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Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits. The Finnish population is enriched for genetic variants which are rare in other populations. Here, the authors find new genetic loci associated with 1391 circulating metabolites in 6136 Finnish men, demonstrating that metabolite genetic associations can help elucidate disease mechanisms.

Polygenic risk scores (PRSs) have shown promise in predicting susceptibility to common diseases 1 – 3 . We estimated their added value in clinical risk prediction of five common diseases, using large-scale biobank data (FinnGen; n  = 135,300) and the FINRISK study with clinical risk factors to test genome-wide PRSs for coronary heart disease, type 2 diabetes, atrial fibrillation, breast cancer and prostate cancer. We evaluated the lifetime risk at different PRS levels, and the impact on disease onset and on prediction together with clinical risk scores. Compared to having an average PRS, having a high PRS contributed 21% to 38% higher lifetime risk, and 4 to 9 years earlier disease onset. PRSs improved model discrimination over age and sex in type 2 diabetes, atrial fibrillation, breast cancer and prostate cancer, and over clinical risk in type 2 diabetes, breast cancer and prostate cancer. In all diseases, PRSs improved reclassification over clinical thresholds, with the largest net reclassification improvements for early-onset coronary heart disease, atrial fibrillation and prostate cancer. This study provides evidence for the additional value of PRSs in clinical disease prediction. The practical applications of polygenic risk information for stratified screening or for guiding lifestyle and medical interventions in the clinical setting remain to be defined in further studies. In a large and prospective cohort, higher polygenic risk is associated with higher risk and earlier age of onset for cardiometabolic disorders and cancer, and has added value to clinical risk scores in clinical disease prediction.

Clonal hematopoiesis driven by somatic heterozygous TET2 loss is linked to malignant degeneration via consequent aberrant DNA methylation, and possibly to cardiovascular disease via increased cytokine and chemokine expression as reported in mice. Here, we discover a germline TET2 mutation in a lymphoma family. We observe neither unusual predisposition to atherosclerosis nor abnormal pro-inflammatory cytokine or chemokine expression. The latter finding is confirmed in cells from three additional unrelated TET2 germline mutation carriers. The TET2 defect elevates blood DNA methylation levels, especially at active enhancers and cell-type specific regulatory regions with binding sequences of master transcription factors involved in hematopoiesis. The regions display reduced methylation relative to all open chromatin regions in four DNMT3A germline mutation carriers, potentially due to TET2-mediated oxidation. Our findings provide insight into the interplay between epigenetic modulators and transcription factor activity in hematological neoplasia, but do not confirm the putative role of TET2 in atherosclerosis. Somatic heterozygous TET2 loss drives clonal hematopoiesis, which is linked to malignant cell degeneration and potentially cardiovascular disease. Here, the authors investigate the molecular impact of a germline TET2 mutation in a lymphoma family, finding elevated blood DNA methylation levels and no predisposition to atherosclerosis

Migraine is a highly prevalent neurovascular disorder for which genome-wide association studies (GWAS) have identified over one hundred risk loci, yet the causal variants and genes remain mostly unknown. Here, we meta-analyze three migraine GWAS including 98,374 cases and 869,160 controls and identify 122 independent risk loci of which 35 were new. Fine-mapping of a meta-analysis is challenging because some variants may be missing from some participating studies and accurate linkage disequilibrium (LD) information of the variants is often not available. Here, using the exact in-sample LD, we first investigate which statistics could reliably capture the quality of fine-mapping when only reference LD is available. We observe that the posterior expected number of causal variants best distinguishes between the high- and low-quality results. Next, we perform fine-mapping for 102 autosomal risk regions using FINEMAP. We produce high-quality fine-mapping for 93 regions and define 181 distinct credible sets. Among the high-quality credible sets are 7 variants with very high posterior inclusion probability (PIP > 0.9) and 2 missense variants with PIP > 0.5 (rs6330 in NGF and rs1133400 in INPP5A ). For 35 association signals, we manage to narrow down the set of potential risk variants to at most 5 variants. A large-scale migraine genetics study identifies 122 risk regions, including 35 previously unreported ones, and pinpoints likely causal genetic variants using an in silico fine-mapping method.

Drug candidates supported by genetic evidence are more likely to succeed in clinical trials, with genome-wide association studies (GWAS) providing a key source of such evidence. Standard GWAS approaches assume additive effects of alleles on the phenotype, but non-additive models have also successfully identified novel associations across various traits. Despite their potential, the large-scale application of non-additive GWAS across thousands of phenotypes in biobanks has been limited by high computational costs. To address this challenge, we present a method that leverages the correlation between additive and non-additive p -values to prioritize variants likely to reach genome-wide significance in non-additive analyses. Applied to the FinnGen dataset comprising 500,349 individuals and 2329 phenotypes, this method reduces computational costs by three orders of magnitude while retaining nearly all true non-additive associations, identifying 781 novel loci missed by additive GWAS. We report fine-mapping and colocalization with 571 datasets for novel loci, uncovering likely causal variants and potential insights into biological mechanisms. The authors introduce a cost-efficient, non-additive genome-wide association study pipeline that, when applied to a dominant analysis of 2329 phenotypes in 500,349 individuals, reduced computational costs from $27,000 to $39 and identified 781 new associations.

Despite evidence that genetic factors contribute to the duration of gestation and the risk of preterm birth, robust associations with genetic variants have not been identified. We used large data sets that included the gestational duration to determine possible genetic associations. We performed a genomewide association study in a discovery set of samples obtained from 43,568 women of European ancestry using gestational duration as a continuous trait and term or preterm (<37 weeks) birth as a dichotomous outcome. We used samples from three Nordic data sets (involving a total of 8643 women) to test for replication of genomic loci that had significant genomewide association (P<5.0×10 ) or an association with suggestive significance (P<1.0×10 ) in the discovery set. In the discovery and replication data sets, four loci (EBF1, EEFSEC, AGTR2, and WNT4) were significantly associated with gestational duration. Functional analysis showed that an implicated variant in WNT4 alters the binding of the estrogen receptor. The association between variants in ADCY5 and RAP2C and gestational duration had suggestive significance in the discovery set and significant evidence of association in the replication sets; these variants also showed genomewide significance in a joint analysis. Common variants in EBF1, EEFSEC, and AGTR2 showed association with preterm birth with genomewide significance. An analysis of mother-infant dyads suggested that these variants act at the level of the maternal genome. In this genomewide association study, we found that variants at the EBF1, EEFSEC, AGTR2, WNT4, ADCY5, and RAP2C loci were associated with gestational duration and variants at the EBF1, EEFSEC, and AGTR2 loci with preterm birth. Previously established roles of these genes in uterine development, maternal nutrition, and vascular control support their mechanistic involvement. (Funded by the March of Dimes and others.).

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acidsubstituting missense variations on protein structure and function being especially challenging. Here we characterize the three-dimensional (3D) amino acid positions affected in pathogenic and population variants from 1,330 disease-associated genes using over 14,000 experimentally solved human protein structures. By measuring the statistical burden of variations (i.e., point mutations) from all genes on 40 3D protein features, accounting for the structural, chemical, and functional context of the variations’ positions, we identify features that are generally associated with pathogenic and population missense variants. We then perform the same amino acid-level analysis individually for 24 protein functional classes, which reveals unique characteristics of the positions of the altered amino acids: We observe up to 46% divergence of the class-specific features from the general characteristics obtained by the analysis on all genes, which is consistent with the structural diversity of essential regions across different protein classes. We demonstrate that the function-specific 3D features of the variants match the readouts of mutagenesis experiments for BRCA1 and PTEN, and positively correlate with an independent set of clinically interpreted pathogenic and benign missense variants. Finally, we make our results available through a web server to foster accessibility and downstream research. Our findings represent a crucial step toward translational genetics, from highlighting the impact of mutations on protein structure to rationalizing the variants’ pathogenicity in terms of the perturbed molecular mechanisms.

Genetic imputation is a cost-efficient way to improve the power and resolution of genome-wide association (GWA) studies. Current publicly accessible imputation reference panels accurately predict genotypes for common variants with minor allele frequency (MAF)≥5% and low-frequency variants (0.5≤MAF<5%) across diverse populations, but the imputation of rare variation (MAF<0.5%) is still rather limited. In the current study, we evaluate imputation accuracy achieved with reference panels from diverse populations with a population-specific high-coverage (30 ×) whole-genome sequencing (WGS) based reference panel, comprising of 2244 Estonian individuals (0.25% of adult Estonians). Although the Estonian-specific panel contains fewer haplotypes and variants, the imputation confidence and accuracy of imputed low-frequency and rare variants was significantly higher. The results indicate the utility of population-specific reference panels for human genetic studies.

Suicide is a worldwide health crisis. We aimed to identify genetic risk variants associated with suicide death and suicidal behavior. Meta-analysis for suicide death was performed using 3765 cases from Utah and matching 6572 controls of European ancestry. Meta-analysis for suicidal behavior using data across five cohorts ( n  = 8315 cases and 256,478 psychiatric or populational controls of European ancestry) was also performed. One locus in neuroligin 1 ( NLGN1 ) passing the genome-wide significance threshold for suicide death was identified (top SNP rs73182688, with p  = 5.48 × 10 −8 before and p  = 4.55 × 10 −8 after mtCOJO analysis conditioning on MDD to remove genetic effects on suicide mediated by MDD). Conditioning on suicidal attempts did not significantly change the association strength ( p  = 6.02 × 10 −8 ), suggesting suicide death specificity. NLGN1 encodes a member of a family of neuronal cell surface proteins. Members of this family act as splice site-specific ligands for beta-neurexins and may be involved in synaptogenesis. The NRXN-NLGN pathway was previously implicated in suicide, autism, and schizophrenia. We additionally identified ROBO2 and ZNF28 associations with suicidal behavior in the meta-analysis across five cohorts in gene-based association analysis using MAGMA. Lastly, we replicated two loci including variants near SOX5 and LOC101928519 associated with suicidal attempts identified in the ISGC and MVP meta-analysis using the independent FinnGen samples. Suicide death and suicidal behavior showed positive genetic correlations with depression, schizophrenia, pain, and suicidal attempt, and negative genetic correlation with educational attainment. These correlations remained significant after conditioning on depression, suggesting pleiotropic effects among these traits. Bidirectional generalized summary-data-based Mendelian randomization analysis suggests that genetic risk for the suicidal attempt and suicide death are both bi-directionally causal for MDD.

A coding mutation in APP , the gene that encodes the amyloid-β precursor protein, is found to protect against Alzheimer’s disease and cognitive decline in the elderly without Alzheimer’s disease. 'Natural' protection against Alzheimer's disease Alzheimer's disease is characterized by the existence in the brain of amyloid plaques, which form as a consequence of proteolic cleavage of amyloid precursor protein (APP). By screening almost 2,000 genomes, Kari Stefansson and colleagues find a coding mutation in the APP gene that protects against Alzheimer's disease and cognitive decline in elderly people who lack symptoms of Alzheimer's disease. The mutation causes an approximately 40% reduction in the formation of amyloidogenic peptides in vitro . The strong protective effect of this mutation, which lies next to the aspartyl protease beta-site in APP, provides support for the hypothesis that reducing the beta-cleavage of APP may protect against Alzheimer's. The results also raise the possibility that Alzheimer's disease and cognitive decline in the elderly are mechanistically related. The prevalence of dementia in the Western world in people over the age of 60 has been estimated to be greater than 5%, about two-thirds of which are due to Alzheimer’s disease 1 , 2 , 3 , 4 . The age-specific prevalence of Alzheimer’s disease nearly doubles every 5 years after age 65, leading to a prevalence of greater than 25% in those over the age of 90 (ref. 3 ). Here, to search for low-frequency variants in the amyloid-β precursor protein ( APP ) gene with a significant effect on the risk of Alzheimer’s disease, we studied coding variants in APP in a set of whole-genome sequence data from 1,795 Icelanders. We found a coding mutation (A673T) in the APP gene that protects against Alzheimer’s disease and cognitive decline in the elderly without Alzheimer’s disease. This substitution is adjacent to the aspartyl protease β-site in APP, and results in an approximately 40% reduction in the formation of amyloidogenic peptides in vitro . The strong protective effect of the A673T substitution against Alzheimer’s disease provides proof of principle for the hypothesis that reducing the β-cleavage of APP may protect against the disease. Furthermore, as the A673T allele also protects against cognitive decline in the elderly without Alzheimer’s disease, the two may be mediated through the same or similar mechanisms.