6365 results for "11+1双色球多少钱一注"

in 20.46 milliseconds.

• Reference epigenome IPS01_N_Fibroblast_mRNA-Seq data generated from KEP study

  A IPS01_N_Fibroblast_mRNA-Seq paired end data for iPSC(Oct4)

  Dataset EGAD00001003488

• Targeted capture ctDNA Library CRCQV42Run035-18

  Targeted capture ctDNA Library CRCQV42Run035-18 from patient PBC0002853, plasma 3 month sample

  Dataset EGAD00001010583

• Targeted capture ctDNA Library CRCQV42Run036-6

  Targeted capture ctDNA Library CRCQV42Run036-6 from patient PBC0005064, plasma 3 month sample

  Dataset EGAD00001010604

• Targeted capture ctDNA Library CRCQV42Run043-17

  Targeted capture ctDNA Library CRCQV42Run043-17 from patient PBC0004350, plasma 3 month sample

  Dataset EGAD00001010646

• Targeted capture ctDNA Library CRCQV42Run043-4

  Targeted capture ctDNA Library CRCQV42Run043-4 from patient PBC0002294, plasma 15 month sample

  Dataset EGAD00001010654

• Targeted capture ctDNA Library CRCQV42Run043-6

  Targeted capture ctDNA Library CRCQV42Run043-6 from patient PBC0001255, plasma 15 month sample

  Dataset EGAD00001010656

• Reference epigenome Islet-derived-MSC06 WGBS data generated from KEP study

  Islet-derived_MSC06 WGBS paired end data

  Dataset EGAD00001007875

• Targeted capture ctDNA Library CRCQV34Run015-17

  Targeted capture ctDNA Library CRCQV34Run015-17 from patient PBC0002824, plasma 6 month sample

  Dataset EGAD00001010381

• Targeted capture ctDNA Library CRCQV34Run015-9

  Targeted capture ctDNA Library CRCQV34Run015-9 from patient PBC0001627, plasma 12 month sample

  Dataset EGAD00001010386

• Targeted capture ctDNA Library CRCQV34Run015-12

  Targeted capture ctDNA Library CRCQV34Run015-12 from patient PBC0002108, plasma 12 month sample

  Dataset EGAD00001010378

• Targeted capture ctDNA Library CRCQV42Run017-16

  Targeted capture ctDNA Library CRCQV42Run017-16 from patient PBC0002816, plasma 6 month sample

  Dataset EGAD00001010408

• Targeted capture ctDNA Library CRCQV42Run017-10

  Targeted capture ctDNA Library CRCQV42Run017-10 from patient PBC0002255, plasma 12 month sample

  Dataset EGAD00001010403

• Targeted capture ctDNA Library CRCQV42Run017-13

  Targeted capture ctDNA Library CRCQV42Run017-13 from patient PBC0002383, plasma 12 month sample

  Dataset EGAD00001010406

• Targeted capture ctDNA Library CRCQV42Run017-15

  Targeted capture ctDNA Library CRCQV42Run017-15 from patient PBC0001673, plasma 24 month sample

  Dataset EGAD00001010407

• Targeted capture ctDNA Library CRCQV42Run017-22

  Targeted capture ctDNA Library CRCQV42Run017-22 from patient PBC0001627, plasma 6 month sample

  Dataset EGAD00001010413

• Targeted capture ctDNA Library CRCQV42Run023-10

  Targeted capture ctDNA Library CRCQV42Run023-10 from patient PBC0001470, earliest baseline plasma sample

  Dataset EGAD00001010460

• Targeted capture ctDNA Library CRCQV42Run024-6

  Targeted capture ctDNA Library CRCQV42Run024-6 from patient PBC0001589, plasma baseline sample

  Dataset EGAD00001010474

• Targeted capture ctDNA Library CRCQV42Run026-5

  Targeted capture ctDNA Library CRCQV42Run026-5 from patient PBC0003014, plasma baseline sample

