SNP-based Approach Research Articles

Ancestry-, Sex-, and Age-Based Differences of Gene Alterations in NSCLC: From the Real-World Data of Cancer Genomic Profiling Tests.

Some genomic alterations in non-small cell lung cancer (NSCLC) are known to differ according to race, sex, or age. These studies have been limited in sample size and thus they cannot detect the differences precisely and comprehensively. Tissue-based comprehensive genomic profiling was performed on 75,362 patients with NSCLC from the United States during routine clinical care. Additionally, we examined data of a Japanese NSCLC cohort with 1,019 patients. In the US cohort, 296 genes were examined for pathogenic alterations. Predominant genetic ancestry was inferred using a SNP-based approach, and patients were categorized into European (EUR), African (AFR), East Asian (EAS), Admixed American (AMR), and South Asian (SAS) ancestry groups. Patients were additionally stratified by histologic type, age (<40/≥40 years, <75/≥75 years), and sex. The prevalence of high tumor mutational burden (TMB-High) and microsatellite instability status was also calculated. Stratified by ancestry, EGFR alterations were significantly enriched in EAS versus other ancestry groups. The prevalence of ALK was significantly higher in the AMR, EAS, and SAS patients than in AFR and EUR patients. KRAS and STK11 were enriched in EUR and AFR patients versus other groups. TMB-High was significantly enriched in AFR patients versus all other groups. An analysis based on sex revealed differences in prevalence of alterations in 80 genes and TMB-High status. For example, EGFR, ALK, BRAF, and KRAS alterations were significantly enriched in females, whereas TP53, STK11, KEAP1, and TMB-High were significantly enriched in males. With respect to age, the prevalence of alterations in 41 genes, including ALK, RET, MET, EGFR, STK11, KEAP1, BRAF, and KRAS, as well as TMB-High, were significantly different between patients aged <40 years and those aged ≥40 years. Comprehensive analysis from a large real-world dataset revealed ancestry-associated differences in genomic alterations in NSCLC. Age- and sex-related differences in prevalence of genomic alterations and TMB-High status were also observed.

Genetic ancestry-associated differences in genomic profiling and treatment patterns in pancreatic ductal adenocarcinoma (PDAC).

4138 Background: Prior epidemiological studies have explored race both as a population-level risk factor and as a factor influencing outcomes in PDAC. However, the impact of genetic ancestry on PDAC remains unclear. We assessed the genomic landscape, clinical care patterns, and outcomes across ancestries in a large and diverse PDAC cohort to determine the potential impact of genomics on ancestral disparities. Methods: A total of 24,948 patients diagnosed with PDAC who received tissue-biopsy based comprehensive genomic profiling (CGP; Foundation Medicine, Inc. (FMI)) as part of routine clinical care from Aug 2014-Sep 2023 were examined for genomic differences based on genetic ancestry which was determined using a SNP-based approach. The US-based de-identified Flatiron Health (FH)-FMI clinico-genomic database (CGDB) was used to assess testing and treatment patterns based on ancestry in 3,863 patients of European (EUR) and 414 patients of African (AFR) ancestry diagnosed with PDAC. Results: The genetic ancestry distribution in the overall cohort was 80.9% EUR, 10.0% AFR, 4.9% Admixed American, 3.5% East Asian, and 0.7% South Asian. AFR patients showed a slightly younger median age at biopsy than EUR (64 vs 67, p &lt; .001). KRAS, TP53, CDKN2A/ 2B and SMAD4 were the most commonly altered genes in the cohort, as expected. Compared to EUR, AFR patients were enriched in alterations in TP53 (85% vs 78%, p &lt; .001) and KRAS (95% vs 93%, p &lt; .001). While rare, alterations in GNAS, CHEK2, and BRAF were observed less frequently in AFR (1.7% vs 3.3%, 0.3% vs 1.7%, 0.9% vs 2.2%, respectively, all p &lt; .001). In the CGDB cohort, AFR patients were likely to have lower socioeconomic status (SES) compared to EUR patients (33% of AFR in the lowest SES quintile compared to 9% of EUR; p &lt; .001 across all quintiles). AFR patients were less likely to receive care at academic centers (AFR 14.8% vs EUR 22.2%, p &lt; .001) and less likely to receive an investigational drug during their cancer treatment course (5.9% vs 14.1%, p = .001). Rates of surgery (AFR 30.2% vs EUR 34.6%, p = .08), immunotherapy (3.2% vs 3.3%, p &gt; .99), and targeted therapy (4.1% vs 6.2%, p = .28) were not significantly different. While AFR patients comprised a significantly larger proportion of AFR+EUR CGP volume following the decision to implement Medicare coverage for NGS testing (10.6% after Mar 16, 2018 vs 6.6% prior, p &lt; .001), this remained below the expected based on share of the overall US population. Conclusions: To our knowledge, this is the largest study of genetic ancestry in PDAC to date. There were largely similar patterns of gene alterations across AFR and EUR ancestry groups suggesting non-genomic factors may underlie clinical outcomes disparities which have been reported in other studies. Notably, there were ancestry-based differences associated with SES and both CGP testing and clinical trial enrollment patterns, which may impact patient outcomes.

