General Framework for Meta-Analysis of Haplotype Association Tests

  1. Wang, Shuai
  2. Zhao, Jing Hua
  3. An, Ping
  4. Guo, Xiuqing
  5. Jensen, Richard A.
  6. Marten, Jonathan
  7. Huffman, Jennifer E.
  8. Boeing, Heiner ORCID logo
  9. Campbell, Archie
  10. Rice, Kenneth M.
  11. Scott, Robert A.
  12. Yao, Jie
  13. Wareham, Nicholas J.
  14. Borecki, Ingrid B.
  15. Province, Michael A.
  16. Rotter, Jerome I.
  17. Hayward, Caroline
  18. Goodarzi, Mark O.
  19. Meigs, James B.
  20. Dupuis, Josée
  21. Meidtner, Karina ORCID logo
  22. Schulze, Matthias B. ORCID logo

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WeightNameValue
1000 Titel
  • General Framework for Meta-Analysis of Haplotype Association Tests
1000 Autor/in
  1. Wang, Shuai |
  2. Zhao, Jing Hua |
  3. An, Ping |
  4. Guo, Xiuqing |
  5. Jensen, Richard A. |
  6. Marten, Jonathan |
  7. Huffman, Jennifer E. |
  8. Boeing, Heiner |
  9. Campbell, Archie |
  10. Rice, Kenneth M. |
  11. Scott, Robert A. |
  12. Yao, Jie |
  13. Wareham, Nicholas J. |
  14. Borecki, Ingrid B. |
  15. Province, Michael A. |
  16. Rotter, Jerome I. |
  17. Hayward, Caroline |
  18. Goodarzi, Mark O. |
  19. Meigs, James B. |
  20. Dupuis, Josée |
  21. Meidtner, Karina |
  22. Schulze, Matthias B. |
1000 Erscheinungsjahr 2016
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2016-03-08
1000 Erschienen in
1000 Quellenangabe
  • 40(3): 244-252
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2016
1000 Lizenz
1000 Verlagsversion
  • http://doi.org/10.1002/gepi.21959 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4869684/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • For complex traits, most associated single nucleotide variants (SNV) discovered to date have a small effect, and detection of association is only possible with large sample sizes. Because of patient confidentiality concerns, it is often not possible to pool genetic data from multiple cohorts, and meta-analysis has emerged as the method of choice to combine results from multiple studies. Many meta-analysis methods are available for single SNV analyses. As new approaches allow the capture of low frequency and rare genetic variation, it is of interest to jointly consider multiple variants to improve power. However, for the analysis of haplotypes formed by multiple SNVs, meta-analysis remains a challenge, because different haplotypes may be observed across studies. We propose a two-stage meta-analysis approach to combine haplotype analysis results. In the first stage, each cohort estimate haplotype effect sizes in a regression framework, accounting for relatedness among observations if appropriate. For the second stage, we use a multivariate generalized least square meta-analysis approach to combine haplotype effect estimates from multiple cohorts. Haplotype-specific association tests and a global test of independence between haplotypes and traits are obtained within our framework. We demonstrate through simulation studies that we control the type-I error rate, and our approach is more powerful than inverse variance weighted meta-analysis of single SNV analysis when haplotype effects are present. We replicate a published haplotype association between fasting glucose-associated locus (G6PC2) and fasting glucose in seven studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and we provide more precise haplotype effect estimates.
