1. Genetics and Genomics

Whole-genome sequencing analysis of semi-supercentenarians

  1. Paolo Garagnani  Is a corresponding author
  2. Julien Marquis
  3. Massimo Delledonne
  4. Chiara Pirazzini
  5. Elena Marasco
  6. Katarzyna Malgorzata Kwiatkowska
  7. Vincenzo Iannuzzi
  8. Maria Giulia Bacalini
  9. Armand Valsesia
  10. Jerome Carayol
  11. Frederic Raymond
  12. Alberto Ferrarini
  13. Luciano Xumerle
  14. Sebastiano Collino
  15. Daniela Mari
  16. Beatrice Arosio
  17. Martina Casati
  18. Evelyn Ferri
  19. Daniela Monti
  20. Benedetta Nacmias
  21. Sandro Sorbi
  22. Donata Luiselli
  23. Davide Pettener
  24. Gastone Castellani
  25. Claudia Sala
  26. Giuseppe Passarino
  27. Francesco De Rango
  28. Patrizia D'Aquila
  29. Luca Bertamini
  30. Nicola Martinelli
  31. Domenico Girelli
  32. Oliviero Olivieri
  33. Cristina Giuliani
  34. Patrick Descombes
  35. Claudio Franceschi
  1. Department of Experimental, Diagnostic, and Specialty Medicine (DIMES), University of Bologna, Italy
  2. Clinical Chemistry, Department of Laboratory Medicine, Karolinska Institutet at Huddinge University Hospital, Sweden
  3. Alma Mater Research Institute on Global Challenges and Climate Change (Alma Climate), University of Bologna, Italy
  4. Nestlé Research, Société des Produits Nestlé SA, Switzerland
  5. Functional Genomics Laboratory, Department of Biotechnology, University of Verona, Italy
  6. IRCCS Istituto delle Scienze Neurologiche di Bologna, Italy
  7. Applied Biomedical Research Center (CRBA), S. Orsola-Malpighi Polyclinic, Italy
  8. Fondazione Ca' Granda, IRCCS Ospedale Maggiore Policlinico, Italy
  9. Geriatric Unit, Department of Clinical Sciences and Community Health, University of Milan, Italy
  10. Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Italy
  11. Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Italy
  12. IRCCS Fondazione Don Carlo Gnocchi, Italy
  13. Department for the Cultural Heritage (DBC), University of Bologna, Italy
  14. Department of Biological, Geological, and Environmental Sciences (BiGeA), Laboratory of Molecular Anthropology and Centre for Genome Biology, University of Bologna, Italy
  15. Department of Physics and Astronomy, University of Bologna, Italy
  16. Department of Biology, Ecology and Earth Sciences, University of Calabria, Italy
  17. Department of Medicine, Unit of Internal Medicine, University of Verona, Italy
  18. School of Anthropology and Museum Ethnography, University of Oxford, United Kingdom
  19. Department of Applied Mathematics and Laboratory of Systems Biology of Aging, Lobachevsky University, Russian Federation
https://doi.org/10.7554/eLife.57849
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Cite this article
  1. Paolo Garagnani
  2. Julien Marquis
  3. Massimo Delledonne
  4. Chiara Pirazzini
  5. Elena Marasco
  6. Katarzyna Malgorzata Kwiatkowska
  7. Vincenzo Iannuzzi
  8. Maria Giulia Bacalini
  9. Armand Valsesia
  10. Jerome Carayol
  11. Frederic Raymond
  12. Alberto Ferrarini
  13. Luciano Xumerle
  14. Sebastiano Collino
  15. Daniela Mari
  16. Beatrice Arosio
  17. Martina Casati
  18. Evelyn Ferri
  19. Daniela Monti
  20. Benedetta Nacmias
  21. Sandro Sorbi
  22. Donata Luiselli
  23. Davide Pettener
  24. Gastone Castellani
  25. Claudia Sala
  26. Giuseppe Passarino
  27. Francesco De Rango
  28. Patrizia D'Aquila
  29. Luca Bertamini
  30. Nicola Martinelli
  31. Domenico Girelli
  32. Oliviero Olivieri
  33. Cristina Giuliani
  34. Patrick Descombes
  35. Claudio Franceschi
(2021)
Whole-genome sequencing analysis of semi-supercentenarians
eLife 10:e57849.
https://doi.org/10.7554/eLife.57849
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7 figures, 2 tables and 20 additional files

Figures

Figure 1
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Study design.

(A) 105+/110+ (in blue) and controls (in orange) recruited in the Italian peninsula and analyzed by whole genome sequencing (discovery cohort). (B) The study design applied in the present study. (C) PCA plot for the discovery cohort (Cohort 1), in red are indicated 105+/110+ and in black the group of controls (CTRL).

Figure 2 with 1 supplement
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Association analysis results considering common variants (MAF >5%).

(A) Manhattan plot for all the SNPs tested for the association analysis by considering semi-supercentenarians and controls. The x-axis shows SNPs according to their chromosomal positions and y-axis shows the p-values, expressed as –log10(p-value). (B) QQ plot of expected –log10(p-values) (x axis) versus observed –log10(p-values) (y axis) (one black point per variant). The genomic inflation factor was estimated to 1.02. (C) Allele frequency of rs7456688-A in all the cohorts analyzed.

Figure 2—figure supplement 1
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eQTL violin plot for rs623108 (chr7: 43864699) identified in a previous longevity study of the Italian population Giuliani et al., 2018b and replicated in the present study.

The rs623108-A allele correlates with rs7456688-A (moderate LD r2 = 0.58 in European populations of 1000Genomes). Data Source: GTEx Analysis Release V8.

Figure 3
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Regional association plot made by LocusZoom.

Regional plots for the most significant region comparing semi-supercentenarians and controls for the Cohort 1, that is discovery cohort (A) and for the validation cohort (B). Each point indicates the p-value for one SNP, the x-axis indicates the genomic localization of the variant and the y-axis indicates the -log10(p-value) from the association analysis. The recombination rate is plotted and indicated in the y-axis. Both plots show the same genomic positions, from chr7:43560257 to chr7:43938230 (GRCH37/hg19).

Figure 4 with 1 supplement
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Common and rare variants analysis.

(A) Common variants in COA1 gene and output of the Bayesian model RiVIERA (Risk Variant Inference using Epigenomic Reference Annotations). The SNPs are shown as a function of their position on chromosome 7. The symbol (dot, rectangle, triangle) indicates the distance to the transcription start site (TSS). The size of the symbol reflects the credible score which exhibit an higher probability of regulatory properties. The colour (indicated as ‘overlapping_annotation’) indicates the total number of epigenomic marks that co-localize with the SNP. (B) KEGG Pathways analysis was performed using i-GSEA4GWAS. -log(FDR value) were indicating for each significant pathways (<0.01). (C) Number of rare variants in the NME1, NME1-NME2 region. Genomic positions were reported in x-axis while the number of variants for each position is reported in y-axis. The number of rare variants in 105+/110+ is reported in blue and in CTRL in orange.

Figure 4—figure supplement 1
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Number of private mutations for each 105+/110+.

Y-axes reported the prevalence and x-axes the number of mutations.

Figure 5 with 2 supplements
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Prevalence of somatic mutations.

(A) Prevalence of somatic mutations in 105+/110+ and controls considering the seven genes analysed. (B,C) the distribution of single-nucleotide substitutions types observed in 105+/110+ and CTRL.

Figure 5—figure supplement 1
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Allelic fraction distribution of the somatic mutations observed.
Figure 5—figure supplement 2
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Boxplot with Polygenic risk scores (PRS) calculated according to different SNPs list identified from previous publication and applied to 105+/110+ and CTRL.

The y-axes reported the value of the score calculated in 105+/110+ (in blue) and in CTRL (in red).

Author response image 1
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The identified area (STK17A) was reported for two centenarians as an example.

All the other data are uniform and comparable to this picture.

