Vera Fominykh

• Hop, Paul J.; Kooyman, Maarten; Kenna, Brendan J.; Zwamborn, Ramona A. J.; Eijk, Kristel R. van & Wang, Yan [Show all 85 contributors for this article] (2026). Large-scale exome analyses reveal new rare variant contributions in amyotrophic lateral sclerosis. Nature Genetics. ISSN 1061-4036. p. 717–725. doi: 10.1038/s41588-026-02535-9. Full text in Research Archive Show summary

  Amyotrophic lateral sclerosis (ALS) is a heritable disorder where rare variants with low-to-moderate penetrance are thought to dominate genetic risk. To identify such rare variants, we harmonized and analyzed exome data from 22 cohorts, totaling 17,919 individuals with ALS and 200,703 controls across discovery and replication phases. Rare variant analyses identified several new risk genes, with replication confirming association of YKT6 and supporting HTR3C , GBGT1 and KNTC1 . We also provide strong, independent validation for genes with limited previous evidence: ARPP21 , DNAJC7 and CFAP410 . Notably, in ARPP21 , we identified a new high-effect variant (p.P747L) and confirmed that p.P563L is an ALS-associated variant leading to an aggressive disease course. Beyond new discoveries, our analyses largely recapitulated the known genetic architecture of ALS, identifying risk variants in over 20% of cases and supporting a cumulative oligogenic risk model. These findings highlight new translational targets and show that rare variant analyses capture substantially more genetic risk than common variant genome-wide association studies.

• Akdeniz, Bayram Cevdet; Bahrami, Shahram; Hagen, Espen; Fuhrer, Julian; Fominykh, Vera & Shadrin, Alexey [Show all 108 contributors for this article] (2026). Polygenic Hazard Score for Predicting Age-associated Risk of Alzheimer’s Disease in European Populations: Development and Validation. medRxiv. doi: 10.1101/2025.07.28.25332293. Full text in Research Archive Show summary

  Abstract Objectives Polygenic hazard score (PHS) models can be used to predict the age-associated risk for complex diseases, including Alzheimer’s disease (AD). In this study, we present an improved PHS model for AD that incorporates a large number of genetic variants and demonstrates enhanced predictive accuracy for age of onset in European populations compared to alternative models. Methods We used the genotyped European Alzheimer & Dementia Biobank (EADB) sample (n=42,120) to develop and evaluate the performance of the PHS model. We developed a PHS model building on 720 genetic variants, including Apolipoprotein E ( APOE ) ε2 and ε4 alleles. We used Elastic Net-regularized Cox regression approach to develop the PHS model. Results The new PHS model (EADB720) improved prediction accuracy compared to alternative models in European populations, with the Odds Ratio OR80/20 from the highest quintile of risk (80th risk percentile and above) to the lowest quintile of risk (20th risk percentile and below) varying between 5.10 and 13.15 within the range of age of onset from 65 – 85 years. Our model also improved risk stratification across ε3/3 individuals of European ancestry (OR80/20 ranges from 1.95 to 3.52). It was also successfully validated in independent datasets (HUSK, DemGene and ADNI) by achieving OR80/20 up to 10.00 in each independent dataset. Conclusion Our EADB720 model significantly improves the accuracy of age-associated risk of AD across European populations (pval<0.03). Accurately predicting the age of onset of AD is of large clinical importance to implementing new AD medication and early intervention in clinical settings.

• Wiström, Erik David; Fuhrer, Julian; Shadrin, Alexey; Fominykh, Vera; O'Connell, Kevin Sean & Jaholkowski, Piotr Pawel [Show all 17 contributors for this article] (2026). Dissecting the genetic relationship between severe mental disorders and autoimmune diseases. Brain, Behavior, and Immunity. ISSN 0889-1591. 133. doi: 10.1016/j.bbi.2025.106249. Full text in Research Archive Show summary

  Severe mental disorders have been linked to immune system dysfunction. While a genetic association between mental disorders and autoimmune diseases has been suggested, their genetic relationship remains incompletely understood. Utilizing a complementary set of statistical analyses, we conducted a comprehensive investigation of the genetic architecture between severe mental disorders (major depression (MD), bipolar disorder (BD), and schizophrenia (SCZ)) and seven autoimmune diseases (autoimmune thyroiditis, celiac disease, inflammatory bowel disease (IBD), multiple sclerosis, psoriasis, rheumatoid arthritis, and type 1 diabetes), involving a total of 667,518 cases from 10 genome-wide association studies. While MD was positively genetically correlated with five autoimmune diseases, BD and SCZ were only positively correlated with IBD, suggesting differences in the genetic signal shared with autoimmunity across these mental disorders. A considerable fraction of genetic variants influencing autoimmune diseases (range 17.1–88.4 %) was estimated to overlap with mental disorders; however, this constitutes only a minor part of genetic variants influencing the more polygenic mental disorders. Finally, we identified 172 genetic loci jointly affecting mental disorders and autoimmune diseases, implicating both lipid metabolism and TNF signaling. In conclusion, MD, BD, and SCZ have a small but distinct genetic overlap with autoimmune diseases, which may inform new possible immune targets for treatment in mental illness.

• Shadrin, Alexey; Hindley, Guy; Hagen, Espen; Parker, Nadine; Tesfaye, Markos & Jaholkowski, Piotr Pawel [Show all 16 contributors for this article] (2025). Distinct patterns of genetic overlap among multimorbidities revealed with trivariate MiXeR. Genome Medicine. 17. doi: 10.1186/s13073-025-01528-3. Full text in Research Archive Show summary

  Background: Multimorbidities are a global health challenge. Accumulating evidence indicates that overlapping genetic architectures underlie comorbid complex human traits and disorders. This can be quantified for a pair of phenotypes using various techniques. Still, the pattern of genetic overlap between three distinct complex phenotypes, which is important for understanding multimorbidities, has not been possible to quantify. Methods: Here, we present and validate the novel trivariate MiXeR tool, which disentangles the pattern of genetic overlap between three complex phenotypes using summary statistics from genome-wide association studies. Our simulations show that trivariate MiXeR can reliably reconstruct different patterns of genetic overlap and estimate the proportions of genetic overlap between three phenotypes. Results: We found substantial genetic overlap between gastro-intestinal and brain diseases supporting a genetic basis of the gut-brain axis—the pattern consistent with pairwise analysis. However, the pattern of genetic overlap between three diverse cardiometabolic and renal health indicators and three immune-linked disorders revealed a much larger genomic component shared between all phenotypes than expected from separate pairwise analyses. This suggests the existence of core pathways underlying distinct but related chronic conditions. Conclusions: Overall, trivariate MiXeR offers a novel and efficient tool for investigating patterns of genetic overlap among three complex phenotypes. This contributes to a better understanding of genetic relationships between complex traits and disorders, potentially providing new insights into the mechanisms underlying common multimorbidities. Trivariate MiXeR is freely available at https://github.com/precimed/mix3r .

• Smeland, Olav B.; Kutrolli, Gleda; Bahrami, Shahram; Fominykh, Vera; Parker, Nadine & Fuhrer, Julian [Show all 21 contributors for this article] (2025). A genome-wide analysis of the shared genetic risk architecture of complex neurological and psychiatric disorders. Nature Neuroscience. ISSN 1097-6256. 28(12), p. 2439–2450. doi: 10.1038/s41593-025-02090-2. Full text in Research Archive Show summary

  Although neurological and psychiatric disorders have historically been considered to reflect distinct pathogenic entities, recent findings suggest shared pathophysiological mechanisms. However, the extent to which these heritable disorders share genetic influences remains unclear. Here we performed a comprehensive analysis of genome-wide association study data, involving nearly 1 million cases across ten neurological diseases and ten psychiatric disorders, to compare their common genetic signal and biological associations. Using complementary statistical tools, we demonstrate that a large set of common genetic variants impacts the risk of multiple neurological and psychiatric disorders, even in the absence of genetic correlations. Furthermore, genome-wide association studies on psychiatric disorders consistently implicate neuronal biology, whereas neurological diseases are associated with diverse neurobiological processes. Together, this study elucidates the genetic relationship between complex neurological and psychiatric disorders, indicating a larger degree of genetic pleiotropy than previously recognized. The findings have implications for disease classification, precision medicine and clinical practice.