Genomic prediction within and across maize landrace derived populations using haplotypes.

Genomic prediction (GP) using haplotypes is considered advantageous compared to GP solely reliant on single nucleotide polymorphisms (SNPs), owing to haplotypes' enhanced ability to capture ancestral information and their higher linkage disequilibrium with quantitative trait loci (QTL). Many empirical studies supported the advantages of haplotype-based GP over SNP-based approaches. Nevertheless, the performance of haplotype-based GP can vary significantly depending on multiple factors, including the traits being studied, the genetic structure of the population under investigation, and the particular method employed for haplotype construction. In this study, we compared haplotype and SNP based prediction accuracies in four populations derived from European maize landraces. Populations comprised either doubled haploid lines (DH) derived directly from landraces, or gamete capture lines (GC) derived from crosses of the landraces with an inbred line. For two different landraces, both types of populations were generated, genotyped with 600k SNPs and phenotyped as lines per se for five traits. Our study explores three prediction scenarios: (i) within each of the four populations, (ii) across DH and GC populations from the same landrace, and (iii) across landraces using either DH or GC populations. Three haplotype construction methods were evaluated: 1. fixed-window blocks (FixedHB), 2. LD-based blocks (HaploView), and 3. IBD-based blocks (HaploBlocker). In within population predictions, FixedHB and HaploView methods performed as well as or slightly better than SNPs for all traits. HaploBlocker improved accuracy for certain traits but exhibited inferior performance for others. In prediction across populations, the parameter setting from HaploBlocker which controls the construction of shared haplotypes between populations played a crucial role for obtaining optimal results. When predicting across landraces, accuracies were low for both, SNP and haplotype approaches, but for specific traits substantial improvement was observed with HaploBlocker. This study provides recommendations for optimal haplotype construction and identifies relevant parameters for constructing haplotypes in the context of genomic prediction.

Abstract 6120: Genomic profiling of KRAS and EGFR-altered non-squamous non-small cell lung cancer reveal ancestry-specific co-alterations with therapeutic implications

Abstract Background: Racial and ethnic disparities are highly prevalent in cancer care and impact treatment outcomes, with an underrepresentation of minority populations in clinical studies. The impact of genetic ancestry on the genomic landscape of tumors remains understudied. To address this, we sought to comprehensively characterize the ancestry-based genomic co-alteration landscape and immunotherapy-related biomarkers in KRAS and EGFR-altered tumors, two major driver populations in non-squamous non-small cell lung cancer (non-Sq NSCLC). Methods: Our study consisted of 68,197 adult patients with non-Sq NSCLC who underwent comprehensive genomic profiling using FoundationOne® or FoundationOne®CDx during routine clinical care in the United States. Genetic ancestry was inferred using a SNP-based approach. Tumor mutational burden (TMB) was calculated across 0.8-1.2 megabases. PD-L1 expression was determined by immunohistochemistry using the Dako 22C3 PD-L1 antibody. Results: Overall, 81% of the patients were of European (EUR; n= 55,430), 10% of African (AFR; n=7,062), 5% of East Asian (EAS; n=3,297), 3% of Admixed American (AMR; n=2,011) and less than 1% of South Asian ancestry (SAS; n=497). KRAS was the most frequently altered oncogene in EUR (39%) and AFR (33%), whereas EGFR was most frequently altered in EAS (53%), SAS (36%), and AMR (30%) ancestry groups. While STK11 and KEAP1 alterations co-occurred with KRAS across all ancestry groups, they were significantly (FDR p &amp;lt;= 0.05) less frequent in EAS (16%) and AMR (15%) compared to EUR (28%). Additional ancestry-specific co-alteration patterns in KRAS-altered tumors included co-occurrence with GNAS alterations in AMR (Odds ratio, OR: 3.5, p &amp;lt; 10-5), co-occurrence with ARID1A alterations in SAS (OR: 4.7, p = 0.02), and mutual exclusivity with NF1 alterations in EUR (OR: 0.4, p &amp;lt; 10-5) and AFR (OR: 0.4, p &amp;lt; 10-5). In contrast, EGFR-altered tumors had a more conserved co-alteration landscape across all ancestry groups. Despite the known mutual exclusivity of KRAS and EGFR alterations, 13% of patients of EAS ancestry (compared to 2-4% of other ancestry groups) with KRAS alterations had co-occurring EGFR alterations; notably, a majority of these patients had an amplification in KRAS and/or EGFR, potentially representing treatment resistance mechanisms. Among immunotherapy-associated biomarkers, PD-L1 expression was similar across ancestries. Of note, patients of AFR ancestry with KRAS or EGFR alterations had higher TMB and the SAS ancestry group had the lowest TMB. Conclusion: Our study provides a comprehensive landscape of ancestry-specific patterns in KRAS and EGFR-altered non-Sq NSCLC. These findings can help better understand cancer disparities, aid in the development of new therapeutic strategies and inform more inclusive clinical trials and treatment decisions. Citation Format: Saumya D. Sisoudiya, Armande A. Houle, Tharu M. Fernando, Timothy R. Wilson, Jennifer L. Schutzman, Jessica K. Lee, Alexa B. Schrock, Ethan S. Sokol, Smruthy Sivakumar, Zhen Shi, Gaurav Pathria. Genomic profiling of KRAS and EGFR-altered non-squamous non-small cell lung cancer reveal ancestry-specific co-alterations with therapeutic implications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6120.

