1 |
Couto Alves A, De Silva NM, Karhunen V, Sovio U, Das S, Taal HR, et al. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI. Sci Adv 2019;5:eaaw3095.
DOI
|
2 |
Chung W. Statistical models and computational tools for predicting complex traits and diseases. Genomics Inform 2021;19:e36.
DOI
|
3 |
Gong Y, Zou F. Varying coefficient models for mapping quantitative trait loci using recombinant inbred intercrosses. Genetics 2012;190:475-486.
DOI
|
4 |
Chung W, Cho Y. Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies. Genomics Inform 2022;20:e8.
DOI
|
5 |
Gouveia MH, Bentley AR, Leonard H, Meeks KA, Ekoru K, Chen G, et al. Trans-ethnic meta-analysis identifies new loci associated with longitudinal blood pressure traits. Sci Rep 2021;11:4075.
DOI
|
6 |
Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, et al. A largescale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 2009;41:527-534.
DOI
|
7 |
Loh PR, Tucker G, Bulik-Sullivan BK, Vilhjalmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet 2015;47: 284-290.
DOI
|
8 |
Cochran WG. The combination of estimates from different experiments. Biometrics 1954;10:101-129.
DOI
|
9 |
Chen Z, Dunson DB. Random effects selection in linear mixed models. Biometrics 2003;59:762-769.
DOI
|
10 |
Jeffreys H. Theory of Probability. 3rd ed. Oxford: Clarendon Press, 1961.
|
11 |
Yandell BS, Mehta T, Banerjee S, Shriner D, Venkataraman R, Moon JY, et al. R/qtlbim: QTL with Bayesian interval mapping in experimental crosses. Bioinformatics 2007;23:641-643.
DOI
|
12 |
Bates D, Machler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4 Preprint at https://doi.org/10.48550/arXiv.1406.5823 (2014).
DOI
|
13 |
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-575.
DOI
|
14 |
Browning BL, Zhou Y, Browning SR. A one-penny imputed genome from next-generation reference panels. Am J Hum Genet 2018;103:338-348.
DOI
|
15 |
Loh PR, Kichaev G, Gazal S, Schoech AP, Price AL. Mixed-model association for biobank-scale datasets. Nat Genet 2018;50:906-908.
DOI
|
16 |
DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986;7:177-188.
DOI
|
17 |
Lin DY, Sullivan PF. Meta-analysis of genome-wide association studies with overlapping subjects. Am J Hum Genet 2009;85: 862-872.
DOI
|
18 |
Chung W, Chen J, Turman C, Lindstrom S, Zhu Z, Loh PR, et al. Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes. Nat Commun 2019;10:569.
DOI
|
19 |
Kwak IY, Moore CR, Spalding EP, Broman KW. A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes. Genetics 2014;197:1409-1416.
DOI
|
20 |
Ning C, Wang D, Zheng X, Zhang Q, Zhang S, Mrode R, et al. Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein. Genet Sel Evol 2018;50:12.
DOI
|
21 |
Chung W. Grid-based Gaussian process models for longitudinal genetic data. Commun Stat Appl Methods 2022;29:65-83.
|
22 |
Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Series B Stat Methodol 2002;64:583-639.
DOI
|
23 |
Ando T. Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models. Biometrika 2007;94:443-458.
DOI
|
24 |
Lee CH, Eskin E, Han B. Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects. Bioinformatics 2017;33:i379-i388.
DOI
|
25 |
Kim Y, Han BG; KoGES Group. Cohort Profile: The Korean Genome and Epidemiology Study (KoGES) Consortium. Int J Epidemiol 2017;46:e20.
DOI
|
26 |
Ning C, Wang D, Zhou L, Wei J, Liu Y, Kang H, et al. Efficient multivariate analysis algorithms for longitudinal genome-wide association studies. Bioinformatics 2019;35:4879-4885.
DOI
|
27 |
Vicuna L, Barrientos E, Norambuena T, Alvares D, Gana JC, Leiva V, et al. New insights from GWAS on longitudinal and cross-sectional BMI and related phenotypes in admixed children with Native American and European ancestries Preprint at https://doi.org/10.1101/2021.09.24.21263664 (2021).
DOI
|