» Articles » PMID: 37268996

Modeling the Longitudinal Changes of Ancestry Diversity in the Million Veteran Program

Abstract

Background: The Million Veteran Program (MVP) participants represent 100 years of US history, including significant social and demographic changes over time. Our study assessed two aspects of the MVP: (i) longitudinal changes in population diversity and (ii) how these changes can be accounted for in genome-wide association studies (GWAS). To investigate these aspects, we divided MVP participants into five birth cohorts (N-range = 123,888 [born from 1943 to 1947] to 136,699 [born from 1948 to 1953]).

Results: Ancestry groups were defined by (i) HARE (harmonized ancestry and race/ethnicity) and (ii) a random-forest clustering approach using the 1000 Genomes Project and the Human Genome Diversity Project (1kGP + HGDP) reference panels (77 world populations representing six continental groups). In these groups, we performed GWASs of height, a trait potentially affected by population stratification. Birth cohorts demonstrate important trends in ancestry diversity over time. More recent HARE-assigned Europeans, Africans, and Hispanics had lower European ancestry proportions than older birth cohorts (0.010 < Cohen's d < 0.259, p < 7.80 × 10). Conversely, HARE-assigned East Asians showed an increase in European ancestry proportion over time. In GWAS of height using HARE assignments, genomic inflation due to population stratification was prevalent across all birth cohorts (linkage disequilibrium score regression intercept = 1.08 ± 0.042). The 1kGP + HGDP-based ancestry assignment significantly reduced the population stratification (mean intercept reduction = 0.045 ± 0.007, p < 0.05) confounding in the GWAS statistics.

Conclusions: This study provides a characterization of ancestry diversity of the MVP cohort over time and compares two strategies to infer genetically defined ancestry groups by assessing differences in controlling population stratification in genome-wide association studies.

Citing Articles

Genome-wide association study for metabolic syndrome reveals APOA5 single nucleotide polymorphisms with multilayered effects in Koreans.

Park Y, Moon S, Choi J, Kim J, Kim H, Son H Lipids Health Dis. 2024; 23(1):272.

PMID: 39198834 PMC: 11351254. DOI: 10.1186/s12944-024-02248-0.


The impact of assortative mating, participation bias and socioeconomic status on the polygenic risk of behavioural and psychiatric traits.

Cabrera-Mendoza B, Wendt F, Pathak G, Yengo L, Polimanti R Nat Hum Behav. 2024; 8(5):976-987.

PMID: 38366106 PMC: 11161911. DOI: 10.1038/s41562-024-01828-5.


Association between suicidal ideation and tandem repeats in .

Parikh K, Quintero Reis A, Wendt F Front Psychiatry. 2024; 14:1236540.

PMID: 38239902 PMC: 10794671. DOI: 10.3389/fpsyt.2023.1236540.


Declining autozygosity over time: An exploration in over 1 million individuals from three diverse cohorts.

Colbert S, Wendt F, Pathak G, Helmer D, Hauser E, Keller M Am J Hum Genet. 2023; 110(6):1008-1014.

PMID: 37178685 PMC: 10257001. DOI: 10.1016/j.ajhg.2023.04.007.


Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective.

Gao Y, Sharma T, Cui Y Annu Rev Biomed Data Sci. 2023; 6:153-171.

PMID: 37104653 PMC: 10529864. DOI: 10.1146/annurev-biodatasci-020722-020704.

References
1.
Atkinson E, Bloemendal A, Maihofer A, Nievergelt C, Daly M, Neale B . Reply to: On powerful GWAS in admixed populations. Nat Genet. 2021; 53(12):1634-1635. DOI: 10.1038/s41588-021-00975-z. View

2.
Li J, Absher D, Tang H, Southwick A, Casto A, Ramachandran S . Worldwide human relationships inferred from genome-wide patterns of variation. Science. 2008; 319(5866):1100-4. DOI: 10.1126/science.1153717. View

3.
Nagai A, Hirata M, Kamatani Y, Muto K, Matsuda K, Kiyohara Y . Overview of the BioBank Japan Project: Study design and profile. J Epidemiol. 2017; 27(3S):S2-S8. PMC: 5350590. DOI: 10.1016/j.je.2016.12.005. View

4.
Chang C, Chow C, Tellier L, Vattikuti S, Purcell S, Lee J . Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015; 4:7. PMC: 4342193. DOI: 10.1186/s13742-015-0047-8. View

5.
Hou K, Bhattacharya A, Mester R, Burch K, Pasaniuc B . On powerful GWAS in admixed populations. Nat Genet. 2021; 53(12):1631-1633. PMC: 8939372. DOI: 10.1038/s41588-021-00953-5. View