Author response image 2
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The identified area (STK17A) was reported for two controls as an example.

All the other data are uniform and comparable to this picture.

Tables

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 p-values, adjusted p-values using SLIDE method (window of 100 SNPs an 10,000 permutations) frequency in 105+/110+ s and controls and p-values in Cohort 2 were reported.

GENE
NAME		CHRBPdbSNPA1ORL95U95P_unadj (Cohort1)P_adjusted
SLIDE (Cohort1)		F_105 (Cohort1)F_CTRL (Cohort1)P_unadj
(Cohort2)
STK17A743637796rs7456688A5.9062.68812.979.73*10−67.00*10−20.5560.2220.021
STK17A743638009rs10257700C5.9062.68812.979.73*10−67.00*10−20.5560.2220.029
STK17A743643835rs10279856G5.9062.68812.979.73*10−67.00*10−20.5560.2220.021
STK17A,COA1743650221rs69685881A5.9062.68812.979.73*10−67.00*10−20.5560.2220.025
STK17A,COA1743651047rs7805969A5.9062.68812.979.73*10−67.00*10−20.5560.2220.016
Table 2
Most significant haplotypes from each significant suggestive area emerged from the single-SNP analysis.
ChrRegionHaplotypeGeneAllelic test ORAllelic test p-valueF_105F_CTRL
2196515104–196992016GGAGCADNAH711.54 (3.02–65.45)2.34*10–050.980.82
743637796–43643835GTACOA10.19 (0.1–0.36)4.00*10–080.350.74
743655836–43714795CATGATTAGTACGCOA10.18 (0.09–0.35)1.84*10–080.350.75
743720429–43756081GATGACTTCOA10.19 (0.09–0.36)4.05*10–080.360.75
7151364264–151376555ACCATPRKAG20.13 (0.05–0.37)8.31*10–060.040.25
9114679493–114691177TTATGCUGCG3.23 (1.66–6.57)1.69*10–040.480.22
9103874937–103955668TAALPPR10.13 (0.05–0.32)9.16*10–070.050.29
1118852217–18873142CCTGT.3.23 (1.66–6.57)1.69*10–040.480.22
1349897577–49905581GCGATCGCAB39L0.21 (0.09–0.44)1.07*10–050.100.35

Additional files

Supplementary file 1

Position identified in the comparison between 105+/110+ and CTRL with unadjusted p-values<10–4 (logistic regression adding sex as covariate).

In red are indicated the independent SNPs pruned for LD. In the last column, the p-values of the same analysis performed including PC1 and PC2 as covariates is reported.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp1-v1.pdf
Supplementary file 2

Gene based analysis for common variants using VEGAS (genes with a nominal pvalue <0.01 were reported).

Gene name and p-values were reported.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp2-v1.pdf
Supplementary file 3

GTEx analysis for the 4 SNPs rs10279856, rs3779059, rs849166, rs849175 with credible score >0 in the Riviera analysis.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp3-v1.pdf
Supplementary file 4

Significant (FDR < 0.05) KEGG pathways involved in longevity identified by iGSEA4GWAS software.

The analysis has been performed considering all the annotated common variants in Cohort 1.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp4-v1.pdf
Supplementary file 5

Significant (FDR < 0.05) GO pathways involved in longevity identified by iGSEA4GWAS software.

The analysis has been performed considering all the annotated common variants.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp5-v1.pdf
Supplementary file 6

Significant (FDR < 0.05) BioCarta pathways involved in longevity identified by iGSEA4GWAS software.

The analysis has been performed considering all the annotated common variants.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp6-v1.pdf
Supplementary file 7

Comparison of allele frequency for a subset of known longevity variants.

The allele associated with a longer lifespan is reported as ‘Protective allele’. Chromosome, position (GRCH 37/hg19), rs ID, gene name, protective allele, frequency in semi-supercentenarians, frequency in controls and nominal p-values were reported.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp7-v1.pdf
Supplementary file 8

Comparison between the SNPs described in Giuliani et al., 2018b to the present study.