  Dataset EGAD00001010497

• Targeted capture ctDNA Library CRCQV42Run029-24

  Targeted capture ctDNA Library CRCQV42Run029-24 from patient PBC0003334, plasma 3 month sample

  Dataset EGAD00001010517

• Targeted capture ctDNA Library CRCQV42Run033-16

  Targeted capture ctDNA Library CRCQV42Run033-16 from patient PBC0002595, buffy coat sample

  Dataset EGAD00001010555

• Targeted capture ctDNA Library CRCQV42Run034-13

  Targeted capture ctDNA Library CRCQV42Run034-13 from patient PBC0004350, buffy coat sample

  Dataset EGAD00001010567

• Targeted capture ctDNA Library CRCQV42Run035-12

  Targeted capture ctDNA Library CRCQV42Run035-12 from patient PBC0004414, plasma 6 month sample

  Dataset EGAD00001010579

• Targeted capture ctDNA Library CRCQV42Run034-5

  Targeted capture ctDNA Library CRCQV42Run034-5 from patient PBC0003364, buffy coat sample

  Dataset EGAD00001010574

• Targeted capture ctDNA Library CRCQV42Run035-21

  Targeted capture ctDNA Library CRCQV42Run035-21 from patient PBC0003595, plasma 3 month sample

  Dataset EGAD00001010586

• Targeted capture ctDNA Library CRCQV42Run035-13

  Targeted capture ctDNA Library CRCQV42Run035-13 from patient PBC0001859, plasma 12 month sample

  Dataset EGAD00001010580

• Targeted capture ctDNA Library CRCQV42Run035-8

  Targeted capture ctDNA Library CRCQV42Run035-8 from patient PBC0002533, plasma 9 month sample

  Dataset EGAD00001010591

• Targeted capture ctDNA Library CRCQV42Run035-10

  Targeted capture ctDNA Library CRCQV42Run035-10 from patient PBC0002872, plasma 12 month sample

  Dataset EGAD00001010577

• Targeted capture ctDNA Library CRCQV42Run035-15

  Targeted capture ctDNA Library CRCQV42Run035-15 from patient PBC0002744, plasma 15 month sample

  Dataset EGAD00001010582

• Targeted capture ctDNA Library CRCQV42Run035-14

  Targeted capture ctDNA Library CRCQV42Run035-14 from patient PBC0001859, plasma 18 month sample

  Dataset EGAD00001010581

• Targeted capture ctDNA Library CRCQV42Run035-23

  Targeted capture ctDNA Library CRCQV42Run035-23 from patient PBC0004350, plasma 9 month sample

  Dataset EGAD00001010587

• Targeted capture ctDNA Library CRCQV42Run035-6

  Targeted capture ctDNA Library CRCQV42Run035-6 from patient PBC0002406, plasma 3 month sample

  Dataset EGAD00001010589

• Targeted capture ctDNA Library CRCQV42Run035-7

  Targeted capture ctDNA Library CRCQV42Run035-7 from patient PBC0001353, plasma 36 month sample

  Dataset EGAD00001010590

• Targeted capture ctDNA Library CRCQV42Run036-14

  Targeted capture ctDNA Library CRCQV42Run036-14 from patient PBC0001653, plasma 24 month sample

  Dataset EGAD00001010596

• Targeted capture ctDNA Library CRCQV42Run036-17

  Targeted capture ctDNA Library CRCQV42Run036-17 from patient PBC0001299, plasma 36 month sample

  Dataset EGAD00001010597

• Targeted capture ctDNA Library CRCQV42Run036-19

  Targeted capture ctDNA Library CRCQV42Run036-19 from patient PBC0004076, plasma 9 month sample

  Dataset EGAD00001010598

• Targeted capture ctDNA Library CRCQV42Run039-10

  Targeted capture ctDNA Library CRCQV42Run039-10 from patient PBC0001299, plasma 9 month sample

