Genomics of human longevity

In animal models, single-gene mutations in genes involved in insulin/IGF and target of rapamycin signalling pathways extend lifespan to a considerable extent. The genetic, genomic and epigenetic influences on human longevity are expected to be much more complex. Strikingly however, beneficial metabolic and cellular features of long-lived families resemble those in animals for whom the lifespan is extended by applying genetic manipulation and, especially, dietary restriction. Candidate gene studies in humans support the notion that human orthologues from longevity genes identified in lower species do contribute to longevity but that the influence of the genetic variants involved is small. Here we discuss how an integration of novel study designs, labour-intensive biobanking, deep phenotyping and genomic research may provide insights into the mechanisms that drive human longevity and healthy ageing, beyond the associations usually provided by molecular and genetic epidemiology. Although prospective studies of humans from the cradle to the grave have never been performed, it is feasible to extract life histories from different cohorts jointly covering the molecular changes that occur with age from early development all the way up to the age at death. By the integration of research in different study cohorts, and with research in animal models, biological research into human longevity is thus making considerable progress

Assessing cognition and daily function in early dementia using the cognitive-functional composite:findings from the Catch-Cog study cohort

BackgroundThe cognitive-functional composite (CFC) was designed to improve the measurement of clinically relevant changes in predementia and early dementia stages. We have previously demonstrated its good test-retest reliability and feasibility of use. The current study aimed to evaluate several quality aspects of the CFC, including construct validity, clinical relevance, and suitability for the target population.MethodsBaseline data of the Capturing Changes in Cognition study was used: an international, prospective cohort study including participants with subjective cognitive decline (SCD), mild cognitive impairment (MCI), Alzheimer's disease (AD) dementia, and dementia with Lewy bodies (DLB). The CFC comprises seven existing cognitive tests focusing on memory and executive functions (EF) and the informant-based Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q). Construct validity and clinical relevance were assessed by (1) confirmatory factor analyses (CFA) using all CFC subtests and (2) linear regression analyses relating the CFC score (independent) to reference measures of disease severity (dependent), correcting for age, sex, and education. To assess the suitability for the target population, we compared score distributions of the CFC to those of traditional tests (Alzheimer's Disease Assessment Scale-Cognitive subscale, Alzheimer's Disease Cooperative Study-Activities of Daily Living scale, and Clinical Dementia Rating scale).ResultsA total of 184 participants were included (age 71.88.4; 42% female; n=14 SCD, n=80 MCI, n=78AD, and n=12 DLB). CFA showed that the hypothesized three-factor model (memory, EF, and IADL) had adequate fit (CFI=.931, RMSEA=.091, SRMR=.06). Moreover, worse CFC performance was associated with more cognitive decline as reported by the informant (=.61, p</p

CAPICE:a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice.

Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

Background: Epigenetic change is a hallmark of ageing but its link to ageing mechanisms in humans remains poorly understood. While DNA methylation at many CpG sites closely tracks chronological age, DNA methylation changes relevant to biological age are expected to gradually dissociate from chronological age, mirroring the increased heterogeneity in health status at older ages. Results: Here, we report on the large-scale identification of 6366 age-related variably methylated positions (aVMPs) identified in 3295 whole blood DNA methylation profiles, 2044 of which have a matching RNA-seq gene expression profile. aVMPs are enriched at polycomb repressed regions and, accordingly, methylation at those positions is associated with the expression of genes encoding components of polycomb repressive complex 2 (PRC2) in trans. Further analysis revealed trans-associations for 1816 aVMPs with an additional 854 genes. These trans-associated aVMPs are characterized by either an age-related

Refined mapping of autoimmune disease associated genetic variants with gene expression suggests an important role for non-coding RNAs

Genome-wide association and fine-mapping studies in 14 autoimmune diseases (AID) have implicated more than 250 loci in one or more of these diseases. As more than 90% of AID-associated SNPs are intergenic or intronic, pinpointing the causal genes is challenging. We performed a systematic analysis to link 460 SNPs that are associated with 14 AID to causal genes using transcriptomic data from 629 blood samples. We were able to link 71 (39%) of the AID-SNPs to two or more nearby genes, providing evidence that for part of the AID loci multiple causal genes exist. While 54 of the AID loci are shared by one or more AID, 17% of them do not share candidate causal genes. In addition to finding novel genes such as ULK3, we also implicate novel disease mechanisms and pathways like autophagy in celiac disease pathogenesis. Furthermore, 42 of the AID SNPs specifically affected the expression of 53 non-coding RNA genes. To further understand how the non-coding genome contributes to AID, the SNPs were linked to functional regulatory elements, which suggest a model where AID genes are regulated by network of chromatin looping/non-coding RNAs interactions. The looping model also explains how a causal candidate gene is not necessarily the gene closest to the AID SNP, which was the case in nearly 50% of cases. (C) 2016 The Authors. Published by Elsevier Ltd.</p

TAB2 deletions and variants cause a highly recognisable syndrome with mitral valve disease, cardiomyopathy, short stature and hypermobility

Deletions that include the gene TAB2 and TAB2 loss-of-function variants have previously been associated with congenital heart defects and cardiomyopathy. However, other features, including short stature, facial dysmorphisms, connective tissue abnormalities and a variable degree of developmental delay, have only been mentioned occasionally in literature and thus far not linked to TAB2. In a large-scale, social media-based chromosome 6 study, we observed a shared phenotype in patients with a 6q25.1 deletion that includes TAB2. To confirm if this phenotype is caused by haploinsufficiency of TAB2 and to delineate a TAB2-related phenotype, we subsequently sequenced TAB2 in patients with matching phenotypes and recruited patients with pathogenic TAB2 variants detected by exome sequencing. This identified 11 patients with a deletion containing TAB2 (size 1.68-14.31 Mb) and 14 patients from six families with novel truncating TAB2 variants. Twenty (80%) patients had cardiac disease, often mitral valve defects and/or cardiomyopathy, 18 (72%) had short stature and 18 (72%) had hypermobility. Twenty patients (80%) had facial features suggestive for Noonan syndrome. No substantial phenotypic differences were noted between patients with deletions and those with intragenic variants. We then compared our patients to 45 patients from the literature. All literature patients had cardiac diseases, but syndromic features were reported infrequently. Our study shows that the phenotype in 6q25.1 deletions is caused by haploinsufficiency of TAB2 and that TAB2 is associated not just with cardiac disease, but also with a distinct phenotype, with features overlapping with Noonan syndrome. We propose the name "TAB2-related syndrome"

Autosomal genetic variation is associated with DNA methylation in regions variably escaping X-chromosome inactivation

This is the final version of the article. Available from Springer Nature via the DOI in this record.Raw data were submitted to the European Genome-phenome Archive (EGA) under accession EGAS00001001077.X-chromosome inactivation (XCI), i.e., the inactivation of one of the female X chromosomes, restores equal expression of X-chromosomal genes between females and males. However, ~10% of genes show variable degrees of escape from XCI between females, although little is known about the causes of variable XCI. Using a discovery data-set of 1867 females and 1398 males and a replication sample of 3351 females, we show that genetic variation at three autosomal loci is associated with female-specific changes in X-chromosome methylation. Through cis-eQTL expression analysis, we map these loci to the genes SMCHD1/METTL4, TRIM6/HBG2, and ZSCAN9. Low-expression alleles of the loci are predominantly associated with mild hypomethylation of CpG islands near genes known to variably escape XCI, implicating the autosomal genes in variable XCI. Together, these results suggest a genetic basis for variable escape from XCI and highlight the potential of a population genomics approach to identify genes involved in XCI.This research was financially supported by several institutions: BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO, numbers 184.021.007 and 184.033.111); the UK Medical Research Council; Wellcome (www.wellcome.ac.uk; [grant number 102215/2/13/2 to ALSPAC]); the University of Bristol to ALSPAC; the UK Economic and Social Research Council (www.esrc.ac.uk; [ES/N000498/1] to CR); the UK Medical Research Council (www.mrc.ac.uk; grant numbers [MC_UU_12013/1, MC_UU_12013/2 to JLM, CR]); the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria; the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ; the Wellcome Trust, Medical Research Council, European Union (EU), and the National Institute for Health Research (NIHR)- funded BioResource, Clinical Research Facility, and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London

Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA

Genome-wide association studies have identified numerous loci linked with complex diseases, for which the molecular mechanisms remain largely unclear. Comprehensive molecular profiling of circulating metabolites captures highly heritable traits, which can help to uncover metabolic pathophysiology underlying established disease variants. We conduct an extended genome-wide association study of genetic influences on 123 circulating metabolic traits quantified by nuclear magnetic resonance metabolomics from up to 24,925 individuals and identify eight novel loci for amino acids, pyruvate and fatty acids. The LPA locus link with cardiovascular risk exemplifies how detailed metabolic profiling may inform underlying aetiology via extensive associations with very-low-density lipoprotein and triglyceride metabolism. Genetic fine mapping and Mendelian randomization uncover wide-spread causal effects of lipoprotein(a) on overall lipoprotein metabolism and we assess potential pleiotropic consequences of genetically elevated lipoprotein(a) on diverse morbidities via electronic health-care records. Our findings strengthen the argument for safe LPA-targeted intervention to reduce cardiovascular risk