TY - JOUR
T1 - Correlations between complex human phenotypes vary by genetic background, gender, and environment
AU - The Trans-Omics for Precision Medicine (TOPMed) Consortium
AU - Elgart, Michael
AU - Goodman, Matthew O.
AU - Isasi, Carmen
AU - Chen, Han
AU - Morrison, Alanna C.
AU - de Vries, Paul S.
AU - Xu, Huichun
AU - Manichaikul, Ani W.
AU - Guo, Xiuqing
AU - Franceschini, Nora
AU - Psaty, Bruce M.
AU - Rich, Stephen S.
AU - Rotter, Jerome I.
AU - Lloyd-Jones, Donald M.
AU - Fornage, Myriam
AU - Correa, Adolfo
AU - Heard-Costa, Nancy L.
AU - Vasan, Ramachandran S.
AU - Hernandez, Ryan
AU - Kaplan, Robert C.
AU - Redline, Susan
AU - Sofer, Tamar
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2022/12/20
Y1 - 2022/12/20
N2 - We develop a closed-form Haseman-Elston estimator for genetic and environmental correlation coefficients between complex phenotypes, which we term HEc, that is as precise as GCTA yet ∼20× faster. We estimate genetic and environmental correlations between over 7,000 phenotype pairs in subgroups from the Trans-Omics in Precision Medicine (TOPMed) program. We demonstrate substantial differences in both heritabilities and genetic correlations for multiple phenotypes and phenotype pairs between individuals of self-reported Black, Hispanic/Latino, and White backgrounds. We similarly observe differences in many of the genetic and environmental correlations between genders. To estimate the contribution of genetics to the observed phenotypic correlation, we introduce “fractional genetic correlation” as the fraction of phenotypic correlation explained by genetics. Finally, we quantify the enrichment of correlations between phenotypic domains, each of which is comprised of multiple phenotypes. Altogether, we demonstrate that the observed correlations between complex human phenotypes depend on the genetic background of the individuals, their gender, and their environment.
AB - We develop a closed-form Haseman-Elston estimator for genetic and environmental correlation coefficients between complex phenotypes, which we term HEc, that is as precise as GCTA yet ∼20× faster. We estimate genetic and environmental correlations between over 7,000 phenotype pairs in subgroups from the Trans-Omics in Precision Medicine (TOPMed) program. We demonstrate substantial differences in both heritabilities and genetic correlations for multiple phenotypes and phenotype pairs between individuals of self-reported Black, Hispanic/Latino, and White backgrounds. We similarly observe differences in many of the genetic and environmental correlations between genders. To estimate the contribution of genetics to the observed phenotypic correlation, we introduce “fractional genetic correlation” as the fraction of phenotypic correlation explained by genetics. Finally, we quantify the enrichment of correlations between phenotypic domains, each of which is comprised of multiple phenotypes. Altogether, we demonstrate that the observed correlations between complex human phenotypes depend on the genetic background of the individuals, their gender, and their environment.
KW - Haseman-Elston regression
KW - Hispanic Community Health Study/Study of Latinos
KW - Trans-Omics in Precision Medicine
KW - admixed population
KW - genetic architecture
KW - genetic background
KW - genetic correlation
KW - heritability
KW - household correlation
KW - multi-ethnic
UR - http://www.scopus.com/inward/record.url?scp=85144595189&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85144595189&partnerID=8YFLogxK
U2 - 10.1016/j.xcrm.2022.100844
DO - 10.1016/j.xcrm.2022.100844
M3 - Article
C2 - 36513073
AN - SCOPUS:85144595189
SN - 2666-3791
VL - 3
JO - Cell Reports Medicine
JF - Cell Reports Medicine
IS - 12
M1 - 100844
ER -