• Al-Soufi, Laila; Hindley, Guy; Rødevand, Linn; Shadrin, Alexey; Jaholkowski, Piotr Pawel & Fominykh, Vera [Show all 10 contributors for this article] (2025). Polygenic overlap of substance use behaviors and disorders with externalizing and internalizing problems independent of genetic correlations. Psychological Medicine. ISSN 0033-2917. 55. doi: 10.1017/S0033291725000108. Full text in Research Archive Show summary

  Background: Externalizing and internalizing pathways may lead to the development of substance use behaviors (SUBs) and substance use disorders (SUDs), which are all heritable phenotypes. Genetic correlation studies have indicated differences in the genetic susceptibility between SUBs and SUDs. We investigated whether these substance use phenotypes are differently related to externalizing and internalizing problems at a genetic level. Methods: We analyzed data from genome-wide association studies (GWAS) of four SUBs and SUDs, five externalizing traits, and five internalizing traits using the bivariate causal mixture model (MiXeR) to estimate genetic overlap beyond genetic correlation. Results: Two distinct patterns were found. SUBs demonstrated high genetic overlap but low genetic correlation of shared variants with internalizing traits, suggesting a pattern of mixed effect directions of shared genetic variants. Conversely, SUDs and externalizing traits exhibited considerable genetic overlap with moderate to high positive genetic correlation of shared variants, suggesting concordant effect direction of shared risk variants. Conclusions: These results highlight the importance of the externalizing pathway in SUDs as well as the limited role of the internalizing pathway in SUBs. As MiXeR is not intended for the identification of specific genes, further studies are needed to reveal the underlying shared mechanisms of these traits.

• Fominykh, Vera; Shadrin, Alexey; Jaholkowski, Piotr Pawel; Fuhrer, Julian; Parker, Nadine & Wiström, Erik David [Show all 11 contributors for this article] (2025). Mapping the genetic landscape of immune-mediated disorders: potential implications for classification and therapeutic strategies. Frontiers in Immunology. 16. doi: 10.3389/fimmu.2025.1543781. Full text in Research Archive Show summary

  Objectives: Based on clinical, biomarker, and genetic data, McGonagle and McDermott suggested that autoimmune and autoinflammatory disorders can be classified as a disease continuum from purely autoimmune to autoinflammatory with mixed diseases in between. However, the genetic architecture of this spectrum has not been systematically described. Here, we investigate the continuum of polygenic immune-mediated disorders using genome-wide association studies (GWAS) and statistical genetics methods. Methods: We mapped the genetic landscape of 15 immune-mediated disorders using GWAS summary statistics and methods including genomic structural equation modeling (genomic SEM), linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and Gaussian causal mixture modeling (MiXeR). We performed enrichment analyses of tissues and biological gene sets using MAGMA. Results: Genomic SEM suggested a continuum structure with four underlying latent factors from autoimmune diseases at one end to autoinflammatory on the opposite end. Across disorders, we observed a balanced mixture of negative and positive local genetic correlations within the major histocompatibility complex, while outside this region, local genetic correlations were predominantly positive. MiXeR analysis showed large genetic overlap in accordance with the continuum landscape. MAGMA analysis implicated genes associated with known monogenic immune diseases for prominent autoimmune and autoinflammatory component. Conclusions: Our findings support a polygenic continuum across immune-mediated disorders, with four genetic clusters. The “polygenic autoimmune” and “polygenic autoinflammatory” clusters reside on margins of this continuum. These findings provide insights and lead us to hypothesize that the identified clusters could inform future therapeutical strategies, with patients in the same clusters potentially responding similarly to specific therapies.

• Nicolas, Aude; Sherva, Richard; Grenier-Boley, Benjamin; Kim, Yoontae; Kikuchi, Masataka & Timsina, Jigyasha [Show all 52 contributors for this article] (2025). Transferability of European-derived Alzheimer's disease polygenic risk scores across multiancestry populations. Nature Genetics. ISSN 1061-4036. 57(7), p. 1598–1610. doi: 10.1038/s41588-025-02227-w. Full text in Research Archive
• Jaholkowski, Piotr Pawel; Bahrami, Shahram; Fominykh, Vera; Hindley, Guy; Tesfaye, Markos & Parekh, Pravesh [Show all 23 contributors for this article] (2024). Charting the shared genetic architecture of Alzheimer's disease, cognition, and educational attainment, and associations with brain development. Neurobiology of Disease. ISSN 0969-9961. 203. doi: 10.1016/j.nbd.2024.106750. Full text in Research Archive Show summary

  The observation that the risk of developing Alzheimer's disease is reduced in individuals with high premorbid cognitive functioning, higher educational attainment, and occupational status has led to the ‘cognitive reserve’ hypothesis. This hypothesis suggests that individuals with greater cognitive reserve can tolerate a more significant burden of neuropathological changes before the onset of cognitive decline. The underpinnings of cognitive reserve remain poorly understood, although a shared genetic basis between measures of cognitive reserve and Alzheimer's disease has been suggested. Using the largest samples to date and novel statistical tools, we aimed to investigate shared genetic variants between Alzheimer's disease, and measures of cognitive reserve; cognition and educational attainment to identify molecular and neurobiological foundations. We applied the causal mixture model (MiXeR) to estimate the number of trait-influencing variants shared between Alzheimer's disease, cognition, and educational attainment, and condFDR/conjFDR to identify shared loci. To provide biological insights loci were functionally characterized. Subsequently, we constructed a Structural Equation Model (SEM) to determine if the polygenic foundation of cognition has a direct impact on Alzheimer's disease risk, or if its effect is mediated through established risk factors for the disease, using a case-control sample from the UK Biobank. Univariate MiXeR analysis (after excluding chromosome 19) revealed that Alzheimer's disease was substantially less polygenic (450 trait-influencing variants) compared to cognition (11,100 trait-influencing variants), and educational attainment (12,700 trait-influencing variants). Bivariate MiXeR analysis estimated that Alzheimer's disease shared approximately 70 % of trait-influencing variants with cognition, and approximately 40 % with educational attainment, with mixed effect directions. Using condFDR analysis, we identified 18 loci jointly associated with Alzheimer's disease and cognition and 6 loci jointly associated with Alzheimer's disease and educational attainment. Genes mapped to shared loci were associated with neurodevelopment, expressed in early life, and implicated the dendritic tree and phosphatidylinositol phosphate binding mechanisms. Spatiotemporal gene expression analysis of the identified genes showed that mapped genes were highly expressed during the mid-fetal period, further suggesting early neurodevelopmental stages as critical periods for establishing cognitive reserve which affect the risk of Alzheimer's disease in old age. Furthermore, our SEM analysis showed that genetic variants influencing cognition had a direct effect on the risk of developing Alzheimer's disease, providing evidence in support of the neurodevelopmental hypothesis of the disease.

• Gurholt, Tiril Pedersen; Borda, Miguel Germán; Parker, Nadine; Fominykh, Vera; Kjelkenes, Rikka & Linge, Jennifer [Show all 12 contributors for this article] (2024). Linking sarcopenia, brain structure and cognitive performance: a large-scale UK Biobank study. Brain Communications. 6(2). doi: 10.1093/braincomms/fcae083. Full text in Research Archive Show summary

