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• Sequencing data from MethylScan assay on plasma

  Machine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predictions that disadvantage minority populations. Existing bias correction methods face limitations in handling the heterogeneity of biomedical data and the complexity of demographic influences. Here, we present DeBias, a computational framework for mitigating demographic biases in high-dimensional biomedical datasets. DeBias eliminates bias-associated subspaces within the entire feature space using control samples and enables global bias correction across all features, effectively preserving disease-specific signals while minimizing demographic distortions. To demonstrate its effectiveness, we applied DeBias to cfDNA methylation data for cancer detection. It achieved a >6-fold reduction in the number of features containing biases and outperformed existing methods in improving the cancer detection performance for minority populations. Our method represented a promising step toward developing machine learning models that are more accurate, reliable, and equitable for minority populations.

  Study EGAS00001008127

• Whole Genome Profiling to Detect Schizophrenia Methylation Markers

  Schizophrenia (SCZ) is a devastating psychiatric disorder. The aim of this study was to detect DNA methylation (5meC) profiles in whole blood associated with SCZ. The sample consisted of schizophrenia cases and controls selected from national population registers in Sweden. We employed MBD protein-based enrichment of the methylated DNA fraction, followed by single end sequencing (50 bp reads) on the SOLiD platform. To improve methylome-wide coverage we used an existing protocol variant, which increases the relative number of fragments from CpG poor regions. To analyze the data, we used an analysis pipeline described elsewhere7, that was designed specifically for MBD-seq and included alignment, quality control (QC) of reads and samples, coverage calculations for the 26,752,702 autosomal CpGs in the reference genome using a non-parametric estimate of the fragment size distribution, an in silico experiment to identify CpGs in loci with alignment problems, a 2-stage adaptive algorithm to combine data from correlated adjacent CpG sites in "blocks", principal component analysis (PCA), association testing, bioinformatics annotation and network analysis. After QC, 1459 subjects with on average 32.4 million high quality reads remained. Association testing was performed on 4.3 million high quality blocks after regressing out possible assay related technical artifacts, sex/age, and unmeasured confounders through the PCA components.

  Study phs000608

• The Spatio-Temporal Evolution of Multiple Myeloma from Baseline to Relapse-Refractory States

  In multiple myeloma, the spatio-temporal evolution patterns of cancer cells in the bone marrow remain largely unknown. Here, we applied spatial-longitudinal sequencing, including a total of 179 samples collected from 25 patients during a follow-up of up to 14 years. Our study highlights focal bone lesions (FL) as hotspots of tumor evolution and provides possible explanations for their negative prognostic impact. We show a close phylogenetic relationship between baseline FL and relapse clones. In patients with ≥3 FDG-PET-positive FL at diagnosis, relapse was driven by multiple distinct sub-clones, whereas in other patients, a single-cell expansion was typically seen (p<0.01). Applying imaging-guided sampling, we identified a novel evolutionary pattern with unique treatment-resistant sub-clones at distinct locations. We observed multiple selective clonal sweeps, resistant clones that can be hidden over years, and epigenetic modifier mutations as a new class of relapse aberrations. Here we show that imaging can enhance tumor evolution studies and response assessment. The sophisticated evolution mechanisms in myeloma and presence of single cells that can drive relapse even after 10 years of remission suggest that a prerequisite for curative therapies would be to overcome not only tumor heterogeneity but also dormancy.

  Study phs002625

• CIDR: Genome Wide Association Study in Familial Parkinson Disease (PD)

  This proposal brings together the two largest NIH funded genetic studies focused on the identification of novel genes that influence the risk of PD. These two studies, PROGENI (PI: Tatiana Foroud; R01NS037167) and GenePD (PI: Richard Myers; R01NS036711) have been evaluating and recruiting families with two or more PD affected members for more than 8 years and represent the largest such cohorts world-wide. The combined sample has more than 1,000 PD families. Each study has used rigorous clinical criteria to assess their study participants. Unlike previous genome wide association studies (GWAS) in PD, all the PD cases in this proposal have a positive family history of disease. In the vast majority of these families, the index PD case has at least one sibling with the disease. Thus, the sample is unique for having substantial evidence for a genetic contribution to disease. The control group for this study consists of samples previously collected and maintained by the NINDS Repository. Genome-wide, single nucleotide polymorphism (SNP) genotyping services were provided by the Center for Inherited Disease Research (CIDR). Data analyses will focus on the identification of SNPs associated with PD susceptibility and the age of onset of disease.

  Study phs000126

• A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans

  Over 2.8 million African Americans have type 2 diabetes mellitus (T2DM). This represents approximately 13% of the African American population and a significant proportion of the 21 million Americans living with diabetes. On average, an African American individual is twice as likely to have T2DM as a European American peer. Our research group has been actively involved in the study of African American diabetes genetics for over 15 years. We hypothesize that genes contributing to the development of T2DM in African Americans exist and can be located using modern molecular genetic methods. With the exception of TCF7L2(Transcription factor 7-like 2) evidence to date suggests variants that contribute T2DM risk in European-derived populations are not significant contributors to African American T2DM risk. We have performed a SNP-based Whole Genome Association (WGA) analysis on the Affymetrix 6.0 in a case control population of over 1000 African American T2DM cases and over 1000 non-diabetic controls. The DNAs have been collected from participants using uniform criteria from North Carolina and neighboring states. Genotype data will be subjected to extensive genetic analysis with the goal of defining a priority list of SNPs for genotyping in independent populations for confirmation and further detailed analysis.

  Study phs000140

• Targeted MitoExome Sequencing of Mitochondrial OXPHOS Diseases (Massachusetts General Hospital)

  Human disorders of mitochondrial oxidative phosphorylation (OXPHOS) represent a devastating collection of inherited diseases. These disorders impact at least 1:5000 live births, and are characterized by multi-organ system involvement. They are characterized by remarkable locus heterogeneity, with mutations in the mtDNA as well as in over 77 nuclear genes identified to date. It is estimated that additional genes may be mutated in these disorders. To discover the genetic causes of mitochondrial OXPHOS diseases, we performed targeted, deep sequencing of the entire mitochondrial genome (mtDNA) and the coding exons of over 1000 nuclear genes encoding the mitochondrial proteome. We applied this 'MitoExome' sequencing to 124 unrelated patients with a wide range of OXPHOS disease presentations from the Massachusetts General Hospital Mitochondrial Disorders Clinic. The 2.3Mb targeted region was captured by hybrid selection and Illumina sequenced with paired 76bp reads. The total set of 1605 targeted nuclear genes included 1013 genes with strong evidence of mitochondrial localization from the MitoCarta database, 377 genes with weaker evidence of mitochondrial localization from the MitoP2 database and other sources, and 215 genes known to cause other inborn errors of metabolism. Approximately 88% of targeted bases were well-covered (>20X), with mean 200X coverage per targeted base.

  Study phs000339

• Genomic Wide Scans for Female Osteoporosis Genes

  Osteoporosis is a condition of excessive skeletal fragility which results in high risk to low trauma fractures. It is the most prevalent metabolic bone disease and is a major public health problem which may result in devastating morbidity and mortality. The most powerful, measurable determinant of fracture risk is bone mineral density (BMD). More than 60% of BMD variation is attributable to genetic factors. There are gender differences in BMD that contribute to a substantially higher fracture risk among women than men. Genetic studies demonstrate that some osteoporosis risk genes/genomic regions are gender specific. However, specific such genes contributing to female BMD and to the sex differences of BMD are largely unknown. Recent rapid progresses in SNP genotyping technology, in our knowledge about human genome diversity and linkage disequilibrium (LD) patterns in the human genome as revealed have made it feasible and timely to pursue a powerful whole genome-wide association study (GWAS) to identify genes for BMD. The major goal of this project is to perform a powerful GWAS study in a large sample of US Caucasian subjects. Gender specific effects of the genetic variants will be examined. The significant genetic variants discovered will be used to design diagnostic DNA chips for prognosis for potential health problems of osteoporosis later in life.

  Study phs000390

• Identification of Host Genetic Factors That Are Determinant for the Development of Severe Forms of COVID-19

  We conducted whole exome sequencing (WXS) on 21 cases of Multisystem Inflammatory Syndrome in Children (MIS-C) related to COVID-19 from Brazil. All patients who were hospitalized underwent serum (ELISA) and molecular (RT-PCR) testing for SARS-CoV-2, and the main clinical symptoms associated with SARS-CoV-2 infection were collected. During hospitalization, data on complications, medical interventions, and laboratory findings were also collected. Our results revealed in an unprecedented way the occurrence of several rare loss-of-function variants in the NLRP12 gene among the affected children, and three other SNVs, predicted to be highly pathogenic, were identified in the IL17RC gene. An additional nonsense variant, in the IFNA10 gene, was identified in a single patient. Through in vitro functional analysis, we unequivocally demonstrated that these mutations impact NF-kB activation. These findings are similar to those observed in NLRP12-associated systemic autoinflammatory disorders, characterized by altered innate immune response, with increased NF-κB activation and excessive production of inflammatory cytokines. To our knowledge, our study is the first to provide a plausible molecular mechanism for MIS-C involving inborn errors in NLRP12. We suggest that some patients with MIS-C may benefit from Interleukin (IL)-1 pathway blockade treatments.

  Study phs003512

• Genetic Predictors of Ibrutinib-Related Cardiovascular Side Effects in Patients with Chronic Lymphocytic Leukemia

  The goal of this study was to identify genetic polymorphisms associated with ibrutinib-related cardiovascular side effects (CVSEs). This is an observational non-interventional pharmacogenomic study of 50 patients with newly diagnosed or relapsed refractory chronic lymphocytic leukemia (NDCLL or RRCLL) receiving ibrutinib 420 mg for at least six months. The ibrutinib-related CVSEs included atrial fibrillation and hypertension, which occurred in 20% of patients. DNA obtained from buccal swabs was genotyped for 40 single nucleotide polymorphisms (SNPs) in GATA4, SGK1, KCNQ1, KCNA5, NPPA, and SCN5A using a customized next generation sequencing panel. It was found that GATA4 rs804280 AA (P =.043), KCNQ1 rs163182 GG (P =.036) and KCNQ1 rs2237895 AA (P =.023) were associated with the ibrutinib-related CVSEs. Patients with a high-risk score had at least two of the three significant risk genotypes identified in univariate analysis for GATA4 rs804280 A>C, KCNQ1 rs162182 G>C and KCNQ1 rs2237895 A>C, and patients with a low-risk score had either one or none of these genotypes. In multivariate analysis, 18 patients with at least two of the three high-risk genotypes had an 11.5-fold higher odds of CVSEs (P =.019; 1.79-119.73 at 95% CI).

  Study phs003370

• Dataset developed for use with EOSC4Cancer of synthetic colorectal cancer tumor/normal pairs.

  This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. The NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) was utilized to synthetize these 10 pairs of tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) were learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aimed to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient was down-sampled randomly, representing 50% germline variants of a given patient. These were mixed with the other 50% in silico germline variants that were modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learnt from a real CRC patient was added to the germline variant profile used for creating the normal synthetic WGS data of the same patient, consisting of the variants for tumor sample. Neither copy number profile nor structural variation profile was introduced into the tumor synthetic WGS data. Tumor content and ploidy were assumed to be 100% and 2, respectively. For mapping/variant detection, the Sarek pipeline v3.1.2 (https://nf-co.re/sarek/3.1.2) was used, specifically: 1. BWA v0.7.17-r1188 for read mapping 2. GATK v4.3.0.0 for pre-processing BAM file (including markduplicates and recalibration). 2. Mutect2 (GATK v4.3.0.0) for somatic variant calling 3. Strelka2 v2.9.10 for germline and somatic variant calling Metadata information of 10 CRC patients used for the generation of synthetic normal and tumor WGS data: Patient_id Tumor_barcode Normal_barcode Age Sex Tissue Cancer SIM007 SIM007_T SIM007_N 71 F Rectal Primary CRC SIM008 SIM008_T SIM008_N 45 F Colon Neuroendocrine Metastasis CRC SIM010 SIM010_T SIM010_N 62 M Colon Metastasis CRC SIM011 SIM011_T SIM011_N 55 M Colon Neuroendocrine Metastasis CRC SIM012 SIM012_T SIM012_N 57 M Rectal Metastasis CRC SIM013 SIM013_T SIM013_N 69 M Colon Metastasis CRC SIM014 SIM014_T SIM014_N 68 M Colon Neuroendocrine primary CRC SIM015 SIM015_T SIM015_N 58 F Colon Primary CRC SIM016 SIM016_T SIM016_N 49 M Colon/Rectal Primary CRC SIM017 SIM017_T SIM017_N 78 M Colon Neuroendocrine primary CRC

  Dataset EGAD50000000564