CpG methylation changes in human mesenchymal and neural stem cells in response to in vitro niche modifications.

Stem cell therapies hold promise in addressing the burden of neurodegenerative diseases with human embryonic neural stem cells (hNSC-H9s) and bone marrow-derived human mesenchymal stem cells (hMSCs) as viable candidates. The induction of hMSC neurospheres (hMSC-IN) generate a more lineage-restricted common neural progenitor-like cell population, potentially tunable by heparan sulfate proteoglycans (HSPGs). We examined CpG (5 mC) site methylation patterns using Illumina Infinium 850 K EPIC arrays in hNSC-H9, hMSCs and hMSC-IN cultures with HSPG agonist heparin at early and late phases of growth. We identified key regulatory CpG sites in syndecans (SDC2; SDC4) that potentially regulate gene expression in monolayers. Unique hMSC-IN hypomethylation in glypicans (GPC3; GPC4) underscore their significance in neural lineages with Sulfatase 1 and 2 (SULF1 &2) CpG methylation changes potentially driving the neurogenic shift. hMSC-INs methylation levels at SULF1 CpG sites and SULF2:cg25401628 were more closely aligned with hNSC-H9 cells than with hMSCs. We further suggest SOX2 regulation governed by lncSOX2-Overall Transcript (lncSOX2-OT) methylation changes with preferential activation of ENO2 over other neuronal markers within hMSC-INs. Our findings illuminate epigenetic dynamics governing neural lineage commitment of hMSC-INs offering insights for targeted mechanisms for regenerative medicine and therapeutic strategies.

Alterations in DNA methylation associate with reduced migraine and headache days after medication withdrawal treatment in chronic migraine patients: a longitudinal study.

BACKGROUND: Chronic migraine, a highly disabling migraine subtype, affects nearly 2% of the general population. Understanding migraine chronification is vital for developing better treatment and prevention strategies. An important factor in the chronification of migraine is the overuse of acute headache medication. However, the mechanisms behind the transformation of episodic migraine to chronic migraine and vice versa have not yet been elucidated. We performed a longitudinal epigenome-wide association study to identify DNA methylation (DNAm) changes associated with treatment response in patients with chronic migraine and medication overuse as part of the Chronification and Reversibility of Migraine clinical trial. Blood was taken from patients with chronic migraine (n = 98) at baseline and after a 12-week medication withdrawal period. Treatment responders, patients with ≥ 50% reduction in monthly headache days (MHD), were compared with non-responders to identify DNAm changes associated with treatment response. Similarly, patients with ≥ 50% versus < 50% reduction in monthly migraine days (MMD) were compared. RESULTS: At the epigenome-wide significant level (p < 9.42 × 10-8), a longitudinal reduction in DNAm at an intronic CpG site (cg14377273) within the HDAC4 gene was associated with MHD response following the withdrawal of acute medication. HDAC4 is highly expressed in the brain, plays a major role in synaptic plasticity, and modulates the expression and release of several neuroinflammation markers which have been implicated in migraine pathophysiology. Investigating whether baseline DNAm associated with treatment response, we identified lower baseline DNAm at a CpG site (cg15205829) within MARK3 that was significantly associated with MMD response at 12 weeks. CONCLUSIONS: Our findings of a longitudinal reduction in HDAC4 DNAm status associated with treatment response and baseline MARK3 DNAm status as an early biomarker for treatment response, provide support for a role of pathways related to chromatin structure and synaptic plasticity in headache chronification and introduce HDAC4 and MARK3 as novel therapeutic targets.

Integrating eQTL and GWAS data characterises established and identifies novel migraine risk loci.

Migraine-a painful, throbbing headache disorder-is the most common complex brain disorder, yet its molecular mechanisms remain unclear. Genome-wide association studies (GWAS) have proven successful in identifying migraine risk loci; however, much work remains to identify the causal variants and genes. In this paper, we compared three transcriptome-wide association study (TWAS) imputation models-MASHR, elastic net, and SMultiXcan-to characterise established genome-wide significant (GWS) migraine GWAS risk loci, and to identify putative novel migraine risk gene loci. We compared the standard TWAS approach of analysing 49 GTEx tissues with Bonferroni correction for testing all genes present across all tissues (Bonferroni), to TWAS in five tissues estimated to be relevant to migraine, and TWAS with Bonferroni correction that took into account the correlation between eQTLs within each tissue (Bonferroni-matSpD). Elastic net models performed in all 49 GTEx tissues using Bonferroni-matSpD characterised the highest number of established migraine GWAS risk loci (n = 20) with GWS TWAS genes having colocalisation (PP4 > 0.5) with an eQTL. SMultiXcan in all 49 GTEx tissues identified the highest number of putative novel migraine risk genes (n = 28) with GWS differential expression at 20 non-GWS GWAS loci. Nine of these putative novel migraine risk genes were later found to be at and in linkage disequilibrium with true (GWS) migraine risk loci in a recent, more powerful migraine GWAS. Across all TWAS approaches, a total of 62 putative novel migraine risk genes were identified at 32 independent genomic loci. Of these 32 loci, 21 were true risk loci in the recent, more powerful migraine GWAS. Our results provide important guidance on the selection, use, and utility of imputation-based TWAS approaches to characterise established GWAS risk loci and identify novel risk gene loci.

Shared genetics and causal relationships between migraine and thyroid function traits.

BACKGROUND: Epidemiological studies have reported a comorbid relationship between migraine and thyroid dysfunction. METHODS: We investigated the genetic relationship between migraine and thyroid function traits using genome-wide association study (GWAS) data. RESULTS: We found a significant genetic correlation (rg) with migraine for hypothyroidism (rg = 0.0608), secondary hypothyroidism (rg = 0.195), free thyroxine (fT4) (rg = 0.0772), and hyperthyroidism (rg = -0.1046), but not thyroid stimulating hormone (TSH). Pairwise GWAS analysis revealed two shared loci with TSH and 11 shared loci with fT4. Cross-trait GWAS meta-analysis of migraine identified novel genome-wide significant loci: 17 with hypothyroidism, one with hyperthyroidism, five with secondary hypothyroidism, eight with TSH, and 15 with fT4. Of the genes at these loci, six (RERE, TGFB2, APLF, SLC9B1, SGTB, BTBD16; migraine + hypothyroidism), three (GADD45A, PFDN1, RSPH6A; migraine + TSH), and three (SSBP3, BRD3, TEF; migraine + fT4) were significant in our gene-based analysis (pFisher's combined P-value < 2.04 × 10-6). In addition, causal analyses suggested a negative causal relationship between migraine and hyperthyroidism (p = 8.90 × 10-3) and a positive causal relationship between migraine and secondary hypothyroidism (p = 1.30 × 10-3). CONCLUSION: These findings provide strong evidence for genetic correlation and suggest complex causal relationships between migraine and thyroid traits.

Cross-trait analyses identify shared genetics between migraine, headache, and glycemic traits, and a causal relationship with fasting proinsulin.

The co-occurrence of migraine and glycemic traits has long been reported in observational epidemiological studies, but it has remained unknown how they are linked genetically. We used large-scale GWAS summary statistics on migraine, headache, and nine glycemic traits in European populations to perform cross-trait analyses to estimate genetic correlation, identify shared genomic regions, loci, genes, and pathways, and test for causal relationships. Out of the nine glycemic traits, significant genetic correlation was observed for fasting insulin (FI) and glycated haemoglobin (HbA1c) with both migraine and headache, while 2-h glucose was genetically correlated only with migraine. Among 1703 linkage disequilibrium (LD) independent regions of the genome, we found pleiotropic regions between migraine and FI, fasting glucose (FG), and HbA1c, and pleiotropic regions between headache and glucose, FI, HbA1c, and fasting proinsulin. Cross-trait GWAS meta-analysis with glycemic traits, identified six novel genome-wide significant lead SNPs with migraine, and six novel lead SNPs with headache (Pmeta < 5.0 × 10-8 and Psingle-trait < 1 × 10-4), all of which were LD-independent. Genes with a nominal gene-based association (Pgene ≤ 0.05) were significantly enriched (overlapping) across the migraine, headache, and glycemic traits. Mendelian randomisation analyses produced intriguing, but inconsistent, evidence for a causal relationship between migraine and headache with multiple glycemic traits; and consistent evidence suggesting increased fasting proinsulin levels may causally decrease the risk of headache. Our findings indicate that migraine, headache, and glycemic traits share a common genetic etiology and provide genetic insights into the molecular mechanisms contributing to their comorbid relationship.

Migraine genetics: Status and road forward.

BACKGROUND: Migraine is considered a multifactorial genetic disorder. Different platforms and methods are used to unravel the genetic basis of migraine. Initially, linkage analysis in multigenerational families followed by Sanger sequencing of protein-coding parts (exons) of genes in the genomic region shared by affected family members identified high-effect risk DNA mutations for rare Mendelian forms of migraine, foremost hemiplegic migraine. More recently, genome-wide association studies testing millions of DNA variants in large groups of patients and controls have proven successful in identifying many dozens of low-effect risk DNA variants for the more common forms of migraine with the number of associated DNA variants increasing steadily with larger sample sizes. Currently, next-generation sequencing, utilising whole exome and whole genome sequence data, and other omics data are being used to facilitate their functional interpretation and the discovery of additional risk factors. Various methods and analysis tools, such as genetic correlation and causality analysis, are used to further characterise genetic risk factors. FINDINGS: We describe recent findings in genome-wide association studies and next-generation sequencing analysis in migraine. We show that the combined results of the two most recent and most powerful migraine genome-wide association studies have identified a total of 178 LD-independent (r2 < 0.1) genome-wide significant single nucleotide polymorphisms (SNPs), of which 99 were unique to Hautakangas et al., 11 were unique to Choquet et al., and 68 were identified by both studies. When considering that Choquet et al. also identified three SNPs in a female-specific genome-wide association studies then these two recent studies identified 181 independent SNPs robustly associated with migraine. Cross-trait and causal analyses are beginning to identify and characterise specific biological factors that contribute to migraine risk and its comorbid conditions. CONCLUSION: This review provides a timely update and overview of recent genetic findings in migraine.

Migraine and thyroid dysfunction: Co-occurrence, shared genes and biological mechanisms.

BACKGROUND AND PURPOSE: Migraine and thyroid dysfunction, particularly hypothyroidism, are common medical conditions and are known to have high heritability. Thyroid function measures, thyroid stimulating hormone (TSH) and free thyroxine (fT4), are also known to be genetically influenced. Although observational epidemiological studies report an increased co-occurrence of migraine and thyroid dysfunction, a clear and combined interpretation of the findings is currently lacking. A narrative review is provided of the epidemiological and genetic association evidence linking migraine, hypothyroidism, hyperthyroidism and thyroid hormones TSH and fT4. METHODS: An extensive literature search was conducted in the PubMed database for epidemiological, candidate gene and genome-wide association studies using the terms migraine, headache, thyroid hormones, TSH, fT4, thyroid function, hypothyroidism and hyperthyroidism. RESULTS: Epidemiological studies suggest a bidirectional relationship between migraine and thyroid dysfunction. However, the nature of the relationship remains unclear, with some studies suggesting migraine increases the risk for thyroid dysfunction whilst other studies suggest the reverse. Early candidate gene studies have provided nominal evidence for MTHFR and APOE, whilst more recently genome-wide association studies have provided robust evidence for THADA and ITPK1 being associated with both migraine and thyroid dysfunction. CONCLUSIONS: These genetic associations improve our understanding of the genetic relationship between migraine and thyroid dysfunction, provide an opportunity to develop biomarkers to identify migraine patients most likely to benefit from thyroid hormone therapy, and indicate that further cross-trait genetic studies have excellent potential to provide biological insight into their relationship and inform clinical interventions.

The shared genetic landscape of blood cell traits and risk of neurological and psychiatric disorders.

Phenotypic associations have been reported between blood cell traits (BCTs) and a range of neurological and psychiatric disorders (NPDs), but in most cases, it remains unclear whether these associations have a genetic basis and, if so, to what extent genetic correlations reflect causality. Here, we report genetic correlations and Mendelian randomization analyses between 11 NPDs and 29 BCTs, using genome-wide association study summary statistics. We found significant genetic correlations for four BCT-NPD pairs, all of which have prior evidence for a phenotypic correlation. We identified a previously unreported causal effect of increased platelet distribution width on susceptibility to Parkinson's disease. We identified multiple functional genes and regulatory elements for specific BCT-NPD pairs, some of which are targets of known drugs. These results enrich our understanding of the shared genetic landscape underlying BCTs and NPDs and provide a robust foundation for future work to improve prognosis and treatment of common NPDs.

Shared genetic influences between blood analyte levels and risk of severe COVID-19.

Genome-wide association studies (GWASs) show that genetic factors contribute to the risk of severe coronavirus disease 2019 (COVID-19) and blood analyte levels. Here, we utilize GWAS summary statistics to study the shared genetic influences (pleiotropy) between severe COVID-19 and 344 blood analytes at the genome, gene, and single-nucleotide polymorphism (SNP) levels. Our pleiotropy analyses genetically link blood levels of 71 analytes to severe COVID-19 in at least one of the three levels of investigation-suggesting shared biological mechanisms or causal relationships. Six analytes (alanine aminotransferase, alkaline phosphatase, apolipoprotein B, C-reactive protein, triglycerides, and urate) display evidence of pleiotropy with severe COVID-19 at all three levels. Causality analyses indicate that higher triglycerides levels causally increase the risk of severe COVID-19, thereby providing important support for the use of lipid-lowering drugs such as statins and fibrates to prevent severe COVID-19.

Genetic Overlap Analysis Identifies a Shared Etiology between Migraine and Headache with Type 2 Diabetes.

Migraine and headache frequently co-occur with type 2 diabetes (T2D), suggesting a shared aetiology between the two conditions. We used genome-wide association study (GWAS) data to investigate the genetic overlap and causal relationship between migraine and headache with T2D. Using linkage disequilibrium score regression (LDSC), we found a significant genetic correlation between migraine and T2D (rg = 0.06, p = 1.37 × 10−5) and between headache and T2D (rg = 0.07, p = 3.0 × 10−4). Using pairwise GWAS (GWAS-PW) analysis, we identified 11 pleiotropic regions between migraine and T2D and 5 pleiotropic regions between headache and T2D. Cross-trait SNP meta-analysis identified 23 novel SNP loci (Pmeta < 5 × 10−8) associated with migraine and T2D, and three novel SNP loci associated with headache and T2D. Cross-trait gene-based overlap analysis identified 33 genes significantly associated (Pgene-based < 3.85 × 10−6) with migraine and T2D, and 11 genes associated with headache and T2D, with 7 genes (EHMT2, SLC44A4, PLEKHA1, CFDP1, TMEM170A, CHST6, and BCAR1) common between them. There was also a significant overlap of genes nominally associated (Pgene-based < 0.05) with both migraine and T2D (Pbinomial-test = 2.83 × 10−46) and headache and T2D (Pbinomial-test = 4.08 × 10−29). Mendelian randomisation (MR) analyses did not provide consistent evidence for a causal relationship between migraine and T2D. However, we found headache was causally associated (inverse-variance weighted, ORIVW = 0.90, Pivw = 7 × 10−3) with T2D. Our findings robustly confirm the comorbidity of migraine and headache with T2D, with shared genetically controlled biological mechanisms contributing to their co-occurrence, and evidence for a causal relationship between headache and T2D.

