98 and 0.99, respectively.\n\nWe demonstrated that read count and allelic ratio-based models can achieve a high accuracy (up to 0.98) for detecting fetal trisomy even if the fetal fraction is as low as 2%. Currently existing methods require at least 4% fetal fraction, which can be an issue in the case of early gestational age (<10 weeks) or elevated maternal body mass index (>35 kg/m2). More accurate detection can be achieved at higher sequencing depth using HMM in conjunction with supplemental methods, which significantly improve the trisomy detection especially in borderline scenarios (e.g., very low fetal fraction) and can enable to perform NIPT even earlier than 10 weeks of pregnancy.

genomics↗

bioRxiv2018

Creating basis for introducing NIPT in the Estonian public health setting

ObjectiveThe study aimed to validate a whole-genome sequencing-based NIPT method and our newly developed NIPTmer analysis software with the potential to integrate the pipeline into prenatal clinical care in Estonia.\n\nMethodIn total, 447 maternal blood samples were included to the study. Analysis pipeline involved whole-genome library preparation and massively parallel sequencing on Illumina NextSeq 500. Aneuploidy status was determined with NIPTmer software, which is based on counting pre-defined per-chromosome sets of unique k-mers from raw sequencing data. To estimate fetal fraction (FF) from total cell-free DNA SeqFF was implemented.\n\nResultsNIPTmer software allowed to identify correctly all samples of non-mosaic T21 (15/15), T18 (9/9) and T13 (4/4) cases. However, one mosaic T18 remained undetected. Six false positive results were observed, including three for T18 (specificity 99.3%) and three for T13 (specificity 99.3%). FF < 4% (2.8-3.99%) was estimated in eight samples, including two samples with T13 and T18. Despite low FF, these two samples were determined as aneuploid with NIPTmer software.\n\nConclusionOur NIPT analysis pipeline proved to perform efficiently in detecting common fetal aneuploidies T21, T18 and T13 and is feasible for implementation into clinical service in Estonia.

genetics↗

bioRxiv2018

TAC-seq: targeted DNA and RNA sequencing for precise biomarker molecule counting

Targeted next-generation sequencing based biomarker detection methods have become essential for biomedical diagnostics. In addition to their sensitivity and high-throughput capacity, absolute molecule counting based on unique molecular identifier (UMI) has high potential to increase biomarker detection accuracy even further through the reduction of systematic technical biases. Here, we present TAC-seq, a simple and cost-effective targeted allele counting by sequencing method that uses UMIs to estimate the original molecule counts of different biomarker types like mRNAs, microRNAs and cell-free DNA. We applied TAC-seq in three different applications and compared the results with standard sequencing technologies. RNA samples extracted from human endometrial biopsies were analyzed using previously described 57 mRNA-based receptivity biomarkers and 49 selected microRNAs at different expression levels. Cell-free DNA aneuploidy testing was based on cell line (47,XX,+21) genomic DNA. TAC-seq mRNA biomarker profiling showed identical clustering results to full transcriptome RNA sequencing, and microRNA detection demonstrated significant reduction in amplification bias, allowing to determine minor expression changes between different samples that remained undetermined by standard sequencing. The mimicking experiment for cell-free DNA fetal aneuploidy analysis showed that TAC-seq can be applied to count highly fragmented DNA, detecting significant (p=4.8x10-11) excess of molecules in case of trisomy 21. Based on three proof-of-principle applications we show that TAC-seq is a highly accurate and universal method for targeted nucleic acid biomarker profiling.

genomics↗

bioRxiv2017

Common variant burden contributes significantly to the familial aggregation of migraine in 1,589 families

It has long been observed that complex traits, including migraine, often aggregate in families, but the underlying genetic architecture behind this is not well understood. Two competing hypotheses exist, emphasizing either rare or common genetic variation. More specifically, familial aggregation could be predominantly explained by rare, penetrant variants that segregate according to Mendelian inheritance or rather by the sufficient polygenic accumulation of many common variants, each with an individually small effect. Some combination of both common and rare variation could also contribute towards a spectrum of disease risk.\n\nWe investigated this in a collection of 8,319 individuals across 1,589 migraine families from Finland. Family members were individually diagnosed by a migraine-specific questionnaire with either migraine without aura (MO, ICHD-3 code 1.1, n=2,357), migraine with typical aura (ICHD- 3 code 1.2.1, n=2,420), hemiplegic migraine (HM, ICHD-3 code 1.2.3, n=540), or no migraine (n=3,002). For comparison, we used population-based migraine cases (n=1,101) and controls (n=13,369) from the FINRISK study. The disease status of FINRISK individuals was assigned based on health registry data from outpatient clinics and/or prescription medication. All individuals were genotyped on the Illumina(R) CoreExome or PsychArray chip platforms and imputed to a Finnish reference panel of 6,962 haplotypes. Polygenic risk scores (PRS), representing the common variant burden in each individual, were calculated using weights from the most recent large-scale genome-wide association study of migraine. To account for family structure in our analyses, we used a mixed-model approach, adjusting for the genetic relationship matrix as a random effect.\n\nWe found a significantly higher common variant burden in familial cases of migraine (for all subtypes, measured by the odds ratio [OR] per standard deviation [SD] increase in PRS; OR = 1.76, 95% CI = 1.71-1.81, P = 1.7x10-109) compared to cases from a population cohort (OR = 1.32, 95% CI = 1.25-1.38, P = 7.2x10-17) when using the population controls as a reference group. The highest enrichment was observed for HM (OR = 1.96, 95% CI = 1.86-2.07, P = 8.7x10-36) and migraine with typical aura (OR = 1.85, 95% CI = 1.79-1.91, P = 1.4x10-86) but enrichment was also present for MO (OR = 1.57, 95% CI = 1.51-1.63, P = 1.1x10-48). Comparing within cases, there was no significant difference in common variant burden between the migraine with aura subtypes, HM and migraine with typical aura (OR = 1.09, 95% CI = 0.99-1.19, P = 0.09), but both showed significantly higher enrichment compared to MO (OR = 1.28, 95% CI = 1.17-1.38, P = 7.3x10-7, and OR = 1.17, 95% CI = 1.11-1.23, P = 4.62x10-5, respectively). Additionally, we found that higher common variant burden corresponded to earlier age of headache onset (OR per SD increase in PRS for 3,631 cases with onset before 20 years old compared to 1,686 cases with onset later than 20 years old; OR = 1.11, 95% CI = 1.05-1.18, P = 8.3x10-4). FINRISK population cases identified from national health registry data were found to have lower common variant burden in comparison to the familial migraine cases (OR = 1.32, 95% CI = 1.25-1.38, P = 6.8x10-17), unless the individuals had attended both a specialist clinic and also received prophylactic migraine treatment (OR = 1.70, 95% CI = 1.53-1.88, P = 3.9x10-9). Finally, although rare variants have been suggested as the primary cause for familial hemiplegic migraine (FHM), we found only four out of 45 sequenced FHM families (8.9%) with a pathogenic mutation in one of the known risk genes.\n\nIn summary, our results demonstrate a substantial contribution of common polygenic variation to familial aggregation in migraine, comparable to both controls and that observed in migraine cases from a population cohort. The findings also suggest that individuals with migraine aura symptoms (either typical aura, which is mostly visual, or rare motor aura) tend to have higher common variant burden on average supporting the polygenic model also in these migraine subtypes.

genetics↗