5 research outputs found
Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility.
- Author
- Bartels M
- Boezen HM
- Bolze A
- Boulogne F
- Campbell A
- Cirulli ET
- Claringbould A
- COVID-19 Host Genetics Initiative
- de Geus EJC
- Deelen P
- Fawns-Ritchie C
- Franke LH
- Ganna A
- Hayward C
- Herkert JC
- Hottenga J-J
- Karjalainen J
- Lanting P
- Lifelines COVID-19 cohort study
- Lim YW
- Lopera-Maya EA
- Lu JT
- Ori APS
- Porteous D
- Ramirez JM
- Riffle S
- Schiabor Barrett KM
- Tanudjaja F
- van Blokland IV
- Vonk JM
- Wang X
- Warmerdam RCA
- Washington NL
- White S
- Publication venue
- Publication date
- 01/01/2021
- Field of study
Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak
Global Biobank Meta-analysis Initiative: powering genetic discovery across human disease
- Author
- Bhattacharya A
- Brumpton BM
- Chapman SB
- Chavan S
- Chen TT
- Chen Z
- Daya M
- Ding Y
- Favé MJ
- Feng YCA
- Gignoux CR
- Graham SE
- Guare LA
- Hirbo JB
- Hornsby WE
- Ingold N
- Ismail SI
- Johnson R
- Kanai M
- Karjalainen J
- Kurki M
- Laisk T
- Lin K
- Lindgren CM
- Lo Faro V
- Lopera-Maya EA
- Lv J
- Läll K
- Maasha M
- Millwood IY
- Moreno-Grau S
- Nam K
- Namba S
- Palta P
- Pandit A
- Partanen JJ
- Preuss MH
- Rasheed H
- Saad C
- Setia-Verma S
- Surakka I
- Thorsteinsdottir U
- Tsuo K
- Uzunovic J
- Verma A
- Walters RG
- Wang Y
- Wolford BN
- Wu KHH
- Zawistowski M
- Zhao H
- Zhong X
- Zhou W
- Publication venue
- Cell Press
- Publication date
- 01/01/2022
- Field of study
Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits
Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease
- Author
- Afifi N
- Al Thani A
- Al-Dabhani KM
- Barnes KC
- Bhattacharya A
- Boezen M
- Bradford Y
- Brumpton BM
- Campbell A
- Chapman SB
- Chavan S
- Chen CY
- Chen TT
- Chen Z
- Cho J
- Crooks K
- Damrauer SM
- Daya M
- de Bock GH
- Ding Y
- Douville NJ
- Favé MJ
- Feng YCA
- Finer S
- Finucane HK
- Franke L
- Fritsche LG
- Fthenou E
- Gamazon ER
- Ganna A
- Gaunt TR
- Ge T
- Gignoux CR
- Gonzalez-Arroyo G
- Graham SE
- Griffiths CJ
- Guare LA
- Guo Y
- Hirbo JB
- Hornsby WE
- Huang H
- Huffman J
- Hunt KA
- Ingold N
- Ioannidis A
- Ismail SI
- Jansonius NM
- Johnson R
- Kanai M
- Karjalainen J
- Konuma T
- Kurki M
- Laisk T
- Lee MTM
- Lin K
- Lo Faro V
- Lopera-Maya EA
- Lopez-Pineda A
- Lv J
- Läll K
- Maasha M
- Marioni RE
- Matsuda Y
- Millwood IY
- Moatamed B
- Moreno-Grau S
- Nam K
- Namba S
- Nava-Aguilar MA
- Numakura K
- Palta P
- Pandit A
- Partanen JJ
- Patil S
- Preuss MH
- Rafaels N
- Rasheed H
- Richmond A
- Rojas-Muñoz A
- Saad C
- Setia-Verma S
- Shortt JA
- Smith GD
- Straub P
- Surakka I
- Tao R
- Thorsteinsdottir U
- Tsuo K
- Uzunovic J
- Vanderwerff B
- Verma A
- Vernekar M
- Veturi Y
- Wang Y
- Wolford BN
- Wu KHH
- Zawistowski M
- Zhao H
- Zhong X
- Zhou W
- Publication venue
- 'Elsevier BV'
- Publication date
- 12/10/2022
- Field of study
Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits
Meta-analysis fine-mapping is often miscalibrated at single-variant resolution
- Author
- Afifi N
- Al-Dabhani KM
- Awadalla P
- Barnes KC
- Bhattacharya A
- Biobank
- Biobank of Korea
- Biobank of the Americas
- Biobank FE
- Biobank MGB
- BioBank PM
- Biobank Q
- Biobank T
- BioMe
- BioVU
- Boehnke M
- Boezen M
- Bradford Y
- Brumpton BM
- Bustamante CD
- Campbell A
- Chapman SB
- Chavan S