A large-scale genome-wide cross-trait analysis reveals shared genetic architecture between Alzheimer's disease and gastrointestinal tract disorders.

Consistent with the concept of the gut-brain phenomenon, observational studies suggest a relationship between Alzheimer's disease (AD) and gastrointestinal tract (GIT) disorders; however, their underlying mechanisms remain unclear. Here, we analyse several genome-wide association studies (GWAS) summary statistics (N = 34,652-456,327), to assess the relationship of AD with GIT disorders. Findings reveal a positive significant genetic overlap and correlation between AD and gastroesophageal reflux disease (GERD), peptic ulcer disease (PUD), gastritis-duodenitis, irritable bowel syndrome and diverticulosis, but not inflammatory bowel disease. Cross-trait meta-analysis identifies several loci (Pmeta-analysis < 5 × 10-8) shared by AD and GIT disorders (GERD and PUD) including PDE4B, BRINP3, ATG16L1, SEMA3F, HLA-DRA, SCARA3, MTSS2, PHB, and TOMM40. Colocalization and gene-based analyses reinforce these loci. Pathway-based analyses demonstrate significant enrichment of lipid metabolism, autoimmunity, lipase inhibitors, PD-1 signalling, and statin mechanisms, among others, for AD and GIT traits. Our findings provide genetic insights into the gut-brain relationship, implicating shared but non-causal genetic susceptibility of GIT disorders with AD's risk. Genes and biological pathways identified are potential targets for further investigation in AD, GIT disorders, and their comorbidity.

Elucidating the relationship between migraine risk and brain structure using genetic data.

Migraine is a highly common and debilitating disorder that often affects individuals in their most productive years of life. Previous studies have identified both genetic variants and brain morphometry differences associated with migraine risk. However, the relationship between migraine and brain morphometry has not been examined on a genetic level, and the causal nature of the association between brain structure and migraine risk has not been determined. Using the largest available genome-wide association studies to date, we examined the genome-wide genetic overlap between migraine and intracranial volume, as well as the regional volumes of nine subcortical brain structures. We further focused the identification and biological annotation of genetic overlap between migraine and each brain structure on specific regions of the genome shared between migraine and brain structure. Finally, we examined whether the size of any of the examined brain regions causally increased migraine risk using a Mendelian randomization approach. We observed a significant genome-wide negative genetic correlation between migraine risk and intracranial volume (rG = -0.11, P = 1 × 10-3) but not with any subcortical region. However, we identified jointly associated regional genomic overlap between migraine and every brain structure. Gene enrichment in these shared genomic regions pointed to possible links with neuronal signalling and vascular regulation. Finally, we provide evidence of a possible causal relationship between smaller total brain, hippocampal and ventral diencephalon volume and increased migraine risk, as well as a causal relationship between increased risk of migraine and a larger volume of the amygdala. We leveraged the power of large genome-wide association studies to show evidence of shared genetic pathways that jointly influence migraine risk and several brain structures, suggesting that altered brain morphometry in individuals with high migraine risk may be genetically mediated. Further interrogation of these results showed support for the neurovascular hypothesis of migraine aetiology and shed light on potentially viable therapeutic targets.

Integrative multi-omic analysis identifies genetically influenced DNA methylation biomarkers for breast and prostate cancers.

Aberrant DNA methylation has emerged as a hallmark in several cancers and contributes to risk, oncogenesis, progression, and prognosis. In this study, we performed imputation-based and conventional methylome-wide association analyses for breast cancer (BrCa) and prostate cancer (PrCa). The imputation-based approach identified DNA methylation at cytosine-phosphate-guanine sites (CpGs) associated with BrCa and PrCa risk utilising genome-wide association summary statistics (NBrCa = 228,951, NPrCa = 140,254) and prebuilt methylation prediction models, while the conventional approach identified CpG associations utilising TCGA and GEO experimental methylation data (NBrCa = 621, NPrCa = 241). Enrichment analysis of the association results implicated 77 and 81 genetically influenced CpGs for BrCa and PrCa, respectively. Furthermore, analysis of differential gene expression around these CpGs suggests a genome-epigenome-transcriptome mechanistic relationship. Conditional analyses identified multiple independent secondary SNP associations (Pcond < 0.05) around 28 BrCa and 22 PrCa CpGs. Cross-cancer analysis identified eight common CpGs, including a strong therapeutic target in SREBF1 (17p11.2)-a key player in lipid metabolism. These findings highlight the utility of integrative analysis of multi-omic cancer data to identify robust biomarkers and understand their regulatory effects on cancer risk.

Glucose-Related Traits and Risk of Migraine-A Potential Mechanism and Treatment Consideration.

Migraine and glucose-related (glycaemic) traits (fasting glucose, fasting insulin, and type 2 diabetes) are common and complex comorbid disorders that cause major economic and social burdens on patients and their families. Studies on the relationship between migraine and glucose-related traits have yielded inconsistent results. The purpose of this review is to synthesise and discuss the information from the available literature on the relationship between fasting glucose, fasting insulin, and type 2 diabetes (T2D) with migraine. Publications on migraine and fasting glucose, migraine and fasting insulin, and migraine and T2D were identified from a PubMed and Google Scholar database search and reviewed for this article. Multiple publications have suggested that the comorbidity of migraine and glucose-related traits may have a similar complex pathogenic mechanism, including impaired glucose homeostasis, insulin resistance, reduced cerebrovascular reactivity, abnormal brain metabolism, shared genetic factors, neurotransmitters, and sex hormones. Furthermore, several studies have found a bi-directional link between migraine with insulin resistance and T2D. There is strong evidence for a biological association between migraine headache and glucose-related traits, and burgeoning evidence for shared genetic influences. Therefore, genetic research into these comorbid traits has the potential to identify new biomarkers and therapeutic targets and provide biological insight into their relationships. We encourage healthcare professionals to consider the co-occurrence of migraine with glucose-related traits in the evaluation and treatment of their patients.

Genetic analyses identify pleiotropy and causality for blood proteins and highlight Wnt/ß-catenin signalling in migraine.

Migraine is a common complex disorder with a significant polygenic SNP heritability ([Formula: see text]). Here we utilise genome-wide association study (GWAS) summary statistics to study pleiotropy between blood proteins and migraine under the polygenic model. We estimate [Formula: see text] for 4625 blood protein GWASs and identify 325 unique proteins with a significant [Formula: see text] for use in subsequent genetic analyses. Pleiotropy analyses link 58 blood proteins to migraine risk at genome-wide, gene and/or single-nucleotide polymorphism levels-suggesting shared genetic influences or causal relationships. Notably, the identified proteins are largely distinct from migraine GWAS loci. We show that higher levels of DKK1 and PDGFB, and lower levels of FARS2, GSTA4 and CHIC2 proteins have a significant causal effect on migraine. The risk-increasing effect of DKK1 is particularly interesting-indicating a role for downregulation of ß-catenin-dependent Wnt signalling in migraine risk, suggesting Wnt activators that restore Wnt/ß-catenin signalling in brain could represent therapeutic tools against migraine.

Genome-wide imputed differential expression enrichment analysis identifies trait-relevant tissues.

The identification of pathogenically-relevant genes and tissues for complex traits can be a difficult task. We developed an approach named genome-wide imputed differential expression enrichment (GIDEE), to prioritise trait-relevant tissues by combining genome-wide association study (GWAS) summary statistic data with tissue-specific expression quantitative trait loci (eQTL) data from 49 GTEx tissues. Our GIDEE approach analyses robustly imputed gene expression and tests for enrichment of differentially expressed genes in each tissue. Two tests (mean squared z-score and empirical Brown's method) utilise the full distribution of differential expression p-values across all genes, while two binomial tests assess the proportion of genes with tissue-wide significant differential expression. GIDEE was applied to nine training datasets with known trait-relevant tissues and ranked 49 GTEx tissues using the individual and combined enrichment tests. The best-performing enrichment test produced an average rank of 1.55 out of 49 for the known trait-relevant tissue across the nine training datasets-ranking the correct tissue first five times, second three times, and third once. Subsequent application of the GIDEE approach to 20 test datasets-whose pathogenic tissues or cell types are uncertain or unknown-provided important prioritisation of tissues relevant to the trait's regulatory architecture. GIDEE prioritisation may thus help identify both pathogenic tissues and suitable proxy tissue/cell models (e.g., using enriched tissues/cells that are more easily accessible). The application of our GIDEE approach to GWAS datasets will facilitate follow-up in silico and in vitro research to determine the functional consequence(s) of their risk loci.