  Sarcopenia refers to age-related loss of muscle mass and function and is related to impaired somatic and brain health, including cognitive decline and Alzheimer’s disease. However, the relationships between sarcopenia, brain structure and cognition are poorly understood. Here, we investigate the associations between sarcopenic traits, brain structure and cognitive performance. We included 33 709 UK Biobank participants (54.2% female; age range 44–82 years) with structural and diffusion magnetic resonance imaging, thigh muscle fat infiltration (n = 30 561) from whole-body magnetic resonance imaging (muscle quality indicator) and general cognitive performance as indicated by the first principal component of a principal component analysis across multiple cognitive tests (n = 22 530). Of these, 1703 participants qualified for probable sarcopenia based on low handgrip strength, and we assigned the remaining 32 006 participants to the non-sarcopenia group. We used multiple linear regression to test how sarcopenic traits (probable sarcopenia versus non-sarcopenia and percentage of thigh muscle fat infiltration) relate to cognitive performance and brain structure (cortical thickness and area, white matter fractional anisotropy and deep and lower brain volumes). Next, we used structural equation modelling to test whether brain structure mediated the association between sarcopenic and cognitive traits. We adjusted all statistical analyses for confounders. We show that sarcopenic traits (probable sarcopenia versus non-sarcopenia and muscle fat infiltration) are significantly associated with lower cognitive performance and various brain magnetic resonance imaging measures. In probable sarcopenia, for the included brain regions, we observed widespread significant lower white matter fractional anisotropy (77.1% of tracts), predominantly lower regional brain volumes (61.3% of volumes) and thinner cortical thickness (37.9% of parcellations), with |r| effect sizes in (0.02, 0.06) and P-values in (0.0002, 4.2e−29). In contrast, we observed significant associations between higher muscle fat infiltration and widespread thinner cortical thickness (76.5% of parcellations), lower white matter fractional anisotropy (62.5% of tracts) and predominantly lower brain volumes (35.5% of volumes), with |r| effect sizes in (0.02, 0.07) and P-values in (0.0002, 1.9e−31). The regions showing the most significant effect sizes across the cortex, white matter and volumes were of the sensorimotor system. Structural equation modelling analysis revealed that sensorimotor brain regions mediate the link between sarcopenic and cognitive traits [probable sarcopenia: P-values in (0.0001, 1.0e−11); muscle fat infiltration: P-values in (7.7e−05, 1.7e−12)]. Our findings show significant associations between sarcopenic traits, brain structure and cognitive performance in a middle-aged and older adult population. Mediation analyses suggest that regional brain structure mediates the association between sarcopenic and cognitive traits, with potential implications for dementia development and prevention.

• Karadag, Naz; Hagen, Espen; Shadrin, Alexey; Meer, Dennis van der; O'Connell, Kevin Sean & Rahman, Mohammad Zillur [Show all 18 contributors for this article] (2024). Dissecting the Shared Genetic Architecture of Common Epilepsies With Cortical Brain Morphology. Neurology: Genetics. 10(3). doi: 10.1212/NXG.0000000000200143. Full text in Research Archive Show summary

  Background and Objectives: Epilepsies are associated with differences in cortical thickness (TH) and surface area (SA). However, the mechanisms underlying these relationships remain elusive. We investigated the extent to which these phenotypes share genetic influences. Methods: We analyzed genome-wide association study data on common epilepsies (n = 69,995) and TH and SA (n = 32,877) using Gaussian mixture modeling MiXeR and conjunctional false discovery rate (conjFDR) analysis to quantify their shared genetic architecture and identify overlapping loci. We biologically interrogated the loci using a variety of resources and validated in independent samples. Results: The epilepsies (2.4 k–2.9 k variants) were more polygenic than both SA (1.8 k variants) and TH (1.3 k variants). Despite absent genome-wide genetic correlations, there was a substantial genetic overlap between SA and genetic generalized epilepsy (GGE) (1.1 k), all epilepsies (1.1 k), and juvenile myoclonic epilepsy (JME) (0.7 k), as well as between TH and GGE (0.8 k), all epilepsies (0.7 k), and JME (0.8 k), estimated with MiXeR. Furthermore, conjFDR analysis identified 15 GGE loci jointly associated with SA and 15 with TH, 3 loci shared between SA and childhood absence epilepsy, and 6 loci overlapping between SA and JME. 23 loci were novel for epilepsies and 11 for cortical morphology. We observed a high degree of sign concordance in the independent samples. Discussion: Our findings show extensive genetic overlap between generalized epilepsies and cortical morphology, indicating a complex genetic relationship with mixed-effect directions. The results suggest that shared genetic influences may contribute to cortical abnormalities in epilepsies.