- Chen C-Y
- Chen T-T
- Chen Z
- Cho J
- Cox NJ
- Crooks K
- Daly MJ
- Daly MJ
- Damrauer SM
- Daya M
- de Bock GH
- Ding Y
- Douville NJ
- Elzur R
- Faro VL
- Fatumo S
- Favé M-J
- Feng Y-CA
- Finer S
- Finucane HK
- Finucane HK
- Franke L
- Fritsche LG
- Fthenou E
- Gamazon ER
- Ganna A
- Gaunt TR
- Ge T
- Genetics D
- Geschwind DH
- Gignoux CR
- Gonzalez-Arroyo G
- Graham SE
- Griffiths CJ
- Group CKBC
- Guare LA
- Guo Y
- Hayward C
- Hirbo JB
- Hornsby WE
- Huang H
- Huffman J
- Hunt KA
- Hveem K
- Ingold N
- Initiative GBM-A
- Initiative MG
- Initiative UACH
- Ioannidis A
- Ismail SI
- Jansonius NM
- Johnson R
- Kanai M
- Kanai M
- Karjalainen J
- Katsanis N
- Kenny EE
- Konuma T
- Koskela JT
- Kurki M
- Laisk T
- Lajonchere C
- Law MH
- Lee MTM
- Lee S
- Li L
- LifeLines
- Lin K
- Lin Y-F
- Lindgren CM
- Loos RJF
- Lopera-Maya EA
- Lopez-Pineda A
- Lv J
- Läll K
- Maasha M
- MacGregor S
- Marioni RE
- Martin AR
- Martin HC
- Matsuda K
- Matsuda Y
- Mbarek H
- Medicine CCFP
- Medland SE
- Millwood IY
- Moatamed B
- Moreno-Grau S
- Mägi R
- Nam K
- Namba S
- Nava-Aguilar MA
- Neale BM
- Numakura K
- Okada Y
- Olsen CM
- Palotie AV
- Palta P
- Pandit A
- Partanen JJ
- Pasaniuc B
- Patil S
- Porteous DJ
- Preuss MH
- Project BJ
- Rader DJ
- Rafaels N
- Rasheed H
- Resource UG
- Richmond A
- Ritchie MD
- Rojas-Muñoz A
- Saad C
- Sanna S
- Scotland G
- Setia-Verma S
- Shavit JA
- Shortt JA
- Smith GD
- Smoller JW
- Snieder H
- Stefansson K
- Straub P
- Study C-OH
- Study TH
- Study TQSAH
- Surakka I
- Takano T
- Tao R
- Team GHR
- Thani AA
- Thorsteinsdottir U
- Trembath RC
- Tsuo K
- Uzunovic J
- van Heel DA
- Vanderwerff B
- Verma A
- Vernekar M
- Veturi Y
- Vonk JM
- Walters RG
- Wang Y
- Whiteman DC
- Wicks SJ
- Wijmenga C
- Willer CJ
- Wolford BN
- Wright J
- Wu K-HH
- Zawistowski M
- Zhao H
- Zheng J
- Zhong X
- Zhou W
- Zhou W
- Zhou X
- Zöllner S
- Publication venue
- 'Elsevier BV'
- Publication date
- 01/11/2022
- Field of study
Mapping the human genetic architecture of COVID-19
- Author
- Abdelrazik M
- Abdullah T
- Abe R
- Abe S
- Abecasis GR
- Abel L
- Abernathy C
- Abraheem A
- Abul-Husn NS
- Acosta-Herrera M
- Acquilini D
- Acquilini D
- Adachi T
- Adachi Y
- Adams C
- Adams K
- Adanini O
- Adeleye O
- Adeniji K
- Adra D
- Afifi N
- Afilalo J
- Afilalo M
- Afolabi D
- Afrasiabi Z
- Afset JE
- Agasou A
- Aghemo A
- Agranoff D
- Agrawal S
- Aguirre LA
- Agwuh K
- Ahmad HF
- Ahmad N
- Ahmed A
- Ahmed C
- Ahmed KA
- Ai M
- Ail D
- Akeroyd L
- Akhtar MN
- Akhtar S
- Akinkugbe O
- Aksentijevich A
- Al Thani A
- Al-Muftah W
- Al-Sarraj Y
- Alarcon C
- Alarcon-Riquelme ME
- Alasdair F
- Alaverdian D
- Alavere H
- Albertos R
- Albillos A
- Albrecht W
- Albrich W
- Albrich WC
- Aldera EL
- Aldridge J
- Alegria A
- Alex B
- Alexander P
- Alfonso J
- Algera AG
- Ali A
- Ali IMA
- Ali S
- Aliberti S
- Allan A
- Allan E
- Allan J
- Alldis Z
- Allen L
- Allen S
- Allibone S
- Allison KS
- Allos R
- Ally SM
- Almaden-Boyle C
- Altabaibeh A
- Altmueller J
- Alvaro A
- Amar D
- Amin V
- Amitrano S
- Amiya S
- Ampuero J
- Anan R
- Anania S
- Anastasescu E
- Anderson F
- Anderson J
- Anderson M
- Anderson P
- Anderson S
- Anderson S
- Anderson T
- Ando A
- Andolfo I
- Andrade V
- Andretta F
- Andreucci E
- Andreucci E
- Andrew A
- Andrew B
- Andrew G
- Andrews E
- Andrews SJ
- Andrews SJ
- Andrews SJ
- Andrews SJ