1000 Sacherschließung
lokal linear mixed effects model
lokal family samples
lokal haplotype association tests
lokal meta-analysis
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
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1000 Label
1000 Förderer
  1. Chief Scientist Office |
  2. Intramural NIH HHS |
  3. Medical Research Council |
  4. NCATS NIH HHS |
  5. NCRR NIH HHS |
  6. NHLBI NIH HHS |
  7. NIA NIH HHS |
  8. NIDDK NIH HHS |
  9. PHS HHS |
  10. Wellcome Trust |
1000 Fördernummer
  1. CZD/16/6; CZD/16/6/4
  2. -
  3. MC_U106179471; MC_PC_U127561128
  4. UL1 TR000124; UL1 TR001079; UL1-TR-000040; UL1-TR-001079; UL1TR000124; UL1 TR000040
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  7. AG023629; R56 AG023629; R01 AG023629; U01 AG023746
  8. K24 DK080140; P30 DK063491; R01 DK075681; R01-DK-075681; U01 DK078616; DK063491; R01-DK-8925601; R01 DK078616; R01 DK089256
  9. HHSN268200800007C; HHSN268201200036C
  10. -
1000 Förderprogramm
  1. -
  2. -
  3. -
  4. -
  5. -
  6. -
  7. -
  8. -
  9. -
  10. -
1000 Dateien
  1. General Framework for Meta-Analysis of Haplotype Association Tests
    General Framework for Meta-Analysis of Haplotype Association Tests
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Chief Scientist Office |
    1000 Förderprogramm -
    1000 Fördernummer CZD/16/6; CZD/16/6/4
  2. 1000 joinedFunding-child
    1000 Förderer Intramural NIH HHS |
    1000 Förderprogramm -
    1000 Fördernummer -
  3. 1000 joinedFunding-child
    1000 Förderer Medical Research Council |
    1000 Förderprogramm -
    1000 Fördernummer MC_U106179471; MC_PC_U127561128
  4. 1000 joinedFunding-child
    1000 Förderer NCATS NIH HHS |
    1000 Förderprogramm -
    1000 Fördernummer UL1 TR000124; UL1 TR001079; UL1-TR-000040; UL1-TR-001079; UL1TR000124; UL1 TR000040
  5. 1000 joinedFunding-child
    1000 Förderer NCRR NIH HHS |
    1000 Förderprogramm -
    1000 Fördernummer UL1 RR025005; UL1-RR-025005
  6. 1000 joinedFunding-child
    1000 Förderer NHLBI NIH HHS |
    1000 Förderprogramm -
    1000 Fördernummer R01-HL-071250; N01HC95163; N01-HC-95159; R01 HL071259; U01 HL080295; N01-HC-95160; N01HC95160; N01HC95166; N01-HC-95165; R01 HL071250; R01 HL068986; N01-HC-95161; R01-HL-071205; R01-HL-071259; N01HC95159; N01-HC-95163; R01-HL-071258; N01-HC-95169; N01HC95164; R01-HL-071051; N01-HC-95162; R01 HL080295; N01HC95169; R01-HL-088215; R01 HL087700; N01HC85081; N01HC95161; R01-HL-071252; HL068986; HHSN268200800007C; R01-HL-071251; N01-HC-95167; N01HC85080; N01HC95162; N01HC85083; N01HC95167; N01-HC-95166; N01HC85082; N02HL64278; HL080295; N01HC85079; R01-HL-087700; R01 HL071258; N01HC95165; R01 HL087652; N01-HC-95168; R01 HL071205; R01 HL071252; HHSN268201200036C; N01-HC-95164; N01HC85086; N01HC95168; N01HC55222; HL103612; R01 HL103612; HL087652; R01 HL088215
  7. 1000 joinedFunding-child
    1000 Förderer NIA NIH HHS |
    1000 Förderprogramm -
    1000 Fördernummer AG023629; R56 AG023629; R01 AG023629; U01 AG023746
  8. 1000 joinedFunding-child
    1000 Förderer NIDDK NIH HHS |
    1000 Förderprogramm -
    1000 Fördernummer K24 DK080140; P30 DK063491; R01 DK075681; R01-DK-075681; U01 DK078616; DK063491; R01-DK-8925601; R01 DK078616; R01 DK089256
  9. 1000 joinedFunding-child
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    1000 Förderprogramm -
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  10. 1000 joinedFunding-child
    1000 Förderer Wellcome Trust |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
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1000 Erstellt am 2017-06-26T13:52:48.843+0200
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