Online Resource 1

For article “Common SIRT1 variants modify the effect of abdominal adipose tissue on aging-related lung function decline”,

submitted to AGE on Nov 26th 2015,
by Ivan Curjuric, Medea Imboden, Pierre-Olivier Bridevaux, Margaret W Gerbase, Margot Haun, Dirk Keidel, Ashish Kumar, Marco Pons, Thierry Rochat, Tamara Schikowski, Christian Schindler, Arnold von Eckardstein, Florian Kronenberg, Nicole M Probst-Hensch.

Corresponding author: Dr. Ivan Curjuric, MD, PhD

Swiss Tropical and Public Health Institute

Socinstrasse 57, P.O. Box, 4002 Basel,

Switzerland

Email:

Details of the genotyping procedures and biomarker measurements

SIRT1 genotypes

The genotype data single nucleotide polymorphisms (SNPs) of gene SIRT1 used in this analysis consisted of three tagging SNPs (rs730821, rs10997868, rs10823116). They were obtained from two different genotyping platforms referred as genotype data (A) and genotype data (B).

The first genotyping was done in a case-control design on all asthmatics (n=654) and a random sample of non-asthmatic participants (n=958) using the Illumina Human 610Kquad BeadChip (A) in the framework of the EU-funded GABRIEL study (1), a large asthma study consortium. The genotype data of this SAPALDIA subsample was imputed to 2.5 Mio SNPs using MACH v 1.0 software (2) and the HapMap v22 CEPH reference panel of Utah residents with ancestry from northern and western Europe (3) to fill in missing genotype values and improve genomic coverage. Asthmatic subjects were excluded from the current analyses. The second genotyping was done in a non-overlapping random sample of participants without asthma (N=3015) on the Illumina Human OmniExpress-Exome BeadChip (B). No imputation was performed for this subset of SAPALDIA samples.
Quality control (QC) criteria were the same for the two platforms. Only samples with high genotyping success rate (≥97%) were kept and those of non-European origin, with cryptic relatedness or sex-inconsistencies were excluded. For SNP-based quality control, thresholds of 97% call rate, p<10-4 for the deviation from Hardy-Weinberg equilibrium, and minor allele frequency <5% were applied.
All variants falling into a 40kb window spanning on both sides from the SIRT1 tagging SNP rs2273773 were defined as SIRT-1 SNPs (SIRT1is 33 kb in length and rs2273773 is located within the transcribed part). By comparing the SNP coverage of the two genotype data sets, ten overlapping SNPs were identified (rs730821, rs3758391, rs10823108, rs10997868, rs2224573, rs2273773, rs10997870, rs10823112, rs10997875, rs10823116). To identify SNPs capturing common variation in the SIRT1 gene region and reduce the number of statistical tests, we subjected genotype data (B) to pairwise linkage disequilibrium (LD) based pruning using PLINK v1.03 (URL: http://pngu.mgh.harvard.edu/ purcell/plink) (4). Pruning identified rs730821 (located 17 kilobases from 5’ end of SIRT1), rs10997868 (intronic SNP) and rs10823116 (intronic to the neighboring HERC4 gene) as tagging SNPs capturing the common genetic haplotype variation in our study population. All three SNPs had been imputed with high quality (all R-squared metrics >0.9745) from genotype data (A).

C-reactive protein gene (CRP) SNP rs1800947

SNP rs1800947 in CRP was genotyped using the iPLEX Gold MassARRAY (SEQUENOM, San Diego, USA). rs1800947 is a functional SNP whose C-alleles have been associated with lower systemic levels of C-reactive protein (Kettunen et al. 2011). The SNP was used to assess the potential role of systemic inflammation in mediating the observed effects of SIRT1.

C-reactive protein serum measurements

Concentrations of high sensitivity C-reactive protein were determined by a latex-enhanced immunoturbidimetric assay (Roche diagnostics, Germany) with a lower detection threshold of 0.1 mg/l. Due to their skewed distribution, the values were transformed using a logarithmic function for analysis. C-reactive protein values ≥10mg/L were excluded when analyzing effects of rs1800947 on protein serum levels.

References

1. Moffatt MF, Gut IG, Demenais F, Strachan DP, Bouzigon E, Heath S, et al. A large-scale, consortium-based genomewide association study of asthma. The New England journal of medicine. 2010;363:1211-1221.

2. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genetic epidemiology. 2010;34:816-834.

3. International HapMap C. A haplotype map of the human genome. Nature. 2005;437:1299-1320.

4. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. American journal of human genetics. 2007;81:559-575.