Imputation-Based HLA Typing with GWAS SNPs.

SNP-based imputation approaches for human leukocyte antigen (HLA) typing take advantage of the haplotype structure within the major histocompatibility complex (MHC) region. These methods predict HLA classical alleles using dense SNP genotypes, commonly found on array-based platforms used in genome-wide association studies (GWAS). The analysis of HLA classical alleles can be conducted on current SNP datasets at no additional cost. Here, we describe the workflow of HIBAG, an imputation method with attribute bagging, to infer a sample's HLA classical alleles using SNP data. Two examples are offered to demonstrate the functionality using public HLA and SNP data from the latest release of the 1000 Genomes project: genotype imputation using pre-built classifiers in a GWAS, and model training to create a new prediction model. The GPU implementation facilitates model building, making it hundreds of times faster compared to the single-threaded implementation.

Evaluation of core genome and whole genome multilocus sequence typing schemes for Campylobacter jejuni and Campylobacter coli outbreak detection in the USA.

Campylobacter is a leading causing of bacterial foodborne and zoonotic illnesses in the USA. Pulsed-field gene electrophoresis (PFGE) and 7-gene multilocus sequence typing (MLST) have been historically used to differentiate sporadic from outbreak Campylobacter isolates. Whole genome sequencing (WGS) has been shown to provide superior resolution and concordance with epidemiological data when compared with PFGE and 7-gene MLST during outbreak investigations. In this study, we evaluated epidemiological concordance for high-quality SNP (hqSNP), core genome (cg)MLST and whole genome (wg)MLST to cluster or differentiate outbreak-associated and sporadic Campylobacter jejuni and Campylobacter coli isolates. Phylogenetic hqSNP, cgMLST and wgMLST analyses were also compared using Baker's gamma index (BGI) and cophenetic correlation coefficients. Pairwise distances comparing all three analysis methods were compared using linear regression models. Our results showed that 68/73 sporadic C. jejuni and C. coli isolates were differentiated from outbreak-associated isolates using all three methods. There was a high correlation between cgMLST and wgMLST analyses of the isolates; the BGI, cophenetic correlation coefficient, linear regression model R 2 and Pearson correlation coefficients were >0.90. The correlation was sometimes lower comparing hqSNP analysis to the MLST-based methods; the linear regression model R 2 and Pearson correlation coefficients were between 0.60 and 0.86, and the BGI and cophenetic correlation coefficient were between 0.63 and 0.86 for some outbreak isolates. We demonstrated that C. jejuni and C. coli isolates clustered in concordance with epidemiological data using WGS-based analysis methods. Discrepancies between allele and SNP-based approaches may reflect the differences between how genomic variation (SNPs and indels) are captured between the two methods. Since cgMLST examines allele differences in genes that are common in most isolates being compared, it is well suited to surveillance: searching large genomic databases for similar isolates is easily and efficiently done using allelic profiles. On the other hand, use of an hqSNP approach is much more computer intensive and not scalable to large sets of genomes. If further resolution between potential outbreak isolates is needed, wgMLST or hqSNP analysis can be used.

Landscape of genomic alterations (GA) in urothelial bladder carcinoma (UBC) in patients of East Asian and South Asian ancestry.