  Dataset EGAD00001010606

• Targeted capture ctDNA Library CRCQV42Run039-12

  Targeted capture ctDNA Library CRCQV42Run039-12 from patient PBC0001328, plasma 6 month sample

  Dataset EGAD00001010608

• Targeted capture ctDNA Library CRCQV42Run039-14

  Targeted capture ctDNA Library CRCQV42Run039-14 from patient PBC0001353, plasma 6 month sample

  Dataset EGAD00001010609

• Targeted capture ctDNA Library CRCQV42Run039-15

  Targeted capture ctDNA Library CRCQV42Run039-15 from patient PBC0001432, plasma 6 month sample

  Dataset EGAD00001010610

• Targeted capture ctDNA Library CRCQV42Run039-18

  Targeted capture ctDNA Library CRCQV42Run039-18 from patient PBC0002108, plasma 6 month sample

  Dataset EGAD00001010613

• Targeted capture ctDNA Library CRCQV42Run039-19

  Targeted capture ctDNA Library CRCQV42Run039-19 from patient PBC0002383, plasma 6 month sample

  Dataset EGAD00001010614

• Targeted capture ctDNA Library CRCQV42Run039-22

  Targeted capture ctDNA Library CRCQV42Run039-22 from patient PBC0003364, plasma 6 month sample

  Dataset EGAD00001010617

• Targeted capture ctDNA Library CRCQV42Run039-23

  Targeted capture ctDNA Library CRCQV42Run039-23 from patient PBC0003643, plasma 15 month sample

  Dataset EGAD00001010618

• Targeted capture ctDNA Library CRCQV42Run041-7

  Targeted capture ctDNA Library CRCQV42Run041-7 from patient PBC0001404, plasma 6 month sample

  Dataset EGAD00001010637

• Targeted capture ctDNA Library CRCQV42Run043-12

  Targeted capture ctDNA Library CRCQV42Run043-12 from patient PBC0003385, plasma 6 month sample

  Dataset EGAD00001010642

• Targeted capture ctDNA Library CRCQV42Run043-10

  Targeted capture ctDNA Library CRCQV42Run043-10 from patient PBC0001051, plasma 6 month sample

  Dataset EGAD00001010640

• Targeted capture ctDNA Library CRCQV42Run043-13

  Targeted capture ctDNA Library CRCQV42Run043-13 from patient PBC0001295, plasma 6 month sample

  Dataset EGAD00001010643

• Targeted capture ctDNA Library CRCQV42Run043-15

  Targeted capture ctDNA Library CRCQV42Run043-15 from patient PBC0001470, plasma 3 month sample

  Dataset EGAD00001010644

• Targeted capture ctDNA Library CRCQV42Run043-16

  Targeted capture ctDNA Library CRCQV42Run043-16 from patient PBC0002255, plasma 3 month sample

  Dataset EGAD00001010645

• Targeted capture ctDNA Library CRCQV42Run044-4

  Targeted capture ctDNA Library CRCQV42Run044-4 from patient PBC0001048, plasma 6 month sample

  Dataset EGAD00001010675

• Targeted capture ctDNA Library CRCQV42Run044-12

  Targeted capture ctDNA Library CRCQV42Run044-12 from patient PBC0001550, plasma 3 month sample

  Dataset EGAD00001010662

• Targeted capture ctDNA Library CRCQV42Run044-13

  Targeted capture ctDNA Library CRCQV42Run044-13 from patient PBC0001665, plasma 3 month sample

  Dataset EGAD00001010663

• Targeted capture ctDNA Library CRCQV42Run044-14

  Targeted capture ctDNA Library CRCQV42Run044-14 from patient PBC0001667, plasma 3 month sample

  Dataset EGAD00001010664

• Targeted capture ctDNA Library CRCQV42Run044-23

  Targeted capture ctDNA Library CRCQV42Run044-23 from patient PBC0005498, plasma 3 month sample

  Dataset EGAD00001010673

• Targeted capture ctDNA Library CRCQV42Run044-24

  Targeted capture ctDNA Library CRCQV42Run044-24 from patient PBC0005531, plasma 9 month sample

  Dataset EGAD00001010674

• Targeted capture ctDNA Library CRCQV42Run044-5

  Targeted capture ctDNA Library CRCQV42Run044-5 from patient PBC0001099, plasma 6 month sample

  Dataset EGAD00001010676

• Targeted capture ctDNA Library CRCQV42Run045-19

  Targeted capture ctDNA Library CRCQV42Run045-19 from patient PBC0001311, plasma 3 month sample

  Dataset EGAD00001010690

• Targeted capture ctDNA Library CRCQV42Run045-15

  Targeted capture ctDNA Library CRCQV42Run045-15 from patient PBC0001714, plasma 3 month sample

  Dataset EGAD00001010686

• Targeted capture ctDNA Library CRCQV42Run045-21

  Targeted capture ctDNA Library CRCQV42Run045-21 from patient PBC0002651, plasma 3 month sample

  Dataset EGAD00001010692

• Targeted capture ctDNA Library CRCQV42Run045-22

  Targeted capture ctDNA Library CRCQV42Run045-22 from patient PBC0001456, plasma 6 month sample

  Dataset EGAD00001010693

• Targeted capture ctDNA Library CRCQV42Run045-23

  Targeted capture ctDNA Library CRCQV42Run045-23 from patient PBC0002481, plasma 3 month sample

  Dataset EGAD00001010694

• Targeted capture ctDNA Library CRCQV42Run045-24

  Targeted capture ctDNA Library CRCQV42Run045-24 from patient PBC0001409, plasma 3 month sample

  Dataset EGAD00001010695

• Targeted capture ctDNA Library CRCQV42Run045-4

  Targeted capture ctDNA Library CRCQV42Run045-4 from patient PBC0002458, plasma 6 month sample

  Dataset EGAD00001010696

• Targeted capture ctDNA Library CRCQV42Run045-6

  Targeted capture ctDNA Library CRCQV42Run045-6 from patient PBC0004124, plasma 6 month sample

  Dataset EGAD00001010698

• Targeted capture ctDNA Library CRCQV42Run045-8

  Targeted capture ctDNA Library CRCQV42Run045-8 from patient PBC0006360, plasma 12 month sample

  Dataset EGAD00001010700

• Targeted capture ctDNA Library CRCQV42Run046-21

  Targeted capture ctDNA Library CRCQV42Run046-21 from patient PBC0001828, plasma 9 month sample

  Dataset EGAD00001010712

• Targeted capture ctDNA Library CRCQV42Run046-24

  Targeted capture ctDNA Library CRCQV42Run046-24 from patient PBC0002494, plasma 3 month sample

  Dataset EGAD00001010715

• Targeted capture ctDNA Library CRCQV42Run046-23

  Targeted capture ctDNA Library CRCQV42Run046-23 from patient PBC0002622, plasma 9 month sample

  Dataset EGAD00001010714

• Targeted capture ctDNA Library CRCQV42Run046-4

  Targeted capture ctDNA Library CRCQV42Run046-4 from patient PBC0001776, plasma 3 month sample

  Dataset EGAD00001010716

• Targeted capture ctDNA Library CRCQV42Run046-5

  Targeted capture ctDNA Library CRCQV42Run046-5 from patient PBC0001242, plasma 3 month sample

  Dataset EGAD00001010717

• Targeted capture ctDNA Library CRCQV42Run046-9

  Targeted capture ctDNA Library CRCQV42Run046-9 from patient PBC0002769, plasma 6 month sample

  Dataset EGAD00001010721

• SNF_RNAseq_CD138p_20

  TPM matrices of counts from RNA sequencing (RNAseq) from baseline CD138+ enriched BM fractions.

  Dataset EGAD00001011148

• STAG1-ChIP-Seq of STAG2-mutated and cohesin wildtype adult AMLs

  ChIPseq targeting the cohesin subunit STAG1 in STAG2-mutated AMLs or cohesin wildtype AMLs

  Dataset EGAD00001011206

• SF11310

  SF11310 Oligodendroglioma, IDH-mutant.Tumor Location: Frontal. Age:22. Sex: Female

  Dataset EGAD00001006313

• Reference epigenome IPS04_X_Fibroblast_smRNA-Seq data generated from KEP study

  A IPS04_X_Fibroblast_smRNA-Seq single end data for iPSC(Oct4)

  Dataset EGAD00001003504

• Targeted capture ctDNA Library CRCQV34Run015-13

  Targeted capture ctDNA Library CRCQV34Run015-13 from patient PBC0002459, plasma 9 month sample

  Dataset EGAD00001010379

• Targeted capture ctDNA Library CRCQV42Run031-7

  Targeted capture ctDNA Library CRCQV42Run031-7 from patient PBC0001295, buffy coat sample

  Dataset EGAD00001010535

• Targeted capture ctDNA Library CRCQV42Run033-15

  Targeted capture ctDNA Library CRCQV42Run033-15 from patient PBC0002533, buffy coat sample

  Dataset EGAD00001010554

• Targeted capture ctDNA Library CRCQV42Run036-5

  Targeted capture ctDNA Library CRCQV42Run036-5 from patient PBC0001470, plasma 30 month sample

  Dataset EGAD00001010603

• Targeted capture ctDNA Library CRCQV42Run039-7

  Targeted capture ctDNA Library CRCQV42Run039-7 from patient PBC0002853, plasma 6 month sample

  Dataset EGAD00001010623

• Targeted capture ctDNA Library CRCQV42Run043-9

  Targeted capture ctDNA Library CRCQV42Run043-9 from patient PBC0001306, plasma 6 month sample

  Dataset EGAD00001010659

• Targeted capture ctDNA Library CRCQV42Run044-8

  Targeted capture ctDNA Library CRCQV42Run044-8 from patient PBC0001376, plasma 6 month sample

  Dataset EGAD00001010679

• Targeted capture ctDNA Library CRCQV42Run045-7

  Targeted capture ctDNA Library CRCQV42Run045-7 from patient PBC0005444, plasma 18 month sample

  Dataset EGAD00001010699

• Targeted capture ctDNA Library CRCQV42Run046-7

  Targeted capture ctDNA Library CRCQV42Run046-7 from patient PBC0001528, plasma 6 month sample

  Dataset EGAD00001010719

• Targeted capture ctDNA Library CRCQV42Run42-7

  Targeted capture ctDNA Library CRCQV42Run42-7 from patient PBC0001299, plasma 15 month sample

  Dataset EGAD00001010831

• Targeted capture ctDNA Library CRCQV42Run42-8

  Targeted capture ctDNA Library CRCQV42Run42-8 from patient PBC0003385, plasma 15 month sample

  Dataset EGAD00001010832

• TXT_CD138P_15

  TPM matrices of counts from RNA sequencing (RNAseq) from longitudinal CD138+ enriched BM fractions.

  Dataset EGAD00001011144

• Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Multi-Ethnic Samples (T2D-GENES) Project 1: KARE

  T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Multi-Ethnic Samples) is a NIDDK-funded international research consortium which seeks to identify genetic variants for type 2 diabetes (T2D) through multiethnic sequencing studies. T2D-GENES Project 1 is a multi-ethnic sequencing study designed to assess whether less common variants play a role in T2D risk and to assess similarities and differences in the distribution of T2D risk variants across ancestry groups. The individuals were obtained from 14 cohorts that are listed in Table 1. The strategy was to perform deep exome sequencing of 12,940 individuals, 6,504 with T2D and 6,436 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. Sequencing was performed at the Broad Institute using the Agilent v2 capture reagent on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study does not have a sub study, as it is not consented to be deposited in dbGAP. Table 1. T2D-GENES Whole Exome Sequencing Studies Ancestry Study Countries of Origin # Cases # Controls African American Jackson Heart Study US 502 527 African American Wake Forest School of Medicine Study US 518 532 East Asian Korea Association Research Project Korea 526 561 East Asian Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 486 592 European Ashkenazi US, Israel 506 352 European Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European Malmö-Botnia Study Finland, Sweden 478 443 Hispanic San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 219 Hispanic Starr County, Texas US 749 704 South Asian London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 530 538 South Asian Singapore Indian Eye Study Singapore (Indian Asian) 563 585 The Korea Association Research Project (KARE) studies contributed 526 cases and 561 controls to T2D-GENES Project 1.

  Study phs001096

• Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Multi-Ethnic Samples (T2D-GENES) Project 1: Ashkenazi

  T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Multi-Ethnic Samples) is a NIDDK-funded international research consortium which seeks to identify genetic variants for type 2 diabetes (T2D) through multiethnic sequencing studies. T2D-GENES Project 1 is a multi-ethnic sequencing study designed to assess whether less common variants play a role in T2D risk and to assess similarities and differences in the distribution of T2D risk variants across ancestry groups. The individuals were obtained from 14 cohorts that are listed in Table 1. The strategy was to perform deep exome sequencing of 12,940 individuals, 6,504 with T2D and 6,436 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. Sequencing was performed at the Broad Institute using the Agilent v2 capture reagent on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study does not have a sub study, as it is not consented to be deposited in dbGAP. Table 1. T2D-GENES Whole Exome Sequencing Studies Ancestry Study Countries of Origin # Cases # Controls African American Jackson Heart Study US 502 527 African American Wake Forest School of Medicine Study US 518 532 East Asian Korea Association Research Project Korea 526 561 East Asian Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 486 592 European Ashkenazi US, Israel 506 352 European Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European Malmö-Botnia Study Finland, Sweden 478 443 Hispanic San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 219 Hispanic Starr County, Texas US 749 704 South Asian London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 530 538 South Asian Singapore Indian Eye Study Singapore (Indian Asian) 563 585 The Ashkenazi study contributed 506 cases and 352 controls to T2D-GENES Project 1.

  Study phs001095

• Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Multi-Ethnic Samples (T2D-GENES) Project 1: Singapore

  T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Multi-Ethnic Samples) is a NIDDK-funded international research consortium which seeks to identify genetic variants for type 2 diabetes (T2D) through multiethnic sequencing studies. T2D-GENES Project 1 is a multi-ethnic sequencing study designed to assess whether less common variants play a role in T2D risk and to assess similarities and differences in the distribution of T2D risk variants across ancestry groups. The individuals were obtained from 14 cohorts that are listed in Table 1. The strategy was to perform deep exome sequencing of 12,940 individuals, 6,504 with T2D and 6,436 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. Sequencing was performed at the Broad Institute using the Agilent v2 capture reagent on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study does not have a sub study, as it is not consented to be deposited in dbGAP. Table 1. T2D-GENES Whole Exome Sequencing Studies Ancestry Study Countries of Origin # Cases # Controls African American Jackson Heart Study US 502 527 African American Wake Forest School of Medicine Study US 518 532 East Asian Korea Association Research Project Korea 526 561 East Asian Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 486 592 European Ashkenazi US, Israel 506 352 European Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European Malmö-Botnia Study Finland, Sweden 478 443 Hispanic San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 219 Hispanic Starr County, Texas US 749 704 South Asian London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 530 538 South Asian Singapore Indian Eye Study Singapore (Indian Asian) 563 585 The Singapore studies contributed 1049 cases and 1177 controls to T2D-GENES Project 1.

  Study phs001097

• Type 2 Diabetes Genetic Exploration by Next-generation Sequencing in Multi-Ethnic Samples (T2D-GENES) Project 1: London Life Sciences Population Study (LOLIPOP) UK South Asian

  T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Multi-Ethnic Samples) is a NIDDK-funded international research consortium which seeks to identify genetic variants for type 2 diabetes (T2D) through multiethnic sequencing studies. T2D-GENES Project 1 is a multi-ethnic sequencing study designed to assess whether less common variants play a role in T2D risk and to assess similarities and differences in the distribution of T2D risk variants across ancestry groups. The individuals were obtained from 14 cohorts that are listed in Table 1. The strategy was to perform deep exome sequencing of 12,940 individuals, 6,504 with T2D and 6,436 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. Sequencing was performed at the Broad Institute using the Agilent v2 capture reagent on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study does not have a sub study, as it is not consented to be deposited in dbGAP. Table 1. T2D-GENES Whole Exome Sequencing Studies Ancestry Study Countries of Origin # Cases # Controls African American Jackson Heart Study US 502 527 African American Wake Forest School of Medicine Study US 518 532 East Asian Korea Association Research Project Korea 526 561 East Asian Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 486 592 European Ashkenazi US, Israel 506 352 European Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European Malmö-Botnia Study Finland, Sweden 478 443 Hispanic San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 219 Hispanic Starr County, Texas US 749 704 South Asian London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 530 538 South Asian Singapore Indian Eye Study Singapore (Indian Asian) 563 585 The London Life Sciences Population Study (LOLIPOP) contributed 530 cases and 538 controls to T2D-GENES Project 1.

  Study phs001093

• Defining T cell States Associated with Response to Checkpoint Immunotherapy in Melanoma

  Checkpoint blockade therapy using antibodies targeting CTLA4, PD1 or PDL1 have changed the way cancer patients with advanced disease are being treated, as evident by their FDA approval in a wide variety of malignancies, and their ability to induce high response rates when compared to conventional therapies (e.g. chemotherapy). However, even in melanoma, despite the high response rate, most patients are refractory to therapy or acquire resistance in 10-12 months. To identify key immunological elements coupled with response or resistance to checkpoint immunotherapy, we performed an unbiased analysis on 16,291 CD45+ immune cells from 32 patients (48 samples) treated with checkpoint therapy (anti-PD1=37; anti-PD1/CTLA4=11), using single cell transcriptomics. Initial unsupervised clustering of all CD45+ cells identified 11 clusters. When examining the association of these clusters with clinical outcome, we found that two clusters (B-cells, p=0.005; memory T-cells, p=0.03) were significantly enriched in responder lesions while 4 clusters (monocytes/macrophages, p=0.003; dendritic cells, p=0.015; exhausted CD8+ T-cells, p=0.005; and exhausted/cell-cycle lymphocytes, 1.3x10-5) are enriched in non-responder lesions. Next, by leveraging our unbiased single cell approach, we identified not only clusters but also specific markers associated with either responder lesions (PLAC8, LTB, LY9, SELL, TCF7, IGKC and CCR7) or non-responder ones (CCL3, CD38, HAVCR2, ENTPD1 and WARS), many of which were not previously reported to be associated with clinical outcome to checkpoint therapy. Due to the importance of CD8+ T-cells in controlling tumors, the significant association of T-cell states with clinical outcome, and their high abundance in melanoma tumors, we next focused our analysis on CD8+ T-cells and identified 2 main clusters CD8_G (with increased expression of genes linked to memory) and CD8_B (enriched for genes linked to cell dysfunction), that were significantly enriched in responder (CD8_G, p=1.4x10-6) and non-responder (CD8_B, p=0.005) lesions, respectively. Since cells with both states coexist in each of the responder and non-responder lesions, we decided to calculate the ratio between the number of cells in these 2 clusters and observed a significant separation between responders (CD8_G/CD8_B>1) and non-responders (CD8_G/CD8_B<1) when looking at all samples, baseline or post samples separately. Similar results were detected when looking at the expression of a single transcription factor, TCF7, in CD8 T-cells in an independent cohort (n=43) using a simple immunofluorescence assay. Additionally, we validated the identity and function of some of the newly identified cell states as well as their epigenetic landscape, and found that cells expressing the inhibitory molecules CD39 and TIM3, on top of being enriched in non-responder lesions, are likely to play a crucial role in T-cell exhaustion in melanoma. Collectively, our study provides extensive unbiased data in human tumors for discovery of predictors and therapeutic targets to checkpoint immunotherapy.

  Study phs001680

• Immunophenotyping in a COVID-19 Cohort (IMPACC) Transcriptomics and Genotyping Assays

  IMPACC (IMmunoPhenotyping Assessment in a COVID-19 Cohort) is a prospective longitudinal cohort study (NCT04378777) launched in May 2020 in response to the worldwide pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was funded by the National Institute of Allergy and Infectious Diseases (NIAID) of the National Institute of Health (NIH). Patient enrollment and sample analysis involved 20 hospitals associated with 15 U.S. biomedical research centers and 11 centralized Core immunoassay laboratories.The study aimed to enable the identification of potential biomarkers and inform future therapeutic interventions using a longitudinal approach encompassing the whole disease course for a comprehensive understanding of the contributions of the pathogen and host immune response in modulating the manifestations, severity, and post-acute sequelae of the infection. IMPACC enrolled close to 1200 adult symptomatic, molecularly confirmed hospitalized COVID-19 patients in the study within 48 hours of their hospital admission. Clinical data and biological samples (blood, mid-turbinate nasal swabs, and esophageal aspirant) were collected according to minimal risk guidelines at enrollment and on days 4, 7, 14, 21, and 28. Biological samples were collected at 3-month intervals up to 12 months after hospital discharge to assess functional and immunologic recovery measures. Sample collection, processing, and storage procedures were standardized across sites, and samples were transported to centralized assay-specific core laboratories (Core Labs) in batches for testing and analysis. The description of outcome severity was not limited to survival versus death but encompassed a clinical trajectory approach using 5 groups leveraging longitudinal data based on time in the hospital, disease severity by ordinal scale based on the degree of respiratory illness, and presence or absence of limitations at discharge. Disease severity was assessed based on the degree of respiratory illness using a 7-point ordinal scale adapted from the World Health Organization COVID-19 and NIAID disease ordinal severity scales. DNA for genetic analyses was collected from whole blood at a single time point. To identify any genomic determinants of severe COVID-19 disease, single-nucleotide polymorphism (SNP) genotyping and exome sequencing were conducted to assess genetic variants associated with individual susceptibility to severe disease. In addition, IMPACC included bulk transcriptomic analysis of upper airway (nasal swaps) samples, lower airway (esophageal aspirant) samples, and peripheral blood mononuclear cells (PBMCs) to evaluate dynamic changes in cellular composition and cellular response during the course of the disease. Reads were aligned to the GRCh38 human reference genome. Additional information for the IMPACC study has been submitted to The Immunology Database and Analysis Portal (ImmPort) under study accession SDY1760. Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Dr. Steven Kleinstein (steven.kleinstein@yale.edu).

  Study phs002686

• OA_hip-G-1

  Genotyping of hip osteoarthritis patients who have undergone total joint replacement

  Dataset EGAD00010001292

• HGP-clean

  Himalayan population genetic study QC filtered data

  Dataset EGAD00010001471

• stilts-G-1

  Human Core Exome Genotyping for 1471 samples from the STudy Into Lean and Thin Subjects (STILTS) cohort

  Dataset EGAD00010001622

• Single cell RNA-Seq of Clear Cell Sarcoma of the Kidney

  Dataset EGAD00001007498

• Targeted capture ctDNA Library CRCQV42Run018-18

  Targeted capture ctDNA Library CRCQV42Run018-18 from patient PBC0001859, TNBC FFPE sample

  Dataset EGAD00001010417

• Targeted capture ctDNA Library CRCQV42Run021-7

  Targeted capture ctDNA Library CRCQV42Run021-7 from patient PBC0001299, TNBC FFPE sample

  Dataset EGAD00001010448

• Reference epigenome SMC01_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC01_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003845