Cross-Trait Genetic Analyses Indicate Pleiotropy and Complex Causal Relationships between Headache and Thyroid Function Traits.

Epidemiological studies have reported a comorbid relationship between headache and thyroid traits; however, little is known about the shared genetics and causality that contributes to this association. We investigated the genetic overlap and associations between headache and thyroid function traits using genome-wide association study (GWAS) data. We found a significant genetic correlation (rg) with headache and hypothyroidism (rg = 0.09, p = 2.00 × 10−4), free thyroxine (fT4) (rg = 0.08, p = 5.50 × 10−3), and hyperthyroidism (rg = −0.14, p = 1.80 × 10−3), a near significant genetic correlation with secondary hypothyroidism (rg = 0.20, p = 5.24 × 10−2), but not with thyroid stimulating hormone (TSH). Pairwise-GWAS analysis revealed six, 14, four and five shared (pleiotropic) loci with headache and hypothyroidism, hyperthyroidism, secondary hypothyroidism, and fT4, respectively. Cross-trait GWAS meta-analysis identified novel genome-wide significant loci for headache: five with hypothyroidism, three with secondary hypothyroidism, 12 with TSH, and nine with fT4. Of the genes at these loci, six (FAF1, TMX2-CTNND1, AARSD1, PLCD3, ZNF652, and C20orf203; headache-TSH) and six (HMGB1P45, RPL30P1, ZNF462, TMX2-CTNND1, ITPK1, SECISBP2L; headache-fT4) were significant in our gene-based analysis (pFisher's combined p-value < 2.09 × 10−6). Our causal analysis suggested a positive causal relationship between headache and secondary hypothyroidism (p = 3.64 × 10−4). The results also suggest a positive causal relationship between hypothyroidism and headache (p = 2.45 × 10−3) and a negative causal relationship between hyperthyroidism and headache (p = 1.16 × 10−13). These findings suggest a strong evidence base for a genetic correlation and complex causal relationships between headache and thyroid traits.

Genetic overlap and causality between blood metabolites and migraine.

The availability of genome-wide association studies (GWASs) for human blood metabolome provides an excellent opportunity for studying metabolism in a heritable disease such as migraine. Utilizing GWAS summary statistics, we conduct comprehensive pairwise genetic analyses to estimate polygenic genetic overlap and causality between 316 unique blood metabolite levels and migraine risk. We find significant genome-wide genetic overlap between migraine and 44 metabolites, mostly lipid and organic acid metabolic traits (FDR < 0.05). We also identify 36 metabolites, mostly related to lipoproteins, that have shared genetic influences with migraine at eight independent genomic loci (posterior probability > 0.9) across chromosomes 3, 5, 6, 9, and 16. The observed relationships between genetic factors influencing blood metabolite levels and genetic risk for migraine suggest an alteration of metabolite levels in individuals with migraine. Our analyses suggest higher levels of fatty acids, except docosahexaenoic acid (DHA), a very long-chain omega-3, in individuals with migraine. Consistently, we found a causally protective role for a longer length of fatty acids against migraine. We also identified a causal effect for a higher level of a lysophosphatidylethanolamine, LPE(20:4), on migraine, thus introducing LPE(20:4) as a potential therapeutic target for migraine.

Polygenic Risk Scores Derived From Varying Definitions of Depression and Risk of Depression.

Importance: Genetic studies with broad definitions of depression may not capture genetic risk specific to major depressive disorder (MDD), raising questions about how depression should be operationalized in future genetic studies. Objective: To use a large, well-phenotyped single study of MDD to investigate how different definitions of depression used in genetic studies are associated with estimation of MDD and phenotypes of MDD, using polygenic risk scores (PRSs). Design, Setting, and Participants: In this case-control polygenic risk score analysis, patients meeting diagnostic criteria for a diagnosis of MDD were drawn from the Australian Genetics of Depression Study, a cross-sectional, population-based study of depression, and controls and patients with self-reported depression were drawn from QSkin, a population-based cohort study. Data analyzed herein were collected before September 2018, and data analysis was conducted from September 10, 2020, to January 27, 2021. Main Outcome and Measures: Polygenic risk scores generated from genome-wide association studies using different definitions of depression were evaluated for estimation of MDD in and within individuals with MDD for an association with age at onset, adverse childhood experiences, comorbid psychiatric and somatic disorders, and current physical and mental health. Results: Participants included 12 106 (71% female; mean age, 42.3 years; range, 18-88 years) patients meeting criteria for MDD and 12 621 (55% female; mean age, 60.9 years; range, 43-87 years) control participants with no history of psychiatric disorders. The effect size of the PRS was proportional to the discovery sample size, with the largest study having the largest effect size with the odds ratio for MDD (1.75; 95% CI, 1.73-1.77) per SD of PRS and the PRS derived from ICD-10 codes documented in hospitalization records in a population health cohort having the lowest odds ratio (1.14; 95% CI, 1.12-1.16). When accounting for differences in sample size, the PRS from a genome-wide association study of patients meeting diagnostic criteria for MDD and control participants was the best estimator of MDD, but not in those with self-reported depression, and associations with higher odds ratios with childhood adverse experiences and measures of somatic distress. Conclusions and Relevance: These findings suggest that increasing sample sizes, regardless of the depth of phenotyping, may be most informative for estimating risk of depression. The next generation of genome-wide association studies should, like the Australian Genetics of Depression Study, have both large sample sizes and extensive phenotyping to capture genetic risk factors for MDD not identified by other definitions of depression.

Genetic analyses of gynecological disease identify genetic relationships between uterine fibroids and endometrial cancer, and a novel endometrial cancer genetic risk region at the WNT4 1p36.12 locus.

Endometriosis, polycystic ovary syndrome (PCOS) and uterine fibroids have been proposed as endometrial cancer risk factors; however, disentangling their relationships with endometrial cancer is complicated due to shared risk factors and comorbidities. Using genome-wide association study (GWAS) data, we explored the relationships between these non-cancerous gynecological diseases and endometrial cancer risk by assessing genetic correlation, causal relationships and shared risk loci. We found significant genetic correlation between endometrial cancer and PCOS, and uterine fibroids. Adjustment for genetically predicted body mass index (a risk factor for PCOS, uterine fibroids and endometrial cancer) substantially attenuated the genetic correlation between endometrial cancer and PCOS but did not affect the correlation with uterine fibroids. Mendelian randomization analyses suggested a causal relationship between only uterine fibroids and endometrial cancer. Gene-based analyses revealed risk regions shared between endometrial cancer and endometriosis, and uterine fibroids. Multi-trait GWAS analysis of endometrial cancer and the genetically correlated gynecological diseases identified a novel genome-wide significant endometrial cancer risk locus at 1p36.12, which replicated in an independent endometrial cancer dataset. Interrogation of functional genomic data at 1p36.12 revealed biologically relevant genes, including WNT4 which is necessary for the development of the female reproductive system. In summary, our study provides genetic evidence for a causal relationship between uterine fibroids and endometrial cancer. It further provides evidence that the comorbidity of endometrial cancer, PCOS and uterine fibroids may partly be due to shared genetic architecture. Notably, this shared architecture has revealed a novel genome-wide risk locus for endometrial cancer.