567 Background: UBC is the 10th most common cancer worldwide, with more than 550,00 new cases annually (WHO, International Agency for Research on Cancer, 2020). Rates vary by regions of the world, explained in part by the fact that different genetic ancestries demonstrate varying germline genetics, environmental exposures, and social risk factors. Methods: 8,728 UBC samples underwent hybrid capture-based comprehensive genomic profiling (CGP) to determine all classes of genomic alterations (GA), microsatellite instability (MSI) status, tumor mutational burden (TMB), and genomic trinucleotide signature. Predominant genetic ancestry was determined using a SNP-based approach using an algorithm trained on the 1000 Genomes data (Connelly et al. AACR 2018). Ancestry was classified as one of the five following categories: African (AFR), European (EUR), Central and South American (AMR), South Asian (SAS), or East Asian (EAS). Results: Of the cohort, 7,447 (85.3%) were EUR, 541 (6.2%) were AFR, 461 (5.3%) were of AMR, 74 (0.85%) were SAS, and 205 (2.3%) were EAS. Age, gender, genomic signature distributions, and MSI status (range 0.9%-1.5%) were similar in all cohorts. The frequency of TMB &gt;10 mutations/Megabase was similar in all cohorts (range 30.7%-39.1%) as was the median TMB (6.3 mutations/Megabase for all). At 67.6%, TP53 was the most frequent GA in SAS cohort. When compared with the non-SAS cohort, TERT GA were lower in the SAS cohort (58.1% vs 72.6%; p=0.06) as were GA in FGFR3 (9.5% vs 18.5%, p=0.25). In the EAS cohort, at 59.0%, GA in TP53 were the most common. TERT mutations were significantly lower in EAS compared to the non-EAS cohort (54.1% vs 72.9%; p &lt;0.001). When compared with the non-EAS cohort, PIK3CA alterations were significantly less common in the EAS cohort (12.7% vs 22.1%, p =0.005). GA in FGFR3 were similar in the EAS and non-EAS cohorts (16.6% vs 18.4%). There were no significant differences in genomic landscapes identified between the EAS and SAS cohorts when directly compared to one another. Conclusions: The results from this comprehensive genomic analysis of UBC in East Asian and South Asian patients provides important insight into the unique differences in the genomic landscapes that exist on a population level. These findings should motivate future investigators to include more diverse patient populations in clinical trials of targeted agents and immunotherapy strategies.

CDH1-mutated clinically advanced urothelial bladder cancer (UBC): A genomic landscape and real-world clinical outcome study (RWCOS).

564 Background: CDH1 mutated UBCs are characterized by plasmacytoid histology and are associated with an aggressive clinical course at the time of diagnosis. Methods: Cohort 1: 6,676 clinically advanced UBC patients (pts) underwent comprehensive genomic profiling (CGP) to evaluate all classes of genomic alterations (GA), microsatellite instability (MSI), tumor mutational burden (TMB), and genomic loss of heterozygosity (gLOH, high ≥16%). Predominant genetic ancestry was determined using a SNP-based approach and classified as one of the 5 categories: African (AFR), European (EUR), Central and South American (AMR), South Asian (SAS), or East Asian (EAS). Cohort 2: 586 UBC pts underwent a RWCOS using the nationwide (US-based) de-identified Flatiron Health-Foundation Medicine urothelial clinico-genomic database (FH-FMI CGDB). The de-identified data originated from approximately 280 US cancer clinics (~800 sites of care) Jan 2011-Apr 2022. Differences in real-world progression-free survival (rwPFS) and overall survival (rwOS) were evaluated by Cox proportional hazard models. Results: Cohort 1: 217 (3.3%) of UBC had a CDH1 short variant (SV) mutation with 65.2% featuring plasmacytoid histology. When compared with CDH1 wild-type (WT) UBC, the CDH1-mutated UBC had similar age, gender, and genetic ancestry. The CDH1-mutated UBC featured a higher frequency of MSI (2.7% vs 0.8%; p=.002), mean TMB (14.8 vs 9.9 mut/Mb p&lt;.0001), RB1 GA (52.5% vs 20.3%; p&lt;.0001), PTEN GA (9.2% vs 4.3%; p=.006) and PIK3CA GA (29.5% vs 21.8%; p=.02), but less gLOH high (6.8% vs 15.9%; p=.009), CDKN2A loss (12.4% vs 38.3%; p&lt;.0001), MTAP loss (10.1% vs 25.1%; p&lt;.0001) and FGFR3 GA (9.7% vs 18.1%; p=.002). TP53 GA were similar (62.3% vs 60.3%). Cohort 2: 22 (3.7%) featured CDH1 mutations. Compared with the CDH1 WT pts, the age, gender, ethnicity and ECOG status were similar. Evaluation of the RWCOS showed that CDH1 mutation was associated with less favorable outcomes for 270 UBC pts treated with immune checkpoint inhibitors (ICPI) including rwPFS (2.8 vs 3.5 months; p=.096) and rwOS (3.3 vs 9.5 months; p=.03). Similar comparisons for 316 UBC pts treated with chemotherapy showed no significant adverse impact of CDH1 mutation status on either rwPFS (7.9 vs 6.2 months) and rwOS (13.4 vs 13.4 months). Conclusions: In addition to its classic association with plasmacytoid histology, CDH1-mutated UBC features a unique CGP pattern including higher MSI and TMB status and activating GA in the MTOR pathway while harboring a lower FGFR3 GA frequency. RWCOS further supports that CDH1 mutation predicts resistance to ICPI-based treatments but does not impact responsiveness to chemotherapy. These results further support that CGP has the potential to customize the treatment and improve outcomes for UBC patients based on the determination of their genomic signatures.

Ancestry-based differences in gene alterations in non–small cell lung cancer: Real-world data using genetic ancestry analysis.