The table reported six columns with the description of the SNP, chromosome, position in hg19, minor allele, gene name and the trend of allele frequency in different age groups as described in Giuliani et al., 2018b (Class A, B, E, and F, see legend). From columns 7–10 allele frequencies in Cohort 2 is reported as published in Giuliani et al., 2018b. The p-value of the association test performed between 105+/110+ and CTRL (Cohort 1) is reported in the last column.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp8-v1.pdf
Supplementary file 9

List of 5055 105+/110+ private mutations predicted as damaging in more than 4 (out of 6) database (SIFT Pred, Polyphen2 HVAR Pred, MutationTaster Pred, MutationAssessor Pred, FATHMM Pred, FATHMM MKL Coding Pred).

https://cdn.elifesciences.org/articles/57849/elife-57849-supp9-v1.pdf
Supplementary file 10

Genes identified using SKAT-O method in 105+/110+ and CTRL including all rare variants (genes with a nominal pvalue <0.01 were reported).

Gene name, pvalues and the number of variants is reported.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp10-v1.pdf
Supplementary file 11

Genes identified using SKAT-O method in 105+/110+ and CTRL including only rare damaging variants (genes with a nominal p-value<0.01 were reported).

Genes, p-value and the number of variants is reported.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp11-v1.pdf
Supplementary file 12

Disruptive mutations (moderate and high impact).

Genomic position (hg19), gene name, and group in which the mutation has been identified are reported.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp12-v1.pdf
Supplementary file 13

List of somatic mutations identified that are reported at least seven times in hematopoietic and lymphoid malignancies using the catalogue COSMIC.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp13-v1.pdf
Supplementary file 14

Logistic regression calculated considering genetic risk score for each individual for Alzheimer diseases, cancer (breast, colon, lung, pancreatic, prostate), coronary disease, stroke, and type two diabetes according to Erikson et al., 2016.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp14-v1.pdf
Supplementary file 15

CAD genetic risk scores analyzed in Cohort 1.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp15-v1.pdf
Supplementary file 16

SNPs used for PRS in UK Biobank CardioMetabolic Consortium CHD Working Group et al., 2019.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp16-v1.pdf
Supplementary file 17

SNPs used for PRS in Natarajan et al., 2017.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp17-v1.pdf
Supplementary file 18

SNPs used for PRS in van der Harst and Verweij, 2018.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp18-v1.pdf
Supplementary file 19

SNPs used for PRS in Nelson et al., 2017.

https://cdn.elifesciences.org/articles/57849/elife-57849-supp19-v1.pdf
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https://cdn.elifesciences.org/articles/57849/elife-57849-transrepform-v1.pdf

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  1. Paolo Garagnani
  2. Julien Marquis
  3. Massimo Delledonne
  4. Chiara Pirazzini
  5. Elena Marasco
  6. Katarzyna Malgorzata Kwiatkowska
  7. Vincenzo Iannuzzi
  8. Maria Giulia Bacalini
  9. Armand Valsesia
  10. Jerome Carayol
  11. Frederic Raymond
  12. Alberto Ferrarini
  13. Luciano Xumerle
  14. Sebastiano Collino
  15. Daniela Mari
  16. Beatrice Arosio
  17. Martina Casati
  18. Evelyn Ferri
  19. Daniela Monti
  20. Benedetta Nacmias
  21. Sandro Sorbi
  22. Donata Luiselli
  23. Davide Pettener
  24. Gastone Castellani
  25. Claudia Sala
  26. Giuseppe Passarino
  27. Francesco De Rango
  28. Patrizia D'Aquila
  29. Luca Bertamini
  30. Nicola Martinelli
  31. Domenico Girelli
  32. Oliviero Olivieri
  33. Cristina Giuliani
  34. Patrick Descombes
  35. Claudio Franceschi
(2021)
Whole-genome sequencing analysis of semi-supercentenarians
eLife 10:e57849.
https://doi.org/10.7554/eLife.57849