Confirmation of the Reported Association of Clonal Chromosomal Mosaicism with an Increased Risk of Incident Hematologic Cancer

<div><p>Chromosomal abnormalities provide clinical utility in the diagnosis and treatment of hematologic malignancies, and may be predictive of malignant transformation in individuals without apparent clinical presentation of a hematologic cancer. In an effort to confirm previous reports of an association between clonal mosaicism and incident hematologic cancer, we applied the anomDetectBAF algorithm to call chromosomal anomalies in genotype data from previously conducted Genome Wide Association Studies (GWAS). The genotypes were initially collected from DNA derived from peripheral blood of 12,176 participants in the Group Health electronic Medical Records and Genomics study (eMERGE) and the Women’s Health Initiative (WHI). We detected clonal mosaicism in 169 individuals (1.4%) and large clonal mosaic events (>2 mb) in 117 (1.0%) individuals. Though only 9.5% of clonal mosaic carriers had an incident diagnosis of hematologic cancer (multiple myeloma, myelodysplastic syndrome, lymphoma, or leukemia), the carriers had a 5.5-fold increased risk (95% CI: 3.3–9.3; p-value = 7.5×10⁻¹¹) of developing these cancers subsequently. Carriers of large mosaic anomalies showed particularly pronounced risk of subsequent leukemia (HR = 19.2, 95% CI: 8.9–41.6; p-value = 7.3×10⁻¹⁴). Thus we independently confirm the association between detectable clonal mosaicism and hematologic cancer found previously in two recent publications.</p> </div

Counts of detected mosaic anomalies by chromosomal location and event type.

<p>Counts of detected mosaic anomalies by chromosomal location and event type.</p

Kaplan Meier plots of the proportion of individuals remaining without diagnosed A) Hematologic cancer stratified by presence (blue) or absence (red) of a mosaic anomaly or B) Leukemia stratified by presence (blue) or absence (red) of a large mosaic anomaly (>2 mb).

<p>Kaplan Meier plots of the proportion of individuals remaining without diagnosed A) Hematologic cancer stratified by presence (blue) or absence (red) of a mosaic anomaly or B) Leukemia stratified by presence (blue) or absence (red) of a large mosaic anomaly (>2 mb).</p

Characteristics of mosaic anomalies.

<p>A) BAF and LRR metrics for mosaic anomalies by estimated copy change from disomic state (red = loss, dark blue = gain, orange = copy neutral loss of heterozygosity. B) BAF and LRR metrics for mosaic anomalies by location (dark blue = interstitial, turquoise = p terminal, pink = q terminal or red = whole chromosome). C) BAF and LRR metrics for mosaic anomalies by type of chromosome (green circle = acrocentric, purple cross = metacentric). D) BAF and LRR metrics for mosaic (red) and non-mosaic (black) anomalies.</p

Comparison characteristics of hematologic cancer cases and individuals without a diagnosed hematologic cancer during study follow-up.

<p>Comparison characteristics of hematologic cancer cases and individuals without a diagnosed hematologic cancer during study follow-up.</p

Summary of characteristics of studies included in the analysis.

1<p>Number of individuals included and analyzed in study;</p>2<p>Predominant ancestral group;</p>3<p>Age at baseline and/or sample collection;</p>4<p>Only phase 1 data included;</p>5<p>Only phase 2 data included.</p

Mosaic anomalies plotted across chromosome in megabases (mb) by estimated copy change from disomic state (red = loss, dark blue = gain, orange = copy neutral loss of heterozygosity).

<p>The red box around the ideogram represents the region of interest for the plot located below. Chromosome 21 is omitted due to the absence of detected mosaic anomalies on the chromosome. (Note: plots are not drawn to scale).</p

Comparison of most significant associations identified in European Americans with African Americans from the eMERGE Network.

<p>We plotted p-values, coded allele frequencies, and betas for euthyroid European Americans (n = 4,501) and African Americans (n = 351) in the eMERGE Network for serum TSH level tests of association using SynthesisView. Data shown are comparisons between European Americans (blue markers) and African Americans (red markers) for p-values (data shown are –log10 (pvalue)), genetic effect magnitudes (beta), and minor (coded) allele frequencies (MAF) for the 31 most significant SNPs in European Americans. Red horizontal line on p-value track indicates p = 0.05. SNPs are oriented across the top of the figure, arranged by chromosomal location. Large triangles represent p-values at or smaller than 5×10⁻⁰⁸. Direction of the marker for p-values indicates direction of effect for each SNP.</p

Mechanistic Phenotypes: An Aggregative Phenotyping Strategy to Identify Disease Mechanisms Using GWAS Data

<div><p>A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases. We hypothesized that these disease mechanisms could be identified using low minor allele frequency (MAF<0.1) non-synonymous SNPs (nsSNPs) associated with “mechanistic phenotypes”, comprised of collections of related diagnoses. We studied two mechanistic phenotypes: (1) thrombosis, evaluated in a population of 1,655 African Americans; and (2) four groupings of cancer diagnoses, evaluated in 3,009 white European Americans. We tested associations between nsSNPs represented on GWAS platforms and mechanistic phenotypes ascertained from electronic medical records (EMRs), and sought enrichment in functional ontologies across the top-ranked associations. We used a two-step analytic approach whereby nsSNPs were first sorted by the strength of their association with a phenotype. We tested associations using two reverse genetic models and standard additive and recessive models. In the second step, we employed a hypothesis-free ontological enrichment analysis using the sorted nsSNPs to identify functional mechanisms underlying the diagnoses comprising the mechanistic phenotypes. The thrombosis phenotype was solely associated with ontologies related to blood coagulation (Fisher's p = 0.0001, FDR p = 0.03), driven by the <i>F5, P2RY12</i> and <i>F2RL2</i> genes. For the cancer phenotypes, the reverse genetics models were enriched in DNA repair functions (p = 2×10−5, FDR p = 0.03) (<i>POLG/FANCI, SLX4/FANCP, XRCC1, BRCA1, FANCA, CHD1L</i>) while the additive model showed enrichment related to chromatid segregation (p = 4×10−6, FDR p = 0.005) (<i>KIF25, PINX1</i>). We were able to replicate nsSNP associations for <i>POLG/FANCI, BRCA1, FANCA</i> and <i>CHD1L</i> in independent data sets. Mechanism-oriented phenotyping using collections of EMR-derived diagnoses can elucidate fundamental disease mechanisms.</p></div

Population characteristics in euthyroid individuals for serum thyroid stimulating hormone (TSH) levels and demographics in the eMERGE Network.

<p>Means (standard deviation) are presented unless otherwise noted.</p><p>Population characteristics in euthyroid individuals for serum thyroid stimulating hormone (TSH) levels and demographics in the eMERGE Network.</p