• Karadag, Naz; Hagen, Espen; Shadrin, Alexey; Meer, Dennis van der; O'Connell, Kevin Sean & Rahman, Mohammad Zillur [Show all 19 contributors for this article] (2024). Unraveling the shared genetics of common epilepsies and general cognitive ability. Seizure. ISSN 1059-1311. 122, p. 105–112. doi: 10.1016/j.seizure.2024.09.016. Full text in Research Archive Show summary

  Purpose Cognitive impairment is prevalent among individuals with epilepsy, and increasing evidence indicates that genetic factors can underlie this relationship. However, the extent to which epilepsy subtypes differ in their genetic relationship with cognitive function, and information about the specific genetic variants involved remain largely unknown. Methods We investigated the genetic relationship between epilepsies and general cognitive ability (COG) using complementary statistical tools, including linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR). We analyzed genome-wide association study data on COG (n = 269,867) and common epilepsies (n = 27,559 cases, 42,436 controls), including the broad phenotypes ‘all epilepsy’, focal epilepsies and genetic generalized epilepsies (GGE), as well as specific subtypes. We functionally annotated the identified loci using several biological resources and validated the results in independent samples. Results Using MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than ‘all epilepsy’, GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k – 2.9k variants). The other epilepsy phenotypes were insufficiently powered for MiXeR analysis. We quantified extensive genetic overlap between COG and epilepsy types, but with varying negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and ‘all epilepsy’, and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), ‘all epilepsy’ (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 3.62 × 10–7; ‘all epilepsy’: p = 2.58 × 10–3). Conclusion Our study further dissects the substantial genetic basis shared between epilepsies and COG and identifies novel shared loci. An improved understanding of the genetic relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments.

• Jaholkowski, Piotr Pawel; Shadrin, Alexey; Jangmo, Anders Andreas Krister; Frei, Evgeniia; Woldeyohannes, Markos Tesfaye & Hindley, Guy Frederick Lanyon [Show all 23 contributors for this article] (2023). Associations Between Symptoms of Premenstrual Disorders and Polygenic Liability for Major Psychiatric Disorders. JAMA psychiatry. ISSN 2168-6238. doi: 10.1001/jamapsychiatry.2023.1137. Full text in Research Archive
• Karadag, Naz; Shadrin, Alexey; O'Connell, Kevin Sean; Hindley, Guy Frederick Lanyon; Rahman, Zillur & Parker, Nadine [Show all 18 contributors for this article] (2023). Identification of novel genomic risk loci shared between common epilepsies and psychiatric disorders. Brain. ISSN 0006-8950. 146(8), p. 3392–3403. doi: 10.1093/brain/awad038. Full text in Research Archive Show summary

  Psychiatric disorders and common epilepsies are heritable disorders with a high comorbidity and overlapping symptoms. However, the causative mechanisms underlying this relationship are poorly understood. Here we aimed to identify overlapping genetic loci between epilepsy and psychiatric disorders to gain a better understanding of their comorbidity and shared clinical features. We analysed genome-wide association study data for all epilepsies (n = 44 889), genetic generalized epilepsy (n = 33 446), focal epilepsy (n = 39 348), schizophrenia (n = 77 096), bipolar disorder (n = 406 405), depression (n = 500 199), attention deficit hyperactivity disorder (n = 53 293) and autism spectrum disorder (n = 46 350). First, we applied the MiXeR tool to estimate the total number of causal variants influencing the disorders. Next, we used the conjunctional false discovery rate statistical framework to improve power to discover shared genomic loci. Additionally, we assessed the validity of the findings in independent cohorts, and functionally characterized the identified loci. The epilepsy phenotypes were considerably less polygenic (1.0 K to 3.4 K causal variants) than the psychiatric disorders (5.6 K to 13.9 K causal variants), with focal epilepsy being the least polygenic (1.0 K variants), and depression having the highest polygenicity (13.9 K variants). We observed cross-trait genetic enrichment between genetic generalized epilepsy and all psychiatric disorders and between all epilepsies and schizophrenia and depression. Using conjunctional false discovery rate analysis, we identified 40 distinct loci jointly associated with epilepsies and psychiatric disorders at conjunctional false discovery rate <0.05, four of which were associated with all epilepsies and 39 with genetic generalized epilepsy. Most epilepsy risk loci were shared with schizophrenia (n = 31). Among the identified loci, 32 were novel for genetic generalized epilepsy, and two were novel for all epilepsies. There was a mixture of concordant and discordant allelic effects in the shared loci. The sign concordance of the identified variants was highly consistent between the discovery and independent datasets for all disorders, supporting the validity of the findings. Gene-set analysis for the shared loci between schizophrenia and genetic generalized epilepsy implicated biological processes related to cell cycle regulation, protein phosphatase activity, and membrane and vesicle function; the gene-set analyses for the other loci were underpowered. The extensive genetic overlap with mixed effect directions between psychiatric disorders and common epilepsies demonstrates a complex genetic relationship between these disorders, in line with their bi-directional relationship, and indicates that overlapping genetic risk may contribute to shared pathophysiological and clinical features between epilepsy and psychiatric disorders.

• Jaholkowski, Piotr; Shadrin, Alexey A.; Jangmo, Andreas; Frei, Evgeniia; Woldeyohannes, Markos Tesfaye & Hindley, Guy F. L. [Show all 23 contributors for this article] (2023). Associations Between Symptoms of Premenstrual Disorders and Polygenic Liability for Major Psychiatric Disorders. JAMA psychiatry. ISSN 2168-6238. 80(7), p. 738–742. doi: 10.1001/jamapsychiatry.2023.1137. Full text in Research Archive
• Fominykh, Vera; Shadrin, Alexey; Jaholkowski, Piotr Pawel; Bahrami, Shahram; Athanasiu, Lavinia & Wightman, Douglas P. [Show all 13 contributors for this article] (2023). Shared genetic loci between Alzheimer's disease and multiple sclerosis: Crossroads between neurodegeneration and immune system. Neurobiology of Disease. ISSN 0969-9961. 183. doi: 10.1016/j.nbd.2023.106174. Full text in Research Archive Show summary

  Background Neuroinflammation is involved in the pathophysiology of Alzheimer's disease (AD), including immune-linked genetic variants and molecular pathways, microglia and astrocytes. Multiple Sclerosis (MS) is a chronic, immune-mediated disease with genetic and environmental risk factors and neuropathological features. There are clinical and pathobiological similarities between AD and MS. Here, we investigated shared genetic susceptibility between AD and MS to identify putative pathological mechanisms shared between neurodegeneration and the immune system. Methods We analysed GWAS data for late-onset AD (N cases = 64,549, N controls = 634,442) and MS (N cases = 14,802, N controls = 26,703). Gaussian causal mixture modelling (MiXeR) was applied to characterise the genetic architecture and overlap between AD and MS. Local genetic correlation was investigated with Local Analysis of [co]Variant Association (LAVA). The conjunctional false discovery rate (conjFDR) framework was used to identify the specific shared genetic loci, for which functional annotation was conducted with FUMA and Open Targets. Results MiXeR analysis showed comparable polygenicities for AD and MS (approximately 1800 trait-influencing variants) and genetic overlap with 20% of shared trait-influencing variants despite negligible genetic correlation (rg = 0.03), suggesting mixed directions of genetic effects across shared variants. conjFDR analysis identified 16 shared genetic loci, with 8 having concordant direction of effects in AD and MS. Annotated genes in shared loci were enriched in molecular signalling pathways involved in inflammation and the structural organisation of neurons. Conclusions Despite low global genetic correlation, the current results provide evidence for polygenic overlap between AD and MS. The shared loci between AD and MS were enriched in pathways involved in inflammation and neurodegeneration, highlighting new opportunities for future investigation.

• Reas, Emilie T.; Shadrin, Alexey; Frei, Oleksandr; Motazedi, Ehsan; McEvoy, Linda & Bahrami, Shahram [Show all 22 contributors for this article] (2023). Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures. Alzheimer's & Dementia. ISSN 1552-5260. 19(11), p. 5151–5158. doi: 10.1002/alz.13112. Full text in Research Archive Show summary

  Introduction There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD). Methods Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS. Results The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau. Discussion The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment.

View all works in NVA