- Andrikopoulos P
- Anedda F
- Aneli S
- Anemoli V
- Angelini C
- Angus B
- Annis A
- Antcliffe D
- Antinori A
- Antoni MD
- Anumakonda V
- Anwar M
- Anzai T
- Apetri E
- Arai D
- Arana E
- Arbane G
- Archer K
- Arciero E
- Arden T
- Arias SS
- Arif S
- Arimura K
- Armstrong J
- Armstrong JA
- Armstrong L
- Armstrong R
- Arning N
- Arnold S
- Arora J
- Artuso R
- Arumugam P
- Asakura T
- Asano K
- Aschard H
- Ascolillo S
- Ashish A
- Ashley E
- Ashraf S
- Ashraf S
- Ashton L
- Ashworth M
- Asiimwe IG
- Aslibekyan S
- Asselta R
- Atanasovska B
- Atkinson D
- Atkinson EG
- Attwood B
- Aucella F
- Augustin M
- Auld D
- Auld F
- Austin K
- Austin P
- Auton A
- Auton A
- Avendano-Ortiz J
- Awano N
- Ayers A
- Azarzar S
- Aziz F
- Azuma M
- Azuure C
- Azzolini E
- Baba R
- Baba T
- Bach B
- Bacher P
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- Badalamenti S
- Badia JR
- Badji RM
- Bahmer T
- Baikady RR
- Baillie JK
- Baillie JK
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- Baines B
- Baines D
- Baines K
- Baird T
- Baird Y
- Bakshi S
- Bakthavatsalam D
- Balaconis MK
- Baldassarri M
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- Barclay WS
- Barda B
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- Barker J
- Barlow G
- Barlow SL
- Barnass S
- Barnes KC
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- Barron A
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- Bartels M
- Bartley S
- Baruah R
- Baselli G
- Bashyal A
- Basikolo C
- Bassetti M
- Bassford C
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- Clerc O
- Cloherty A
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- Clohisey S
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- Cockrell M
- Coetzee S
- Coghlan P
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- CRiPSI
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- Tateno H
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- Tavares S
- Tavecchia L
- Taylor A
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- Team AC
- Team GHR
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- Tedder RS
- Teles A
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- Tenesa A
- Tentorio P
- Terai H
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- Terron Arcos V
- Tetla D
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- Thanasi M
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- Uffelmann E
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- Waddy S
- Wagstaff V
- Waite AAC
- Wakabayashi A
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- Wyllie DH
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- Yoshihara T
- Yoshiya K
- Yoshiyama T
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- Younes SE
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- Zanelli G
- Zara F
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- Zborowski AC
- Zeberg H
- Zechner M
- Zechner M
- Zguro K
- Zhai R
- Zhang JE
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- Zhen J
- Zheng C
- Zheng T
- Zhou W
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- Zlatopolsky M
- Zoller H
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- Zongo O
- zu Bentrup FM
- Zucchi P
- Zwinderman AHK
- Zyndorf M
- Publication venue
- Publication date
- 01/01/2021
- Field of study
The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease