Elhaik, Eran

Abstract Motivation Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual’s genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. Results Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual’s genetic similarity to a continental group. Availability and implementation AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

Abstract Purpose: Multiple myeloma (MM) is a plasma cell (PC) malignancy with an increasing incidence in the United States. Fluorescence in situ hybridization (FISH) is currently the gold-standard diagnostic assay to detect recurrent genomic abnormalities of prognostic and therapeutic significance in MM. Hyperdiploid MM, a standard risk abnormality, is characterized by gains of odd-numbered chromosomes including chromosome 15. While gains of chromosome 15 have been reported in approximately 53% of standard risk MM patients (Chretien, Blood, 2015), losses of chromosome 15 are rarely reported. Enumeration of chromosome 15 centromere is detected using the alpha satellite FISH probe. We previously discovered ~9% of cases had false-negative chromosome 15 FISH results when compared to Mate Pair Sequencing (MPseq) (Smadbeck, BCJ, 2019). To our knowledge, no previous studies have reported a reduced performance of the alpha satellite D15Z4 FISH probe in PC FISH studies. A false-negative result for D15Z4 could result in an underreporting of trisomy 15 and overreporting of monosomy 15. Here, we investigated the incidence of false-negative D15Z4 FISH results by examining the presence of monosomy 15 from patients with a monoclonal gammopathy. Methods: We identified 1231 samples from patients with a monoclonal gammopathy who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from bone marrow samples was genotyped on the Precision Medicine Research Array and biogeographical ancestry was assessed using the Geographic Population Structure Origins tool (Elhaik, Nat Commun, 2014; Home DNA 2016). FISH of immunoglobulin (cIg)-stained PCs was performed as described (Baughn, BCJ, 2018). Chromosome 15 centromere probe (Vysis CEP 15 D15Z4, Abbott Molecular, Abbott Park, IL) was used for chromosome 15 enumeration. Samples were divided into two groups: Cases with or without evidence of monosomy 15 by FISH. Mann-Whitney U test was applied to compare difference between groups for data deviating from normal distribution. A Chi-squared test evaluating the differences across these two groups was used. Results: We identified monosomy 15 in 31 cases (2.5% of cohort). Monosomy 15 was observed in nearly 100% of both the PC (cIg+) and non-PC (cIg-) populations, by contrast to the other recurrent PC-specific genomic abnormalities which were restricted to only the PC population. All samples with a banded analysis revealed most metaphases had two normal chromosome 15s demonstrating that the D15Z4 FISH probe failed to correctly enumerate the number of chromosome 15 centromere signals. Of the samples with a monosomy 15 result, the median African ancestry was 76.2% (95% percentiles: 77.5%-89.0%), while the median African ancestry of the non-monosomy 15 cohort was 2.3% (95% percentiles: 0%-87.7%) (). Although not all patients with >50% African ancestry had evidence of monosomy 15, 26/31 (83.9%) of patients with monosomy 15 had >50% African ancestry. Accordingly, patients with African ancestry (similarity ≥ 0.70) had 8.02-fold (95% confidence interval: 3.73-17.25, P = 9.92 X10 -8) increased possibility to have monosomy 15. Of cases with monosomy 15, 22 (71.0%) had an IGH rearrangement with the most prevalent IGH partner being CCND1. The incidence of t(11;14) was greater in the monosomy 15 group (13/31:41.9%), in contrast to the non-monosomy 15 group (330/1200=27.5%) (P = 0.12). Conclusions: We report a polymorphic variant of the chromosome 15 centromere resulting in a false-negative FISH result using the D15Z4 FISH probe. Surprisingly, the false-negative FISH result was not uniformly distributed among patients and was highly enriched in individuals of African ancestry. This may be due to a variation of the chromosome 15 alpha satellite in some individuals that prevents the D15Z4 FISH probe from sufficiently binding (O'Keefe, Genome Research, 2000). Although this study focused on identification of monosomy 15, the incidence of a false-negative result is likely greater than we have reported in this study since we did not evaluate cases that had a true trisomy 15 that was reported as normal chromosome 15 enumeration by FISH. This study highlights a critical need to ensure diagnostic reagents have equal performance for all patients independent of their ancestry. Evaluation of genomic events using unbiased approaches such as whole genome sequencing may circumvent some of these limitations. Disclosures Elhaik: DDC: Consultancy. Baird: DDC: Current Employment. Kumar: Antengene: Consultancy, Honoraria; Bluebird Bio: Consultancy; Beigene: Consultancy; BMS: Consultancy, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Carsgen: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Sudden infant death syndrome (SIDS) is the unexpected death of an infant under one year of age that remains unexplained after a thorough investigation. Despite SIDS remaining a diagnosis of exclusion with an unexplained etiology, it is widely accepted that SIDS can be caused by environmental and/or biological factors, with multiple underlying candidate genes. However, the lack of biomarkers raises questions as to why genetic studies on SIDS to date are unable to provide a clearer understanding of the disease etiology. We sought to improve the identification of SIDS-associated genes by reviewing the SIDS genetic literature and objectively categorizing and scoring the reported genes based on the strength of evidence (from C1 (high) to C5 (low)). This was followed by analyses of function, associations between genes, the enrichment of gene ontology (GO) terms, and pathways and gender difference in tissue gene expression. We constructed a curated database for SIDS gene candidates consisting of 109 genes, 14 of which received a category 4 (C4) and 95 genes received the lowest category of C5. That none of the genes was classified into the higher categories indicates the low level of supporting evidence. We found that genes of both scoring categories show distinct networks and are highly diverse in function and involved in many GO terms and pathways, in agreement with the perception of SIDS as a heterogeneous syndrome. Genes of both scoring categories are part of the cardiac system, muscle, and ion channels, whereas immune-related functions showed enrichment for C4 genes. A limited association was found with neural development. Overall, inconsistent reports and missing metadata contribute to the ambiguity of genetic studies. Considering those parameters could help improve the identification of at-risk SIDS genes. However, the field is still far from offering a full-pledged genetic test to identify at-risk infants and is still hampered with methodological challenges and misunderstandings of the vulnerabilities of vital biological mechanisms.

Purpose: Multiple myeloma (MM) is a plasma cell (PC) malignancy with an increasing incidence in the US. Epidemiological studies demonstrate a 2-3 fold higher incidence of the pre-malignant monoclonal gammopathy of undetermined significance (MGUS) and MM with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). With equal access to care, AAs have better overall survival compared to EAs (Waxman, Blood, 2010). This disparity may be explained by ancestral-associated genetic predisposition of AAs to development of monoclonal gammopathies and to specific acquired, cytogenetically-defined subtypes. Using calculated genetic ancestry data, we have previously identified a higher prevalence of IgH translocations t(11;14), t(14;16) and t(14;20) in individuals with >80% African ancestry (Baughn, BCJ, 2018). Since SNP rs9344 encoding the CCND1 870G>A polymorphism has been reported in association with increased risk of t(11;14) (Weinhold, Nature Genetics, 2013), we investigated whether rs9344 correlates with African ancestry and with t(11;14) in our cohort of patients with plasma cell dyscrasias. Methods: We studied 898 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from bone marrow samples was genotyped on the Precision Medicine Research Array and biogeographical ancestry was assessed using the Geographic Population Structure Origins tool (Elhaik, Nat Commun, 2014). Plasma cell proliferative disorder FISH of immunoglobulin (cIg)-stained positive plasma cells was performed as described (Baughn, BCJ, 2018). Individuals were divided into three ancestral groups: 1. EAs (<0.1% African ancestry and <30% Asian ancestry); 2. AAs (> 80% African ancestry); and 3. Other. Chi-squared test was used to determine the overall comparison between the 3 ancestral groups and also between ancestral groups 1 vs 2, and 2 vs 3 using pairwise comparison. All tests were two-sided with alpha level set at 0.05 for overall statistical significance. Pairwise comparison was considered statistically significant when p<0.025 based on Bonferroni method for multiple comparisons. Results: We identify increased risk of development of either a t(11;14), t(14;16) or t(14;20) in AAs (48.8%) compared to EAs (33.6%) (p-value=0.0051). To explore the genetic basis of increased t(11;14) specifically (37.4% AAs vs. 27.3% p-value=0.049), we evaluated the frequency of the G risk allele of rs9344 in relation to African ancestry. The frequency of the G risk allele was higher in AAs (0.81) compared to EAs (0.59) (p-value <0.0001) and also higher in t(11;14) cases (0.73) compared to non-t(11;14) controls (0.58) (p-value <0.0001). A multivariate model identified only rs9344 as significantly associated with t(11;14) after adjusting for age, gender and race group suggesting that it plays a role in the development of t(11;14) (p-value <0.001 for GG, p-value=0.005 for AG). To test if these results are replicable, we studied the MMRF CoMMpass cohort. This cohort includes individuals with newly diagnosed MM along with self-report race information and translocation data from long insert whole genome sequencing. Although t(11;14) was not enriched in self-reported Black individuals from this cohort (17.5% Black vs. 20.4% White, p-value=0.47), there was association between the rs9344 and self-reported Black race (p-value <0.0001) and with the presence of t(11;14) when the t(11;14) was analyzed by long insert whole genome sequencing (p-value=0.0001). Conclusions: To our knowledge, this study includes the largest group of African individuals with an abnormal plasma cell clone along with uniformly collected FISH, genotyping and ancestry data. We have identified in this diverse population the association of the CCND1 870G>A polymorphism (rs9344) with African ancestry and with t(11;14) suggesting that it plays a role in the development of t(11;14) plasma cell dyscrasias. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Dispenzieri:Alnylam: Research Funding; Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Kumar:Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Research Funding.

Abstract Monoclonal gammopathies, including multiple myeloma (MM), represent a group of plasma cell (PC) disorders that comprise of mostly incurable hematopoietic malignancies with an increasing incidence in the US. Previous epidemiological studies demonstrated a 2-3 fold higher incidence of monoclonal gammopathy of undetermined significance (MGUS) and a similarly higher incidence of MM along with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). This suggests a possible ancestral-associated genetic predisposition of AAs to the development of monoclonal gammopathies (Landgren, Blood, 2006). When access to care is equal, AAs have a better overall survival compared to EAs suggesting that AAs may have a genetic predisposition that renders them better responders to treatment or have more favorable cytogenetic subtypes of MM (Waxman, Blood, 2010). Previous efforts to understand this disparity have relied on self-reported race rather than genetic ancestry. We hypothesize that quantifying genetic ancestry is necessary to fully understand the genetic mechanisms of racial disparities of monoclonal gammopathies. We describe our study of 881 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from the bone marrow samples was genotyped on the Precision Medicine Research Array. Biogeographical ancestry was assessed using the Geographic Population Structure Origins tool, which provides the ancestral breakdown of 36 admixture components for each individual representing different geographic regions and outputs admixture proportions corresponding to those ancestries (Elhaik, Nat Commun, 2014). We evaluated whether an increase in the percentage of African Ancestry altered the odds of any primary cytogenetic abnormality. We demonstrated that a 10% increase in the percentage of African ancestry was associated with a 6% increase in the odds of detecting either an t(11;14), t(14;16) or t(14;20) (odds ratio = 1.06, 95% CI: 1.02 - 1.11; p-value = 0.05). Although we observed an increase in the odds of each of the individual translocations with 10% increase in African ancestry, l t(11;14) (OR=1.03) t(14;16) (OR=1.11) and t(14;20) (OR=1.10), these three abnormalities were combined to achieve greater statistical power. The differences in cytogenetic abnormalities were most striking among individuals with ≥80.0% African (n=120) compared to individuals with <0.1% African ancestry (n=235). Using these cutoff values comparing the ≥80.0% and <0.1% African ancestry cohorts, there was a higher prevalence of t(11;14), t(14;16) and t(14;20) (51% vs. 33%, respectively, p-value=0.008) and a lower prevalence of trisomies (with or without IgH translocations) (48.3% vs. 61.3%, respectively, p-value=0.066). In addition, the ≥80% African ancestry cohort also displayed a slightly lower prevalence of monosomy 13/13q deletion compared to the cohort with <0.1% African ancestry (34.2% vs. 38.7%, respectively, p-value=0.021). Further, we observed a significantly higher prevalence in the proportion of females with monoclonal gammopathies among the ≥80% African ancestry cohort compared to the cohort with <0.1% African ancestry (56.7% vs. 42.1%, respectively, p-value=0.028). We complement our past studies that relied on self-reported race and characterized patients' demographic and uniformly collected cytogenetic data in relation to genetically defined African ancestry. We demonstrate the prevalence of having one of three specific subtypes (i.e., t(11;14), t(14;16), or t(14;20)) was significantly higher in those with highest (≥80%) compared to lowest (<0.1%) African ancestry demonstrating African ancestry is associated with a subtype distribution of MM that differs from non-African individuals. These differences were only revealed after analysis of individuals with the highest and lowest percentage of African ancestry. Since 38.3% of the individuals with ≥80% African ancestry have the favorable t(11;14) translocation (in comparison to 26.8% of <0.1% African individuals), this finding may explain improved survival with equal access to care. Understanding the genetic mechanisms of this disparity is a fundamental step to understanding differences in incidence and outcomes of patients with monoclonal gammopathies. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding; Celgene: Research Funding. Stewart:Amgen Inc., BMS, Celgene, Takeda, Roche, Seattle Genetics, Janssen, Ono: Consultancy; Amgen Inc., Celgene, Roche, Seattle Genetics: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Kumar:Novartis: Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding.