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• Batch1-2_Genotypes_PostQC

  Combined genotypes for Batch 1 and 2 after quality control. All individuals included in analyses.

  Dataset EGAD00010002127

• scoop-G-1

  Human Core Exome Genotyping for 1456 severe early onset obesity cases (SCOOP)

  Dataset EGAD00010001623

• Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments

  Dataset EGAD00001001602

• Reference epigenome OB56_N_PreA_mRNA-Seq data generated from KEP study

  A OB56_N_PreA_mRNA-Seq paired end data for Preadipocytes(fat)

  Dataset EGAD00001003486

• Reference epigenome IPS05_X_NPC_WGBS data generated from KEP study

  A IPS05_X_NPC_WGBS paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003477

• Reference epigenome IPS06_X_ENeuron_WGBS data generated from KEP study

  A IPS06_X_ENeuron_WGBS paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003478

• SSBP1 sample

  The dataset includes exome sequencing results for a patient with SSBP1 mutations that cause a complex optic atrophy spectrum disorder

  Dataset EGAD00001005475

• Germline snv g.vcf for EGAS00001004572

  Bulk Germline snv vcfs from haplotypecaller analysis in EGAS00001004572

  Dataset EGAD00001006910

• African American Multiple Myeloma GWAS

  The case set is from a case-control study designed to identify common genetic risk factors for multiple myeloma in African Americans, the population with the highest risk for this cancer. We conducted two GWAS and combined each of these with convenience controls consisting of unaffected African American participants in cohorts with existing GWAS data. We then conducted a meta-analysis of the two sets. Cases were persons of African ancestry with smoldering or active multiple myeloma identified at participating oncology clinics or through SEER registries, as part of the African American Multiple Myeloma Study (AAMMS) diagnosed from Jan 1, 1988 through July 31, 2016. The majority of the samples were collected from incident and prevalent cases diagnosed since Jan 1, 2008 (80.9%), with a minority obtained from biobanks from cases diagnosed prior to 2008 (19.1%). Additional samples were obtained from the Multiethnic Cohort (USC and University of Hawaii) (n=40), the University of California at San Francisco Multiple Myeloma Study (n=27), and from the Multiple Myeloma Research Consortium for secondary analysis (samples originally provided to MMRC by 8 additional sites (n=84)). We have identified the phenotype (smoldering myeloma, plasma cell multiple myeloma, or myeloma not otherwise specified (myeloma NOS) when myeloma phenotype was not known), sex and age at diagnosis in this data set. The initial GWAS set consisted of 1308 (1,305 passed QC) cases with DNA samples, with a GWAS performed on the Illumina Human Core. Controls consisted of 7,078 unaffected African American subjects who were participants in the African American Prostate and Breast Consortium, with existing GWAS data from the Illumina1M Duo BeadChip. The second GWAS set consisted of 529 African American multiple myeloma patients with samples (406 from University of Arkansas, results not contained in this dataset because NCI funds were not used for the collection and genotyping) with GWAS data resulting from the Illumina Mega-BeadChip v1.1. Controls were 2,390 unaffected African American participants in the Multiethnic Cohort with existing GWAS data from the same array. After QC and removal of duplicates within sets, sex mismatches, and removal of plasmacytoma cases (ICD-0 code 9731), this deposited data set contains the typed GWAS data for the Illumina Human Core (set 1) (n=1298), and for the MegaBead Chip v1.1 (set 2) (n=123), with the University of Arkansas samples removed, for a total of 1,421 case genotypes. Note that 13 samples that overlap set 1 and set 2 were included.

  Study phs001632

• RNAseq dataset for MALT1 in MCL

  Manuscript Title: Co-targeting of BTK and MALT1 overcomes resistance to BTK inhibitors in mantle cell lymphoma Journal: Journal of Clinical Investigation Authors Vivian Changying Jiang1, Yang Liu1, Junwei Lian1, Shengjian Huang1, Alexa Jordan1, Qingsong Cai1, Fangfang Yan3, Joseph Mitchell McIntosh1, Yijing Li1, Yuxuan Che1, Zhihong Chen1, Jovanny Vargas1, Maria Badillo1, JohnNelson Bigcal1, Heng-Huan Lee1, Wei Wang1, Yixin Yao1, Lei Nie1, Christopher Flowers1, and Michael Wang1, 2* Abstract Bruton’s tyrosine kinase (BTK) is a proven target in mantle cell lymphoma (MCL), an aggressive subtype of non-Hodgkin lymphoma. However, resistance to BTK inhibitors is a major clinical challenge. We here report that MALT1 is one of the top overexpressed genes in ibrutinib-resistant MCL cells, while expression of CARD11, which is upstream of MALT1, is decreased. MALT1 genetic knockout or inhibition produced dramatic defects in MCL cell growth regardless of ibrutinib sensitivity. Conversely, CARD11 knockout cells showed anti-tumor effects only in ibrutinib-sensitive cells, suggesting that MALT1 overexpression could drive ibrutinib resistance via bypassing BTK-CARD11 signaling. Additionally, BTK knockdown and MALT1 knockout markedly impaired MCL tumor migration and dissemination, and MALT1 pharmacological inhibition decreased MCL cell viability, adhesion, and migration by suppressing NF-κB, PI3K-ATK-mTOR, and integrin signaling. Importantly, co-targeting MALT1 with safimaltib and BTK with pirtobrutinib induced potent anti-MCL activity in ibrutinib-resistant MCL cell lines and patient-derived xenografts. Therefore, we conclude that MALT1 overexpression associates with resistance to BTK inhibitors in MCL, targeting abnormal MALT1 activity could be a promising therapeutic strategy to overcome BTK inhibitor resistance, and co-targeting of MALT1 and BTK should improve MCL treatment efficacy and durability as well as patient outcomes. Dataset description: The bulk RNA-seq dataset was generated for the cell lines below and used for two major purposes: 1. DEG analysis and GSEA analysis comparing IBN-R and IBN-S cells 2. DEG analysis and GSEA analysis comparing MCL cells with/without MI-2 treatment. sample Cell MI-2 Ibrutinib (IBN) Venetoclax (VEN) Used for IBN-R vs IBN-S comparison Used for MI-2 vs untreated (DMSO) H9 Granta519 - R S yes H21 Granta519 - R S yes H33 Granta519 - R S yes H10 Granta519-VEN-R - R R yes H22 Granta519-VEN-R - R R yes H34 Granta519-VEN-R - R R yes H3 JeKo BTK KD_1 - R R yes yes H15 JeKo BTK KD_1 - R R yes yes H27 JeKo BTK KD_1 - R R yes yes H5 JeKo BTK KD_2 - R R yes yes H17 JeKo BTK KD_2 - R R yes yes H29 JeKo BTK KD_2 - R R yes yes H1 JeKo-1 - S R yes yes H13 JeKo-1 - S R yes yes H25 JeKo-1 - S R yes yes H7 Mino - S S yes H19 Mino - S S yes H31 Mino - S S yes H8 Mino-VEN-R - S R yes H20 Mino-VEN-R - S R yes H32 Mino-VEN-R - S R yes H11 Rec-1 - S S yes H23 Rec-1 - S S yes H12 Rec-VEN-R - S S yes H24 Rec-VEN-R - S R yes H36 Rec-VEN-R - S R yes H35 Rec-1 -- S R yes H4 JeKo BTK KD_1 + MI-2 + yes H16 JeKo BTK KD_1 + MI-2 + yes H28 JeKo BTK KD_1 + MI-2 + yes H6 JeKo BTK KD_2 + MI-2 + yes H18 JeKo BTK KD_2 + MI-2 + yes H30 JeKo BTK KD_2 + MI-2 + yes H2 JeKo-1 + MI-2 + yes H14 JeKo-1 + MI-2 + yes H26 JeKo-1 + MI-2 + yes

  Dataset EGAD00001009771