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.

Association and genetic overlap between clinical chemistry tests and migraine.

INTRODUCTION: In this paper, we studied several serum clinical chemistry tests of cardiovascular disease (CVD), iron deficiency anemia, liver and kidney disorders in migraine. METHODS: We first explored the association of 22 clinical chemistry tests with migraine risk in 697 migraine patients and 2722 controls. To validate and interpret association findings, cross-trait genetic analyses were conducted utilising genome-wide association study (GWAS) data comprising 23,986 to 452,264 individuals. RESULTS: Significant associations with migraine risk were identified for biomarkers of CVD risk, iron deficiency and liver dysfunction (odds ratios = 0.86-1.21; 1 × 10-4 < p < 3 × 10-2). Results from cross-trait genetic analyses corroborate the significant biomarker associations and indicate their relationship with migraine is more consistent with biological pleiotropy than causality. For example, association and genetic overlap between a lower level of HDL-C and increased migraine risk are due to shared biology rather than a causal relationship. Furthermore, additional genetic analyses revealed shared genetics among migraine, the clinical chemistry tests, and heart problems and iron deficiency anemia, but not liver disease. CONCLUSIONS: These findings highlight common biological mechanisms underlying migraine, heart problems and iron deficiency anemia and provide support for their investigation in the development of novel therapeutic and dietary interventions.

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.

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.

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.