A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS
The recent explosion of genetic and high dimensional biobank and 'omic' data has provided researchers with the opportunity to investigate the shared genetic origin (pleiotropy) of hundreds to thousands of related phenotypes. However, existing methods for multi-phenotype genome-wide associa...
Saved in:
Published in | arXiv.org |
---|---|
Main Authors | , , , |
Format | Paper |
Language | English |
Published |
Ithaca
Cornell University Library, arXiv.org
17.03.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | The recent explosion of genetic and high dimensional biobank and 'omic' data has provided researchers with the opportunity to investigate the shared genetic origin (pleiotropy) of hundreds to thousands of related phenotypes. However, existing methods for multi-phenotype genome-wide association studies (GWAS) do not model pleiotropy, are only applicable to a small number of phenotypes, or provide no way to perform inference. To add further complication, raw genetic and phenotype data are rarely observed, meaning analyses must be performed on GWAS summary statistics whose statistical properties in high dimensions are poorly understood. We therefore developed a novel model, theoretical framework, and set of methods to perform Bayesian inference in GWAS of high dimensional phenotypes using summary statistics that explicitly model pleiotropy, beget fast computation, and facilitate the use of biologically informed priors. We demonstrate the utility of our procedure by applying it to metabolite GWAS, where we develop new nonparametric priors for genetic effects on metabolite levels that use known metabolic pathway information and foster interpretable inference at the pathway level. |
---|---|
AbstractList | The recent explosion of genetic and high dimensional biobank and 'omic' data has provided researchers with the opportunity to investigate the shared genetic origin (pleiotropy) of hundreds to thousands of related phenotypes. However, existing methods for multi-phenotype genome-wide association studies (GWAS) do not model pleiotropy, are only applicable to a small number of phenotypes, or provide no way to perform inference. To add further complication, raw genetic and phenotype data are rarely observed, meaning analyses must be performed on GWAS summary statistics whose statistical properties in high dimensions are poorly understood. We therefore developed a novel model, theoretical framework, and set of methods to perform Bayesian inference in GWAS of high dimensional phenotypes using summary statistics that explicitly model pleiotropy, beget fast computation, and facilitate the use of biologically informed priors. We demonstrate the utility of our procedure by applying it to metabolite GWAS, where we develop new nonparametric priors for genetic effects on metabolite levels that use known metabolic pathway information and foster interpretable inference at the pathway level. |
Author | Cape, Joshua Huang, Weiqiong McKennan, Chris Hector, Emily C |
Author_xml | – sequence: 1 givenname: Weiqiong surname: Huang fullname: Huang, Weiqiong – sequence: 2 givenname: Emily surname: Hector middlename: C fullname: Hector, Emily C – sequence: 3 givenname: Joshua surname: Cape fullname: Cape, Joshua – sequence: 4 givenname: Chris surname: McKennan fullname: McKennan, Chris |
BookMark | eNqNjkFrwkAQhZdioVrzHwZ6rZBuNMajlFrvCj2GbTsxa7M7684E8eB_7yKCV0_v8r3vvZEaePL4oIa6KN4m1VTrJ5Ux7_M81-Vcz2bFUJ2XwGLEstgf00ETjcMjxT9oKMLn13ID1EBrdy38WoeeLfmEhRY9ySkgQ8_W74B750w83Vz8CkcrLZgQumSW1AMhcCjmmzoreJGP1WNjOsbsms_qZfWxfV9PQqRDjyz1nvqYFrnW82qRfudlVdxH_QMTW1HO |
ContentType | Paper |
Copyright | 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PIMPY PQEST PQQKQ PQUKI PRINS PTHSS |
DatabaseName | ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central SciTech Premium Collection ProQuest Engineering Collection Engineering Database Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection |
DatabaseTitle | Publicly Available Content Database Engineering Database Technology Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest One Academic Engineering Collection |
DatabaseTitleList | Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Physics |
EISSN | 2331-8422 |
Genre | Working Paper/Pre-Print |
GroupedDBID | 8FE 8FG ABJCF ABUWG AFKRA ALMA_UNASSIGNED_HOLDINGS AZQEC BENPR BGLVJ CCPQU DWQXO FRJ HCIFZ L6V M7S M~E PIMPY PQEST PQQKQ PQUKI PRINS PTHSS |
ID | FETCH-proquest_journals_27890020683 |
IEDL.DBID | BENPR |
IngestDate | Tue Sep 24 19:41:03 EDT 2024 |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | false |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-proquest_journals_27890020683 |
OpenAccessLink | https://www.proquest.com/docview/2789002068/abstract/?pq-origsite=%requestingapplication% |
PQID | 2789002068 |
PQPubID | 2050157 |
ParticipantIDs | proquest_journals_2789002068 |
PublicationCentury | 2000 |
PublicationDate | 20230317 |
PublicationDateYYYYMMDD | 2023-03-17 |
PublicationDate_xml | – month: 03 year: 2023 text: 20230317 day: 17 |
PublicationDecade | 2020 |
PublicationPlace | Ithaca |
PublicationPlace_xml | – name: Ithaca |
PublicationTitle | arXiv.org |
PublicationYear | 2023 |
Publisher | Cornell University Library, arXiv.org |
Publisher_xml | – name: Cornell University Library, arXiv.org |
SSID | ssj0002672553 |
Score | 3.4465759 |
SecondaryResourceType | preprint |
Snippet | The recent explosion of genetic and high dimensional biobank and 'omic' data has provided researchers with the opportunity to investigate the shared genetic... |
SourceID | proquest |
SourceType | Aggregation Database |
SubjectTerms | Bayesian analysis Metabolites Statistical inference |
Title | A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS |
URI | https://www.proquest.com/docview/2789002068/abstract/ |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LS8NAEB7aBsGbT3zUMqBHQ9oku4knqZI2CC3FB_ZWso940aQ28ai_3Z2YWEHocbMwhN1lZvjm-2YALszX0EuYsjVzJZUZ-_ZVmGiCOfqJpxj3JOGQkymPn_y7OZu3IG60MESrbHxi5ahVLgkjdyrFpslteOgkglAAWTrXy3eb5kdRnbUeptEGyx34VLC1bqLp7P4Xb3F5YLJn75_LreLIaAesWbLUq11o6WwPtir6pSz24XOIpOypmiYnr5g2jCk0KSWOn4cPmKdInYVRUTf-n04aSPSsnDDUAom-_oK1Em1tq7hEAlrxT5kayxzfdGnuntTHlfEDOB9Fj7ex3fzzon5hxWJ9Ht4hdLI800eAgvtcyICZmCN9GZprUKlwlatF4KaM8WPobrJ0snn7FLZp2DoxsAZBFzrl6kOfmZBcih60w9G4V5-5WU2-om8lYpnQ |
link.rule.ids | 786,790,12792,21416,33408,33779,43635,43840 |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1NS8NAEB20RfTmJ35UHdCjwZrNbuJJiphGbYtgxd5CsrvpRZPaxKP_3Z01sYLQaxaGZTbMDG_emwE4N18DlnDlaO5KajN2nesg0QRzdBOmuGCScMjhSEQv3sOET2rAraxplU1MtIFaFZIw8kur2DS1jQhuZh8ObY2i7mq9QmMV2h4zqZOU4mH_F2NxhW8qZvYvzNrcEW5C-ymZ6fkWrOh8G9Ys5VKWO_DVQ1Lz2EHJyRtmDUsKTRmJ_dfeMxYZ0jRhVDSB_2d6BhIlqyDctESirE-xVp8tbJUXSOAq_mlNY1Xgu67Me5Pi2BrfhbPwbnwbOc2d4_qvKuOFD9getPIi1_uAqfBEKn1u8oz0ZGBcr7LUVa5OfTfjXBxAZ5mlw-XHp7AejYeDeHA_ejyCDVq2TgysK78DrWr-qY9NSq7SE-v3b5fAlYY |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+statistical+framework+for+GWAS+of+high+dimensional+phenotypes+using+summary+statistics%2C+with+application+to+metabolite+GWAS&rft.jtitle=arXiv.org&rft.au=Huang%2C+Weiqiong&rft.au=Hector%2C+Emily+C&rft.au=Cape%2C+Joshua&rft.au=McKennan%2C+Chris&rft.date=2023-03-17&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422 |