9125 Background: Racial differences in morbidity and mortality of non-small cell lung cancer (NSCLC) have been demonstrated and some genomic alterations (e.g. EGFR) in NSCLC are known to differ according to race. However, studies have been limited in sample size and hence, limited in their capacity to detect discrepancies in less frequently altered genes. Here, we investigated alteration prevalence in a large real-world NSCLC cohort, stratified by genetic ancestry. Methods: 75,362 NSCLC patients from the United States, were profiled with comprehensive genomic profiling (CGP) of tissue biopsy, as part of routine clinical care (Foundation Medicine Inc., FMI; Frampton GM, et al. Nat Biotechnol 2013). 296 genes targeted across all assays were examined for known/likely pathogenic alterations of all classes. Predominant genetic ancestry was inferred using a SNP-based approach (Newberg J, et al. AACR 2019), and patients were categorized into European (EUR), African (AFR), East Asian (EAS), Admixed American (AMR), and South Asian (SAS) ancestry groups. Patients were additionally stratified by histological type, age (≥40/ &lt; 40), and sex. The prevalence of high tumor mutational burden (TMB-High), defined as ≥ 10 mutations/mb (Chalmers ZR, et al. Genome Med 2017), and microsatellite instability status (Trabucco, et al. J Mol Diagn 2019) was also calculated. Results: In this NSCLC cohort, ancestry was split 82.2% for EUR, 10.0% for AFR, 4.4% for EAS, 2.7% for AMR, and 0.8% for SAS. 50.4% were female (n = 37,972) and 49.6% male (n = 37,360). The most prevalent alterations in the overall cohort included TP53 (67.5%), KRAS (30.6%), CDKN2A (29.2%), CDKN2B (17.2%), STK11 (16.1%), and EGFR (15.3%). The prevalence of TMB-High status in the overall cohort was 34.6%. Stratified by ancestry, the prevalence of EGFR alterations was significantly enriched in EAS vs. other ancestry groups ( p &lt; 0.001). KRAS and STK11 were enriched in EUR and AFR vs. other groups ( p &lt; 0.001). TMB-High status was significantly enriched in AFR vs. all other groups ( p &lt; 0.001). In EAS and SAS, TMB-High was typically reduced vs. other ancestry groups (TMB-High AFR 41.6%, AMR 20.1%, EAS 14.1%, EUR 35.5%, SAS 11.7%). A similar analysis based on sex revealed differences in prevalence of 80 gene alterations and TMB-High with sex-specific enrichments (e.g., EGFR 19.0% female vs. 11.5% male; KRAS 34.6% vs. 26.6%). With respect to age, the prevalence of 41 gene alterations and TMB-High were significantly different between samples from patients age &lt; 40 and age ≥ 40 (e.g., ALK 21.1% age &lt; 40 vs. 2.7% age ≥ 40; KRAS 13.0% vs. 30.8%; TMB-High 14.4% vs. 34.8%). Conclusions: Comprehensive analysis of this real-world dataset, which includes the largest NSCLC patient cohort, revealed ancestry-associated differences in genomic alterations in NSCLC. Age and sex were also associated with differences in genomic alteration and TMB-High prevalence.

Fine-Scale Analysis of Runs of Homozygosity Islands Affecting Fertility in Mares.

The loss of genetic variability in livestock populations bred under strict selection processes is a growing concern, as it may lead to increased inbreeding values and lower fertility, as a consequence of the “inbreeding depression” effect. This is particularly important in horses, where inbreeding levels tend to rise as individuals become more and more closely related. In this study, we evaluated the effect of increased inbreeding levels on mare fertility by combining an SNP-based genomic approach using runs of homozygosity and the estimation of genetic breeding values for reproductive traits in a large population of Pura Raza Española mares. Our results showed a negative correlation between whole-genome homozygosity and fertility estimated breeding values (EBVs) at the genome level (ρ = −0.144). However, the analysis at chromosome level revealed a wide variability, with some chromosomes showing higher correlations than others. Interestingly, the correlation was stronger (−0.241) when we repeated the analysis in a reduced dataset including the 10% most and least fertile individuals, where the latter showed an increase in average inbreeding values (FROH) of around 30%. We also found 41 genomic regions (ROHi, runs of homozygosity islands) where homozygosity increased 100-fold, 13 of which were significantly associated with fertility after cross-validation. These regions encompassed 17 candidate genes previously related to oocyte and embryo development in several species. Overall, we demonstrated the relationship between increased homozygosis at the genomic level and fertility in mares. Our findings may help to deal with the occurrence of inbreeding depression, as well as further our understanding of the mechanisms underlying fertility in mares.

Genomic imprinting of the IGF2R/AIR locus is conserved between bovines and mice

Genomic imprinting is an epigenetic phenomenon that leads to genes monoallelically expressed in a parent-of-origin-specific manner and plays an important role in the embryonic development and postnatal growth of mammals. Imprinted genes usually occur in clusters in a chromosomal region and are regulated by a cis-acting imprinting control region that involves differential DNA methylation modification. Igf2r, Slc22a2 and Slc22a3 are three maternally expressed genes on mouse chromosome 17. The paternally expressed long noncoding RNA (lncRNA) Air and the nonimprinted gene Slc22a1 are also located in the imprinted region. Comparative characterization of imprinted clusters between species is useful for us to understand the biological significance and epigenetic regulating mechanism of genomic imprinting. The aim of this study was to analyze the allelic expression pattern of AIR and SLC22A1-3 genes in cattle and to determine the role of DNA methylation in regulating gene expression. Allelic expression analysis was performed in bovine adult tissues and term placenta using an SNP-based approach. We found that IGF2R, AIR and SLC22A3 were monoallelically expressed in all detected bovine somatic tissues, including heart, liver, spleen, lung, kidney, muscle, fat and brain. In bovine placenta, IGF2R and SLC22A3 are maternally expressed; however, the AIR gene is paternally expressed. Tissue-specific monoallelic expression of SLC22A2 is detected in bovines, with monoallelic expression in the spleen and brain but biallelic expression in kidney tissues. SLC22A1 is only detected in bovine liver and kidney tissues and is biallelicly expressed, which is consistent with the imprint expression in mice. To determine the possible role of DNA methylation in regulating the monoallelic/imprinted expression of bovine IGF2R, AIR, SLC22A2, and SLC22A3 genes, we analyzed the DNA methylation status of CpG islands in the first exon of SLC22A2, the promoter region of SLC22A3 and region 2 in the second intron of the IGF2R gene by bisulfite sequencing. Two differentially methylated regions (DMRs) were detected in the first exon of bovine SLC22A3 and the common regions of IGF2R and AIR. This suggests that DNA methylation is involved in the regulation of monoallelic/imprinted expression of IGF2R, AIR and SLC22A3 genes in cattle.

A Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses.

Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.

Large- and small-scale geographic structures affecting genetic patterns across populations of an Alpine butterfly.

Understanding factors influencing patterns of genetic diversity and the population genetic structure of species is of particular importance in the current era of global climate change and habitat loss. These factors include the evolutionary history of a species as well as heterogeneity in the environment it occupies, which in turn can change across time. Most studies investigating spatio‐temporal genetic patterns have focused on patterns across wide geographic areas rather than local variation, but the latter can nevertheless be important particularly in topographically complex areas. Here, we consider these issues in the Sooty Copper butterfly (Lycaena tityrus) from the European Alps, using genome‐wide SNPs identified through RADseq. We found strong genetic differentiation within the Alps with four genetic clusters, indicating western, central, and eastern refuges, and a strong reduction of genetic diversity from west to east. This reduction in diversity may suggest that the southwestern refuge was the largest one in comparison to other refuges. Also, the high genetic diversity in the west may result from (a) admixture of different western refuges, (b) more recent demographic changes, or (c) introgression of lowland L. tityrus populations. At small spatial scales, populations were structured by several landscape features and especially by high mountain ridges and large river valleys. We detected 36 outlier loci likely under altitudinal selection, including several loci related to membranes and cellular processes. We suggest that efforts to preserve alpine L. tityrus should focus on the genetically diverse populations in the western Alps, and that the dolomite populations should be treated as genetically distinct management units, since they appear to be currently more threatened than others. This study demonstrates the usefulness of SNP‐based approaches for understanding patterns of genetic diversity, gene flow, and selection in a region that is expected to be particularly vulnerable to climate change.

Expanding the conservation genomics toolbox: Incorporating structural variants to enhance genomic studies for species of conservation concern.

Structural variants (SVs) are large rearrangements (>50 bp) within the genome that impact gene function and the content and structure of chromosomes. As a result, SVs are a significant source of functional genomic variation, that is, variation at genomic regions underpinning phenotype differences, that can have large effects on individual and population fitness. While there are increasing opportunities to investigate functional genomic variation in threatened species via single nucleotide polymorphism (SNP) data sets, SVs remain understudied despite their potential influence on fitness traits of conservation interest. In this future‐focused Opinion, we contend that characterizing SVs offers the conservation genomics community an exciting opportunity to complement SNP‐based approaches to enhance species recovery. We also leverage the existing literature–predominantly in human health, agriculture and ecoevolutionary biology–to identify approaches for readily characterizing SVs and consider how integrating these into the conservation genomics toolbox may transform the way we manage some of the world's most threatened species.

Epigenome-Wide Study Identified Methylation Sites Associated with the Risk of Obesity.

Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and reveal the mechanism whereby a number of these sites exert their effects. This study provides a framework to perform an omics-wide association study for a phenotype and to understand the mechanism whereby a rare variant causes a disease.

Ancestral characterization of the genomic landscape, comprehensive genomic profiling utilization, and treatment patterns may inform disparities in advanced prostate cancer: A large-scale analysis.

5003 Background: Prostate cancer (PCa) incidence, mortality, and outcomes vary widely across race/ethnicity. The underlying drivers of these differences are multifactorial, including systemic barriers that lead to wide variation in access to care including genomic and precision medicine. Men of African ancestry (AFR) are particularly underrepresented in genomic and precision medicine studies. Therefore, we sought to comprehensively assess patterns of gene alterations, comprehensive genomic profiling (CGP) utilization, and treatment patterns in a large, diverse advanced PCa cohort. Methods: 11,741 PCa patients with CGP, as part of routine clinical care (Foundation Medicine Inc., FMI) were evaluated for their genomic landscape. Predominant ancestry was inferred using a SNP-based approach (Connelly et al, AACR 2018). Independently, the US-based de-identified Flatiron Health (FH)-FMI clinico-genomic database (CGDB) of 897 evaluable PCa patients was also queried. Clinical characteristics and treatment selections were described for patients who received metastatic or castrate-resistant diagnosis between 1/2011 and 6/2020. Results: The FMI cohort included 1,422 (12%) men of AFR and 9,244 (79%) men of European ancestry (EUR). Median age was lower in AFR compared with EUR men (64 vs. 67, p &lt; 0.001). TP53 and PTEN alterations and TMPRSS2-ERG rearrangements occurred less frequently in AFR than EUR men (35% vs. 43%, 21% vs. 33%, 15% vs. 33% respectively, p &lt; 0.05). In contrast, alterations in SPOP (11.9% vs. 7.3%), CDK12 (10.0% vs. 5.2%), CCND1 (6.0% vs. 3.8%), KMT2D (7.7% vs. 5.1%), HGF (4.1% vs. 2.5%), and MYC (13.4% vs. 10.6%) were enriched in the AFR cohort (p &lt; 0.05). Alteration frequency in BRCA1/2, AR, DNA damage response pathway genes, and actionable genes with therapy implications, were similar across ancestry. Of note, BRAF alterations were slightly enriched in AFR (5.0% vs. 3.2%, p &lt; 0.05). In the CGDB cohort (79 AFR, 762 EUR), AFR men received a median of 2 lines of therapy prior to CGP, compared to 1 line for EUR men. Notably, the proportion of patients receiving immunotherapy and PARPi was similar across ancestry, however AFR men were less likely to receive clinical study drug compared with EUR men (11% vs 30%, p &lt; 0.001), even among men with actionable alterations (1% vs 6%, p &lt; 0.001). Conclusions: To our knowledge, this study encompasses the largest cohort, particularly of AFR men in a genomic study, that defines CGP utilization, the genomic landscape and therapeutic implications of CGP in PCa across ancestry. Overall, there were largely similar rates of actionable gene alterations across ancestry. Notably, AFR men were less likely to receive CGP earlier in their treatment course, and less likely to be treated on clinical trials, which could impact the genomic landscape, outcomes, and ultimately disparities.

EQTLHap: a tool for comprehensive eQTL analysis considering haplotypic and genotypic effects.

The high accuracy of recent haplotype phasing tools is enabling the integration of haplotype (or phase) information more widely in genetic investigations. One such possibility is phase-aware expression quantitative trait loci (eQTL) analysis, where haplotype-based analysis has the potential to detect associations that may otherwise be missed by standard SNP-based approaches. We present eQTLHap, a novel method to investigate associations between gene expression and genetic variants, considering their haplotypic and genotypic effect. Using multiple simulations based on real data, we demonstrate that phase-aware eQTL analysis significantly outperforms typical SNP-based methods when the causal genetic architecture involves multiple SNPs. We show that phase-aware eQTL analysis is robust to phasing errors, showing only a minor impact ($<4\%$) on sensitivity. Applying eQTLHap to real GEUVADIS and GTEx datasets detects numerous novel eQTLs undetected by a single-SNP approach, with 22 eQTLs replicating across studies or tissue types, highlighting the utility of phase-aware eQTL analysis. https://github.com/ziadbkh/eQTLHap. ziad.albkhetan@gmail.com. Supplementary data are available at Briefings in Bioinformatics online.

Molecular characteristics of Brucella melitensis isolates from humans in Qinghai Province, China

BackgroundThe prevalence of human brucellosis in Qinghai Province of China has been increasing rapidly, with confirmed cases distributed across 31 counties. However, the epidemiology of brucellosis transmission has not been fully elucidated. To characterize the infecting strains isolated from humans, multiple-locus variable-number tandem repeats analysis (MLVA) and whole-genome single-nucleotide polymorphism (SNP)-based approaches were employed.MethodsStrains were isolated from two males blood cultures that were confirmed Brucella melitensis positive following biotyping and MLVA. Genomic DNA was extracted from these two strains, and whole-genome sequencing was performed. Next, SNP-based phylogenetic analysis was performed to compare the two strains to 94 B. melitensis strains (complete genome and draft genome) retrieved from online databases.ResultsThe two Brucella isolates were identified as B. melitensis biovar 3 (QH2019001 and QH2019005) following conventional biotyping and were found to have differences in their variable number tandem repeats (VNTRs) using MLVA-16. Phylogenetic examination assigned the 96 strains to five genotype groups, with QH2019001 and QH2019005 assigned to the same group, but different subgroups. Moreover, the QH2019005 strain was assigned to a new subgenotype, IIj, within genotype II. These findings were then combined to determine the geographic origin of the two Brucella strains.ConclusionsUtilizing a whole-genome SNP-based approach enabled differences between the two B. melitensis strains to be more clearly resolved, and facilitated the elucidation of their different evolutionary histories. This approach also revealed that QH2019005 is a member of a new subgenotype (IIj) with an ancient origin in the eastern Mediterranean Sea.

Haplotype-based genome-wide association increases the predictability of leaf rust (Puccinia triticina) resistance in wheat.

Resistance breeding is crucial for sustainable control of wheat leaf rust and single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) are widely used to dissect leaf rust resistance. Unfortunately, GWAS based on SNPs often explained only a small proportion of the genetic variation. We compared SNP-based GWAS with a method based on functional haplotypes (FH) considering epistasis in a comprehensive hybrid wheat mapping population composed of 133 parents plus their 1574 hybrids and characterized with 626 245 high-quality SNPs. In total, 2408 and 1 139 828 significant associations were detected in the mapping population by using SNP-based and FH-based GWAS, respectively. These associations mapped to 25 and 69 candidate regions, correspondingly. SNP-based GWAS highlighted two already-known resistance genes, Lr22a and Lr34-B, while FH-based GWAS detected associations not only on these genes but also on two additional genes, Lr10 and Lr1. As revealed by a second hybrid wheat population for independent validation, the use of detected associations from SNP-based and FH-based GWAS reached predictabilities of 11.72% and 22.86%, respectively. Therefore, FH-based GWAS is not only more powerful for detecting associations, but also improves the accuracy of marker-assisted selection compared with the SNP-based approach.

Abstract 1921: The DDB2 intronic variation rs830083 is a strong predictor of gemtuzumab ozogamicin response in AML patients: A calicheamicin pathway-directed analysis from the COG-AAML0531 trial

Abstract Polymorphisms in DNA damage repair (DDR) genes could serve as predictors of response to gemtuzumab ozogamicin (GO), a CD33 targeting antibody conjugated to the DNA damaging agent calicheamicin approved for use in immunotherapy for acute myeloid leukemia (AML). GO is internalized upon binding to the myeloid cell surface antigen CD33, allowing calicheamicin-mediated DNA damage and subsequent apoptosis. Following concerns over safety, GO was withdrawn in 2010 but was re-approved in 2017 with the emergence of new evidence of its clinical efficacy and safety. In clinical trials, significantly improved event free survival (EFS), disease free survival (DFS), and relapse risk (RR) were observed in a subset of AML patients with the addition of GO to conventional AML chemotherapy. Combined with low overall survival (OS) and high RR with the relatively unchanged conventional AML chemotherapy, GO usage is anticipated to increase. We used samples from the Children's Oncology Group (COG) trial AAML0531 to determine the association between polymorphism in DDR genes and OS, EFS, DFS, and RR with GO treatment as the primary end points. In this trial, newly diagnosed AML patients were randomized to receive either the standard 5 course chemotherapy alone (Arm A) or with two doses of GO (3mg/m2/dose) during induction 1 and intensification 2 (Arm B). Briefly, genomic DNA samples from a total of 947 patients from Arm A and Arm B were genotyped for 131 SNPs across 43 key genes involved in calicheamicin-mediated DDR. Overall, 28 SNPs in 21 genes were significantly associated with OS, EFS, DFS, and RR. Of these, 21 SNPs in 16 genes were associated with response to GO treatment (Arm B; p ≤ 0.05) and not with conventional AML chemotherapy (Arm A; p ≥ 0.05). The top 5 SNPs occurred within DDB2, XPA, XPC, and XRCC1 – genes involved in the nucleotide excision repair (NER) DDR pathway. The intronic variation rs830083 (G&amp;gt;C) in DDB2 had the poorest outcome (OS Cox p = 0.01; EFS cox p = 0.004; DFS p = 0.000; RR reg p = 0.0006). Furthermore, rs830083 was also most significantly associated with standard risk patients (DFS p = 0.003), EFS (p=0.01) and RR (p = 0.0006). DDB2 has been reported to play a critical role in apoptotic processes post-DNA damage induction by regulating p53, lending further importance to the association between rs830083 and poor response to treatment. In conclusion, the identification of several variants associated with treatment outcomes in our study underscores the importance of a pathway-directed SNP-based candidate gene approach to identify personalized approaches for AML treatment. Specifically, screening DDB2 SNPs could be applied in patient selection and predicting response to GO treatment. Citation Format: Vivek M. Shastri, Lata Chauhan, Todd A. Alonzo, Yi-Cheng Wang, Richard Aplenc, Betsy A. Hirsch, Alan S. Gamis, Soheil Meshinchi, Jatinder K. Lamba. The DDB2 intronic variation rs830083 is a strong predictor of gemtuzumab ozogamicin response in AML patients: A calicheamicin pathway-directed analysis from the COG-AAML0531 trial [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1921.