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Post by Admin on May 15, 2021 19:57:41 GMT
In their blood: For their human longevity study, the researchers sequenced the genomes of 81 semi-supercentenarians and supercentenarians (people over the ages of 105 and 110, respectively). Aging versus long life: the molecules involved Investigators have found that the transcription factor DAF-16 controls the expression of a battery of genes, many of which have small effects on lifespan (promoting either aging or longevity) in Caenorhabditis elegans. Consistent with earlier studies, the pro-longevity genes include some that encode antioxidant enzymes and others that encode heat-shock proteins,which can restore misfolded proteins to their active conformations. Genes that promote ageing include some that encode yolk proteins, consistent with a link between ageing and reproduction. Another pro-ageing protein is the insulin-like INS-7, which, by binding to the insulin/IGF-1 receptor (DAF-2), may repress DAF-16 on the same and other cells. This suggests the presence of a positive feedback loop that regulates DAF-2 activity. Arrows indicate activation; T-bars indicate inhibition. They then compared them to the genomes of 36 healthy people with an average age of 68. "(P)eople in this younger age group tend to avoid many age-related diseases and therefore represent the best example of healthy aging," first author Paolo Garagnani of the University of Bologna explained in a press release. They discovered that the people in the 105+ group were more likely than those in the younger group to have five common genetic variants linked to more efficient DNA repair. When the researchers compared their results to a previous study involving more than 300 people over the age of 100, they saw the same variants in that group. Collecting mutations: Those genes were all variants inherited from their parents, but DNA can change throughout our lives. Some of those changes, known as somatic mutations, occur in specific cell lines throughout the body. Such mutations have been linked to aging1, so the researchers wanted to test their participants for them, too — specifically focusing on genes where the mutations have been linked to cancer2. DNA repair is one of the mechanisms allowing an extended lifespan in humans. CRISTINA GIULIANI When they did, they found that people in the older group — despite living for three or four more decades — had accumulated far fewer of the mutations than people in the younger group, for six of the seven genes tested. What it means: The genetic variants found in the older group indicate that some people are simply born with genes that make their bodies more efficient at DNA repair, and they may be more likely to live longer. However, the research doesn't prove why the people in the older group were less likely to accumulate somatic mutations — it may be the specific genes for DNA repair that they found, or it may be something else. Future studies are needed to tackle that question. Still, this study suggests that the body's ability to ward off cellular damage on its own plays a key role in human longevity, potentially offering new targets for future aging research. "Previous studies showed that DNA repair is one of the mechanisms allowing an extended lifespan across species," senior author Cristina Giuliani of the University of Bologna told New Atlas. "We showed that this is true also within humans." 1. genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1919-52. pubmed.ncbi.nlm.nih.gov/28667884/
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Post by Admin on May 16, 2021 2:44:10 GMT
Researchers from Osaka University have found that high blood pressure and obesity are the strongest factors reducing lifespan based on genetic and clinical information of 700,000 patients in the U.K., Finland and Japan. These findings could be exploited to develop novel medical treatments to improve population health. The genetic code within DNA has long been thought to determine whether one becomes sick or resists illnesses. DNA contains the information that makes all cells that compose our bodies and allows them to function. Part of DNA is composed of genes, of which proteins are produced that participate in virtually every process within our cells and organs. While variations in the genetic code determine biological traits, such as eye color, blood type, and risk for diseases, it is often a group of numerous variations with tiny effects that influence a phenotypic trait. Harnessing a huge amount of genetic and clinical data worldwide and a methodological breakthrough, it is now possible to identify individuals at several-fold increased risk of human diseases using genetic information. While a risk stratification based on genetic information could be one potential strategy to improve population health, a major challenge lies in that the genetic code itself cannot be modified even if there is a known increased risk of a particular disease. In a new study, researchers from Osaka University discovered that individuals who have a genetic susceptibility to certain traits, such as high blood pressure or obesity, have a shorter lifespan. "The genetic code contains a lot of information, most of it of unknown significance to us," says corresponding author of the study Yukinori Okada. "The goal of our study was to understand how we can utilize genetic information to discover risk factors for important health outcomes that we can directly influence as health care professionals." To achieve their goal, the researchers analyzed genetic and clinical information of 700,000 individuals from biobanks in the UK, Finland and Japan. From these data, the researchers calculated polygenic risk scores, which are an estimate of genetic susceptibility to a biological trait, such as a risk for disease, to find out which risk factor causally influences lifespan. "Biobanks are an incredible resource," says lead author of the study Saori Sakaue. "By collaborating with large biobanks in the UK, Finland and Japan, we not only had access to large amounts of data, but also to genetically diverse populations, both of which are necessary to make clinically meaningful conclusions." The researchers found that high blood pressure and obesity were the two strongest risk factors that reduced lifespan of the current generation. Interestingly, while high blood pressure decreased lifespan across all populations the researchers investigated, obesity significantly reduced lifespan in individuals with European ancestry, suggesting that the Japanese population was somehow protected from the detrimental effects obesity has on lifespan. "These are striking results that show how genetics can be used to predict health risks," says Okada. "Our findings could offer an approach to utilize genetic information to seek out health risk factors with the goal of providing targeted lifestyle changes and medical treatment. Ultimately, these approaches would be expected to improve the health of the overall population." The article, "Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan," has been published in Nature Medicine. Abstract While polygenic risk scores (PRSs) are poised to be translated into clinical practice through prediction of inborn health risks1, a strategy to utilize genetics to prioritize modifiable risk factors driving heath outcome is warranted2. To this end, we investigated the association of the genetic susceptibility to complex traits with human lifespan in collaboration with three worldwide biobanks (ntotal = 675,898; BioBank Japan (n = 179,066), UK Biobank (n = 361,194) and FinnGen (n = 135,638)). In contrast to observational studies, in which discerning the cause-and-effect can be difficult, PRSs could help to identify the driver biomarkers affecting human lifespan. A high systolic blood pressure PRS was trans-ethnically associated with a shorter lifespan (hazard ratio = 1.03[1.02–1.04], Pmeta = 3.9 × 10−13) and parental lifespan (hazard ratio = 1.06[1.06–1.07], P = 2.0 × 10−86). The obesity PRS showed distinct effects on lifespan in Japanese and European individuals (Pheterogeneity = 9.5 × 10−8 for BMI). The causal effect of blood pressure and obesity on lifespan was further supported by Mendelian randomization studies. Beyond genotype–phenotype associations, our trans-biobank study offers a new value of PRSs in prioritization of risk factors that could be potential targets of medical treatment to improve population health. More information: Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan, Nature Medicine (2020). DOI: 10.1038/s41591-020-0785-8 , nature.com/articles/s41591-020-0785-8
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Post by Admin on May 16, 2021 6:22:12 GMT
Whole-genome sequencing analysis of semi-supercentenarians Paolo Garagnani et al.
Abstract Extreme longevity is the paradigm of healthy aging as individuals who reached the extreme decades of human life avoided or largely postponed all major age-related diseases. In this study, we sequenced at high coverage (90X) the whole genome of 81 semi-supercentenarians and supercentenarians [105+/110+] (mean age: 106.6 ± 1.6) and of 36 healthy unrelated geographically matched controls (mean age 68.0 ± 5.9) recruited in Italy. The results showed that 105+/110+ are characterized by a peculiar genetic background associated with efficient DNA repair mechanisms, as evidenced by both germline data (common and rare variants) and somatic mutations patterns (lower mutation load if compared to younger healthy controls). Results were replicated in a second independent cohort of 333 Italian centenarians and 358 geographically matched controls. The genetics of 105+/110+ identified DNA repair and clonal haematopoiesis as crucial players for healthy aging and for the protection from cardiovascular events.
Introduction The study of healthy aging is of increasing importance since the phenomenon of human aging is inevitably linked to cumulative burden of age-associated diseases – such as cardiovascular disease (CVDs), stroke, type 2 diabetes, hypertension, different type of cancer, or dementia (Christensen et al., 2009; Franceschi and Bonafè, 2003). The geroscience perspective suggested to consider ageing as the major common risk factor for several chronic diseases and conditions (Kennedy et al., 2014). However few genetics studies followed this theory to elucidate the common mechanisms between aging and age-related diseases.
The geroscience approach may be applied to many diseases and many experimental designs. Here, we decided: (i) to select an informative model of extreme longevity; (ii) to use a whole genome sequencing at high coverage approach; (iii) to analyze the link with the genetic determinants of CVDs.
The study of human extreme longevity constitutes a model useful to assess the impact of genetic variability on this trait according to the following considerations. First, Sebastiani et al., 2016 showed that, considering individuals surviving to age 105 years, the relative risk of sibling surviving to 105 years is 35 times the chance of living to age 105 of the control population. These data suggest a more potent genetic contributions if samples are recruited in the last percentile of survival in accord with Tan et al., 2008 who reported that the power to detect association with longevity is greater for centenarians versus nonagenarians samples of the same birth cohort. Second, despite different definitions and opinion regarding the concept of healthy aging, the clinical and biochemical data on centenarians showed that they can be considered as a paradigm of healthy aging as they avoid or largely postpone all major age-related diseases (Andersen et al., 2012). Thus, healthy aging and exceptional longevity (people who live more than 100 years) are deeply related (Christensen and McGue, 2016).
Many approaches applied in the last decades to the study of the genetics of human longevity seem to have many limitations, as extensively described (Sebastiani et al., 2017). Heterogeneity of the groups – in terms of birth cohort and of population variability – seems to play the most problematic role when different cohorts and datasets are put together in order to increase statistical power. This approach identified genes and pathways important for longevity and healthy aging that are common between human populations, but at the same time misses the context, that is the ‘ecological’ dimension of healthy aging and longevity (Giuliani et al., 2017). In this view, the genetic determinants of longevity are dynamic and historically dependent (Giuliani et al., 2017; Giuliani et al., 2018a; Yashin et al., 2015) and, while the genetic determinants of longevity may be shared by different populations, population-specific genes are expected to play a major role (De Benedictis and Franceschi, 2006; Zeng et al., 2016).
From a technological point of view, the decreasing cost of genotyping arrays has allowed in-depth study of the genetic variability of common variants, using increasingly dense microarrays (>4M SNPs). However, whole genome sequencing (WGS) constitutes a major approach to study genomic variability of each individual (both in coding and noncoding regions). In the study of the genetics of human longevity, there are to date only few examples of WGS. The first studies were published in 2011 by Sebastiani and colleagues who characterized two supercentenarians, in 2014 by Gierman and colleagues Gierman et al., 2014 who published a study on 13 supercentenarians (110 years or older) and in 2014 considering 44 Ashkenazi Jewish centenarians (Freudenberg-Hua et al., 2014). These studies analyzed long-living people without considering a group of controls from the general population, thus reducing the number of potential new information which could be obtained. In 2016 Erickson and colleagues published a WGS paper on a high number of old individuals (N = 511, median age = 84.2 ± 9.3 years) whose health was assessed by self-reported data (‘Wellderly’) and 686 younger controls (median age = 33.3 years) (Erikson et al., 2016). However, despite the potential of the technological approach, the relative ‘young’ age of the elderly, the low number of centenarians and the limitations of the self-reported health status suggest that the possibility to identify the contribution of genetics to human longevity of this study was limited, as argued by Sebastiani et al., 2017.
CVDs constitute the first cause of death globally and many studies highlighted the intersection between CVDs and aging as cardiac and vascular aging are considered the major risk factor for CVDs. Many molecular mechanisms have been described as hallmarks of this process such as cellular senescence, genomic instability, chromatin remodeling, macromolecular damage and mitochondrial oxidative stress perturbed proteostasis, vascular and systemic chronic inflammation, among others (Furman et al., 2019). An emerging common mechanism between aging and CVD is the accumulation with age of somatic mutations. An age-related expansion of hematopoietic clones characterized by disruptive somatic mutations in few recurrent genes (such as DNMT3A, TET2, ASXL1, PPM1D, TP53), conferring to the mutated cells a selective proliferative advantage, has been described (Jaiswal et al., 2014). The expansion of such mutated clones (‘clonal hematopoiesis of indeterminate potential’, CHIP), has been associated to an acceleration of the atherosclerotic process, an increased risk of haematological malignancies (hazard ratio 11,1), ischemic stroke (hazard ratio 2,6), coronary heart disease (hazard ratio 2,0) and all-cause mortality (Jaiswal et al., 2014).
In this study, we generated and analyzed the first WGS data with high coverage (90X) in a cohort of 81 semi-supercentenarians and supercentenarians [105+/110+] (mean age: 106.6 ± 1.6) recruited across the entire Italian peninsula together with a control cohort of 36 healthy geographically matched individuals (Northern, Central, and Southern Italy) (mean age 68.0 ± 5.9). Data recently published (Giuliani et al., 2018b) with a second independent cohort of 333 centenarians (>100 years) and 358 geographically matched controls (Northern, Central, and Southern Italy) were used to replicate our results. In order to reduce the heterogeneity of the group we focused on the Italian peninsula as it has been fully characterized in term of genetic structure by different studies (Sazzini et al., 2016).
The aim of this study is to identify the genetic determinants of extreme longevity in humans focusing on common and rare variants analysis, 105+/110+ private mutations and somatic mutations, and determining polygenic risk score for cardiovascular diseases, the first cause of mortality in humans.
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Post by Admin on May 16, 2021 20:54:59 GMT
Results Design of the study and criteria for longevity definition To study the genetics of longevity, we selected a population of 105+/110+ (N = 81) with mean age of 106.6 years and born in a limited birth cohort range (1903–1909). We used the data produced by WGS in 105+/110+ as discovery data (Cohort 1) to have information about all the variants, and to maximize the probability to identify significant genetic association according to biological models. This design is supported by data showing that the power to detect association with longevity is greater for centenarians versus nonagenarians subjects of the same birth cohort (Tan et al., 2008). The choice of controls is an issue often debated in longevity studies. A number of unrelated samples from the general population matched for geographical origin has been included as control (N = 36). The prevalence of individuals that will become centenarians and semi-supercentenarians in the control group is negligible because of the rarity of the trait. To validate the results obtained in Cohort 1 (discovery phase), we used Cohort 2 (data produced by CoreExomeChip v.1.1 array Illumina 550 k, see details in Materials and methods) that is characterized by a high number of samples (333 centenarians and 358 controls). The design of Cohort 1 (discovery) and Cohort 2 (validation) is driven by the aim of this study, that is to investigate genetic determinants of extreme longevity as a paradigm of healthy aging. A geographical distribution of the samples selected for the discovery and an overview of the study design are reported in Figure 1A and B respectively. Figure 1C reports PCA plot described in detail in Materials and methods (considering all 117 individuals after quality controls), where individuals from Southern and Northern Italy have been indicated, and we validated that the discovery cohort follows the expected genetic structure (Raveane et al., 2019; Sazzini et al., 2016). Figure 1 Common variants : single variant analysis First, we performed a WGS association study adding sex as covariate and considering 5,511,852 common variants (MAF >5%). No association is observed at the genome-wide significance level (<5*10−8) (Figure 2A), but significant p-values after SLIDE correction (significance at adjusted p-value 10%) are observed for STK17A and COA1 genes. The uniform deflation observed in the QQ-plot (Figure 2B) could be due to the small sample size. Genomic inflation factor is 1.02. Figure 2 with 1 supplement Top association signals are located in the same large block of linkage disequilibrium at chromosome seven surrounding STK17A and COA1 genes and are reported in Table 1 (loci with significance at adjusted p-value 10% for the discovery). Technical validation with different technologies (Sequenom MassARRAY iPLEX) of the identified SNPs was performed considering a subset of 53 individuals from Cohort 1. Positions identified by comparing 105+/110+ and controls with unadjusted p-value<10−4 are reported in Supplementary file 1. Supplementary file 1 also includes the previous nominal p-values adjusted for PC1 and PC2.
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Post by Admin on May 17, 2021 2:24:49 GMT
Table 1 Common variants identified in the comparison between 105+/110+ and controls with significance at adjusted p-value 10%. Gene name, chromosome, position (GrCH 37/hg19), rs ID, minor allele (based on whole sample), estimated odds ratio for Cohort 1, lower/upper bound of 95% confidence interval for odds ratio, nominal … see more GENE NAME CHR BP dbSNP A1 OR L95 U95 P_unadj (Cohort1) P_adjusted SLIDE (Cohort1) F_105 (Cohort1) F_CTRL (Cohort1) P_unadj (Cohort2) STK17A 7 43637796 rs7456688 A 5.906 2.688 12.97 9.73*10−6 7.00*10−2 0.556 0.222 0.021 STK17A 7 43638009 rs10257700 C 5.906 2.688 12.97 9.73*10−6 7.00*10−2 0.556 0.222 0.029 STK17A 7 43643835 rs10279856 G 5.906 2.688 12.97 9.73*10−6 7.00*10−2 0.556 0.222 0.021 STK17A,COA1 7 43650221 rs69685881 A 5.906 2.688 12.97 9.73*10−6 7.00*10−2 0.556 0.222 0.025 STK17A,COA1 7 43651047 rs7805969 A 5.906 2.688 12.97 9.73*10−6 7.00*10−2 0.556 0.222 0.016 Given the presence of the well-known Italian genetic structure we decided to perform an association analysis comparing Northern 105+/110+ to Southern 105+/110+subjects. We next merged the identified signals (derived from the comparison 105+/110+ vs CTRL) with the p-values calculated comparing Northern 105+/110+ and Southern 105+/110+ subjects. All the identified signals described in Supplementary file 1 showed a non-significant p-values in the analysis based on geography. This indicates that the Italian genetic structure does not bias the signals identified. The five variants (rs7456688, rs10257700, rs10279856, rs69685881, and rs7805969) identified in Cohort 1 were evaluated for association with longevity in the validation Cohort 2. Cohort 2 includes 333 centenarians (mean age: 100.4 ± 1.4) and a group of 358 unrelated healthy individuals (controls; mean age: 60.7 ± 7.2), genotyped on the Illumina 550 k array platform. The analysis of Cohort 2 imputed genotypes (see Materials and methods) returned nominal p-values<0.05 for all the five variants, as reported in Table 1. Figure 2C reports the allele frequency of rs7456688-A in individuals from Cohort 1 (105+/110+ and CTRL) and Cohort 2 (100+ and CTRL), and of another cohort of healthy controls < 50 years (N = 392, mean age = 39.5 ± 7.2) already described in Giuliani et al., 2018b. The Tuscans data (TSI) reported in 1000 Genomes project is also included. The pattern follows a U-shaped with the highest allele frequency observed for 105+/110+, indicating the relevant role of the variant in extreme longevity. Regional plots (from chr7:43560257 to chr7:43938230) for the most significant regions identified by WGS in Cohort 1 and tested in Cohort 2 (validation cohort) are reported in Figure 3. rs2108078, located in chr7: 43861921, was the most significant SNP in this area (p-value=4,2*10−4) for the validation cohort. Figure 3 Next, we performed a gene-based association study by VEGAS2 including common variants (MAF >5%) in Cohort 1. A full list of all the 179 significant genes (p-values<0.01) is reported in Supplementary file 2, two of them (APOC3 and PPARGC1A) were already described in the GenAge database (http://genomics.senescence.info/genes/human.html), a database that includes genes identified in studies on human aging. Among the 179 genes identified in Cohort 1 (p-value<0.01, Supplementary file 2), eight were also significant in Cohort 2 (p-value<0.05). Fisher method was applied to calculate combined-p-values for STK17A, BLVRA, MYRF, DNAH7, LOC553103, PHF14, SLC22A4, TBRG4 (p-values=2.03*10−9, 1.35*10−7, 1.83*10−7, 0.00049, 0.00146, 0.00205, 0.00154, 0.00146, respectively) and STK17A gene is the gene with the strongest association signal in Cohort 1 (p-value=8.40*10−5) and Cohort 2 (p-value=1*10−6). LPPR1 was identified in our gene-based analysis of WGS data (VEGAS p-values=7.20*10−5) but was not significant in Cohort 2 (VEGAS p-values=0.19). We then performed a RiVIERA analysis – a tool for variant prioritization – on WGS data for inference of possible causal/regulatory variants considering SNPs located in the above-mentioned window (from chr7:43560257 to chr7:43938230) around the COA1 gene (Figure 4A). As reported in Figure 4A and SNPs, rs10279856 (in LD with the five previously reported SNPs), rs3779059, rs849166, rs849175 showed a credible score >0 (credible scores 0.261; 0.261; 0.240; 0.236 respectively). Figure 4 with 1 supplement These four SNPs (rs10279856, rs3779059, rs849166, and rs849175) were identified in GTEx as eQTL for STK17A and surrounding genes like COA1 and BLVRA as reported in Supplementary file 3. The genotypes the most frequent in centenarians (rs10279856-G reference allele and rs3779059-A, rs849166-A, rs849175-A alternative alleles) are associated to a reduced expression of COA1 gene in adipose (subcutaneous), artery - (aorta and tibial), artery - tibial, esophagus - mucosa, oesophagus - muscularis, nerve - tibial and skin, the same SNPs are associated to an increase in BLVRA expression in whole blood and a decrease of the expression of the same gene in artery (tibial) and oesophagus (mucosa). The same four SNPs are associated to an increase of SKT17A gene expression in heart (atrial and left ventricle), lung, nerve and thyroid. Longevity is a complex trait for which gene-environment interactions as well as the complex interplay of multiple genes and pathways play a major role (Zeng et al., 2016). Gene pathway analysis identified 24 KEGG pathways as significantly enriched (FDR ≤ 0.05) as reported in Figure 4B and in Supplementary file 4. Axon guidance, calcium signaling, glycine serine and threonine metabolism, long term potentiation, melanogenesis, PPAR signaling and taste transduction are among the most significant pathways identified. Pathway analysis performed considering GO and BioCarta were reported in Supplementary file 5 and Supplementary file 6. In the top ranking BioCarta pathways enrichment in inflammatory pathways (cytokines and inflammatory response) was observed (FDR value = 0.009).
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