Risk allele proportions at genomic loci with somatic loss.

<p>Allelic copy number was measured in constitutional DNA and leukemia bone marrow (tumor) DNA from ALL patients heterozygous for <i>CDKN2A</i> tagging SNP rs3731217 (A), and <i>IKZF1</i> SNP rs4132601 (B). Risk allele proportions are displayed as a fraction of the total allelic copy number measured in each patient using ddPCR. Each subject was assayed in duplicate, and error bars represent the standard error of the mean (some error bars not visible due to their range falling within boundaries of the data point). Upper/lower thresholds of allelic imbalance (AI) were +/- 3 SDs from the mean allelic proportion from repeat measurements in constitutional DNA samples (white squares). For rs3731217, 11 tumor samples showed AI favoring the risk allele versus 6 patients with AI favoring the protective allele (P = 0.17). For rs4132601, 17 tumor samples showed AI favoring the risk allele versus 12 patients with AI favoring the protective allele (P = 0.23).</p

<i>CDKN2A</i> and <i>IKZF1</i> SNP allele proportions in tumor DNA relative to genomic control copy number.

<p>Stacked histograms showing tumor DNA copy number of (A) <i>CDKN2A</i> and (B) <i>IKZF1</i> SNPs relative to a genomic control locus (<i>SLC24A3</i>). Black and grey bars represent the proportions of normalized SNP copy number accounted for by the risk and protective alleles respectively. White bars represent the difference between <i>CDKN2A</i>/<i>IKZF1</i> SNP copy number and the genomic control gene copy number. SMART-ddPCR was used to measure copy number of SNP risk/protective alleles, as well as the genomic control locus, in 35 leukemia bone marrow (tumor) DNA samples for <i>CDKN2A</i> (SNP rs3731249) and 75 tumor DNA samples for <i>IKZF1</i> (SNP rs4132601). Samples are grouped into those with allelic imbalance (AI) and those without AI, and arranged in order of normalized gene copy number relative to the genomic control.</p

Comparison between deletion gene copy number measurements made by SMART-ddPCR and MLPA.

<p>Copy number measurements were available from ddPCR and MLPA assays for SNPs at the two deletion genes <i>CDKN2A</i> (SNP rs3731249) and <i>IKZF1</i> (SNP rs4132601) in 27 and 75 tumor DNA samples respectively. (A) High correlation (R2 = 0.91) between the combined deletion gene copy number measurements made by ddPCR and MLPA. (B) Bland-Altman plot displaying the difference between measurements made in the same individual against their mean, as measured by two different methodologies (<i>i</i>.<i>e</i>. ddPCR and MLPA). There is very close agreement between the copy number measurements made by the two assays, as demonstrated by the narrow limits of agreement (-0.170 to 0.138) either side of the observed average agreement (-0.016).</p

Summary of the childhood ALL-associated SNPs investigated and the corresponding tumor DNA allelic imbalance results.

<p>* Number of heterozygous samples (for each SNP) with available bone marrow (i.e. tumor) DNA.</p><p>‡ % of HeH ALL samples with gains of that chromosome, based on data from Paulsson <i>et al</i>. (2010) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143343#pone.0143343.ref021" target="_blank">21</a>] and Dastugue <i>et al</i>. (2013) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143343#pone.0143343.ref022" target="_blank">22</a>].</p><p>† High hyperdiploid samples only.</p><p>Significant p-values highlighted in bold.</p><p>Summary of the childhood ALL-associated SNPs investigated and the corresponding tumor DNA allelic imbalance results.</p

Risk allele proportions at genomic loci with somatic gain (<i>i</i>.<i>e</i>. hyperdiploid chromosomes).

<p>Allelic copy number was measured in constitutional DNA and leukemia bone marrow (tumor) DNA from HeH ALL patients heterozygous for ALL-associated SNPs on chromosomes frequently gained in HeH ALL: <i>CEBPE</i> SNP rs2239633 (A), <i>ARID5B</i> SNP rs7089424 (B), <i>PIP4K2A</i> SNP rs10764338 (C), and <i>GATA3</i> SNP rs3824662 (D). Risk allele proportions are displayed as a fraction of the total allelic copy number measured in each patient using ddPCR. Each subject was assayed in duplicate, and error bars represent the standard error of the mean (some error bars not visible due to their range falling within boundaries of the data point). Upper/lower thresholds of allelic imbalance (AI) were +/- 3 SDs from the mean allelic proportion from repeat measurements in constitutional DNA samples (white squares). For rs2239633, 19 tumor samples showed AI favoring the risk allele versus 13 patients with AI favoring the protective allele (P = 0.19). For rs7089424, 20 tumor samples showed AI favoring the risk allele versus 15 patients with AI favoring the protective allele (P = 0.25). For rs10764338, 4 tumor samples showed AI favoring the risk allele versus 5 patients with AI favoring the protective allele (P = 0.50). For rs3824662, 10 tumor samples showed AI favoring the risk allele versus 9 patients with AI favoring the protective allele (P = 0.50). Data points clustering at ~0.66 and ~0.33 represent a 3:2 or 2:3 risk:protective allele proportion due to chromosomal copy number shifting from diploid (n = 2) to triploid (n = 3). Data points at ~0.75 represents a 3:1 risk:protective allele proportion due to a diploid to tetraploid (n = 4) shift in chromosome ploidy. Data points at 1 and 0 likely represent HeH ALL that has arisen via near-haploidy, leading to chromosomal LOH (Paulsson <i>et al</i>. 2005) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143343#pone.0143343.ref031" target="_blank">31</a>].</p

Candidates for tumor PAI: recurrent SCNA loci from TCGA that overlap cancer-associated SNPs (NHGRI GWAS Catalog) identified in matching tumor types.

<p>SNP associations and tumor types highlighted in bold indicate those whereby cancer type of SNP associations matches tumor type in which recurrent SCNAs were identified.</p><p>* Chromosomal locations based on human genome build hg19.</p><p>** Cancer type of SNP association loci that overlap SCNA regions (SNPs retrieved from January 2015 version of NHGRI GWAS catalog).</p><p>‡ Tumor type in which recurrent SCNAs were detected in TCGA.</p><p>ALL = acute lymphoblastic leukemia; BLCA = bladder; BRCA = breast; CLL = chronic lymphoblastic leukemia; CRC = colorectal; GBM = glioblastoma multiforme; HNSC = head and neck squamous cell carcinoma; KIRC = kidney renal cell carcinoma; LAML = acute myeloid leukemia; LUAD = lung adenocarcinoma; LUSC = lung squamous cell carcinoma; OV = serous ovarian carcinoma; UCEC = endometrial (uterine).</p><p>Candidates for tumor PAI: recurrent SCNA loci from TCGA that overlap cancer-associated SNPs (NHGRI GWAS Catalog) identified in matching tumor types.</p

Schematic showing the methodology used to discover genetic signatures of exceptional longevity (EL).

<p>The analysis included genetic matching to remove confounding by population stratification between cases and controls of the discovery and replication set 1, discovery and replication of single SNP associations, multivariate genetic risk modeling and generation of predictive genetic profiles, and cluster analysis of genetic risk profiles to discover genetic signatures of EL.</p

Correlation of genetic signatures with lifespan.

<p><b>Panel A:</b> Some genetic signatures are associated with significantly different life-span. For example the most predictive signature (C1) comprises centenarians with significant longer survival compared to centenarians with signatures C2 or C26. (p-value 0.01 and 0.02) More examples are in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029848#pone.0029848.s015" target="_blank">Figure S15</a></b>. <b>Panel B:</b> The two most predictive genetic signatures and the least predictive signature in the centenarians of the merged replications sets show consistent results. The comparison between survival of centenarians with the most predictive signature R1 and the least predictive signature R15 reaches statistical significance, (p-value = 0.003) while the comparison between survival distributions of centenarians with signatures R1 and R2 does not reach statistical significance (p-value 0.10).</p

Examples of genetic risk profiles in 4 study subjects (3 centenarians with ages at death 107, 108 and 119 years, and a control).

<p>281 nested SNP sets were used to compute the posterior probability of exceptional longevity in the 4 subjects (y-axis) and were plotted against the number of SNPs in each set (x-axis). In the 107 year old, the first 5 SNP sets Σ₁ = [rs2075650], Σ₂ = [Σ₁, rs1322048], …, Σ₅ = [Σ₄, rs6801173] determine a posterior probability of exceptional longevity ranging between 0.54 and 0.28. This subject carries genotypes AA, AG, AG, CC, AA for the 5 SNPs respectively and, with the exclusion of genotype AA of rs2075650 that is more common in centenarians, the other genotypes are more common in controls than centenarians and determine a posterior probability of exceptional longevity that is lower than the posterior probability of average longevity. The sixth SNP set, Σ₆ = [Σ₅, rs337656], predicts an almost 30% chance of exceptional longevity. The subject carries the AA genotype for the SNP rs337656 that is more frequent in centenarians (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029848#pone.0029848.s022" target="_blank">Table S1</a>), and carrying this genotype increases the posterior probability of exceptional longevity. The probability predicted by the next SNP sets increases steadily and all models with more than 20 SNPs predict more than a 50% chance of exceptional longevity. This genetic profile shows that the subject carries some combinations of SNP alleles that are associated with exceptional longevity, while other alleles are associated with “average longevity”. However, the overall genetic risk profile determined by all 281 SNP sets makes a strong case for exceptional longevity because the majority of models predict more than an 80% chance of exceptional longevity. The genetic risk profile of the centenarian who died at age 119 years is even more convincing: with the exception of the first SNP, all subsequent SNP sets determine more than a 70% chance of exceptional longevity, and 272 of the 281 models predict more than an 80% chance for exceptional longevity. This profile shows that this subject is highly enriched for SNPs alleles that are more common in centenarians (longevity associated variants) and that probably played a determinant role in the extreme survival. The profile of the third subject, age 108 years, shows that different SNP sets determine different chances for exceptional longevity, and only the overall trend of genetic risk provides evidence for exceptional longevity. The fourth plot displays the profile of a control, and shows that this subject carries some longevity associated variants; however, the overall trend of genetic risk points to average longevity rather than exceptional longevity.</p

Genes in the genetic risk models have been linked to coronary artery disease and Alzheimer's disease.

<p>The two networks display 38 of the 130 genes in the genetic risk model that are linked to Alzheimer's disease (top) and 24 of the 130 genes that are linked to coronary artery disease (bottom) in the literature, either by functional or genetic association studies. The nodes that are linked by an edge represents either genes that are “co-cited” (dashed lines) or “associated by expert curation” (continuous lines). The arrow head means that the associations are activation (triangle), inhibition (circle), modulation (diamond), conversion (arrow head). The node shape informs about known roles of the genes (see inset). The nodes that are singleton were linked to AD/CAD in the literature but not together with other genes. The number of genes linked to each disease was compared to what is expected by chance using Fisher exact test, and the p-values show that the gene seta are unluckily the result of chance. (Networks generated with Genomatix).</p