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Pregled bibliografske jedinice broj: 1239008

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

(23andMe Research Team ; Social Science Genetic Association Consortium) Okbay, Aysu; Wu, Yeda; Wang, Nancy; Jayashankar, Hariharan; Bennett, Michael; Nehzati, Seyed Moeen; ...; Kolčić, Ivana; ...; Polašek, Ozren; [et al.]
Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals // Nature genetics, 54 (2022), 4; 437-449 doi:10.1038/s41588-022-01016-z (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1239008 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Autori
Okbay, Aysu ; Wu, Yeda ; Wang, Nancy ; Jayashankar, Hariharan ; Bennett, Michael ; Nehzati, Seyed Moeen ; ... ; Kolčić, Ivana ; ... ; Polašek, Ozren ; [et al.]

Kolaboracija
23andMe Research Team ; Social Science Genetic Association Consortium

Izvornik
Nature genetics (1061-4036) 54 (2022), 4; 437-449

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
HUMAN COMPLEX TRAITS ; COHORT PROFILE ; BIOBANK ; GENETICS ; MODELS ; HEALTH ; LOCI ; GWAS

Sažetak
We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of similar to 3 million individuals and identify 3, 952 approximately uncorrelated genome-wide- significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

Izvorni jezik
Engleski

Znanstvena područja
Temeljne medicinske znanosti



POVEZANOST RADA

Ustanove:
Medicinski fakultet, Split

Profili:

Avatar Url Ivana Kolčić (autor)

Avatar Url Ozren Polašek (autor)

Poveznice na cjeloviti tekst rada:

doi www.nature.com

Citiraj ovu publikaciju:

(23andMe Research Team ; Social Science Genetic Association Consortium) Okbay, Aysu; Wu, Yeda; Wang, Nancy; Jayashankar, Hariharan; Bennett, Michael; Nehzati, Seyed Moeen; ...; Kolčić, Ivana; ...; Polašek, Ozren; [et al.]
Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals // Nature genetics, 54 (2022), 4; 437-449 doi:10.1038/s41588-022-01016-z (međunarodna recenzija, članak, znanstveni)
(23andMe Research Team ; Social Science Genetic Association Consortium) (23andMe Research Team, Social Science Genetic Association Consortium) Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S., ..., Kolčić, I., ..., Polašek, O. & [et al.] (2022) Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature genetics, 54 (4), 437-449 doi:10.1038/s41588-022-01016-z.
@article{article, author = {Okbay, Aysu and Wu, Yeda and Wang, Nancy and Jayashankar, Hariharan and Bennett, Michael and Nehzati, Seyed Moeen and Kol\v{c}i\'{c}, Ivana and Pola\v{s}ek, Ozren}, year = {2022}, pages = {437-449}, DOI = {10.1038/s41588-022-01016-z}, keywords = {HUMAN COMPLEX TRAITS, COHORT PROFILE, BIOBANK, GENETICS, MODELS, HEALTH, LOCI, GWAS}, journal = {Nature genetics}, doi = {10.1038/s41588-022-01016-z}, volume = {54}, number = {4}, issn = {1061-4036}, title = {Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals}, keyword = {HUMAN COMPLEX TRAITS, COHORT PROFILE, BIOBANK, GENETICS, MODELS, HEALTH, LOCI, GWAS} }
@article{article, author = {Okbay, Aysu and Wu, Yeda and Wang, Nancy and Jayashankar, Hariharan and Bennett, Michael and Nehzati, Seyed Moeen and Kol\v{c}i\'{c}, Ivana and Pola\v{s}ek, Ozren}, year = {2022}, pages = {437-449}, DOI = {10.1038/s41588-022-01016-z}, keywords = {HUMAN COMPLEX TRAITS, COHORT PROFILE, BIOBANK, GENETICS, MODELS, HEALTH, LOCI, GWAS}, journal = {Nature genetics}, doi = {10.1038/s41588-022-01016-z}, volume = {54}, number = {4}, issn = {1061-4036}, title = {Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals}, keyword = {HUMAN COMPLEX TRAITS, COHORT PROFILE, BIOBANK, GENETICS, MODELS, HEALTH, LOCI, GWAS} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE
  • Nature Index

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