Falconer DS, Mackay TFC. Introduction to Quantitative Genetics. 4th ed. Pearson Education; 1995.

Thornton TA. Introduction to Quantitative Genetics; 2014. Accessed: 29 April 2026. Lecture notes for BIOST 551, University of Washington. Available from: http://faculty.washington.edu/tathornt/BIOST551/lectures_2014/Lecture1_Intro_Quantitative_Genetics.pdf.

Visscher PM, Hill WG, Wray NR. Heritability in the genomics era—concepts and misconceptions. Nat Rev Genet. 2008;9:255–66.

Article  CAS  PubMed  Google Scholar 

Matthews LJ, Turkheimer E. Three legs of the missing heritability problem. Stud Hist Philos Sci. 2022;93:183–91.

Article  PubMed  PubMed Central  Google Scholar 

Kvålseth TO. Cautionary note about R2. Am Statist. 1985;39:279–85.

Google Scholar 

Staerk C, Klinkhammer H, Wistuba T, Maj C, Mayr A. Generalizability of polygenic prediction models: how is the R2 defined on test data? BMC Med Genom. 2024;17:132.

Article  CAS  Google Scholar 

Lee SH, Goddard ME, Wray NR, Visscher PM. A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012;36:214–24.

Article  PubMed  Google Scholar 

Daetwyler HD, Calus MPL, Pong-Wong R, de los Campos G, Hickey JM. Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking. Genetics. 2013;193:347–65. https://doi.org/10.1534/genetics.112.147983. 

Article  PubMed  Google Scholar 

Schuurmann G, Ebert RU, Chen J, Wang B, Kuhne R. External validation and prediction employing the predictive squared correlation coefficient Test set activity mean vs training set activity mean. J Chem Inf Model. 2008;48:2140–5.

Article  PubMed  Google Scholar 

Paré G, Mao S, Deng WQ. A machine-learning heuristic to improve gene score prediction of polygenic traits. Sci Rep. 2017;7:12665.

Article  PubMed  PubMed Central  Google Scholar 

Spiess AN, Neumeyer N. An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach. BMC Pharmacol. 2010;10:6.

Article  PubMed  PubMed Central  Google Scholar 

Zheng B, Agresti A. Summarizing the predictive power of a generalized linear model. Stat Med. 2000;19:1771–81.

Article  CAS  PubMed  Google Scholar 

Christensen R. Analysis of variance, design, and regression: applied statistical methods. CRC Press; 1996.

Book SA, Young PH. The trouble with R2. J Parametr. 2006;25:87–114.

Article  Google Scholar 

Van Calster B, McLernon DJ, Van Smeden M, Wynants L, Steyerberg EW. diagnostic tests TGt al. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17:230.

Article  PubMed  PubMed Central  Google Scholar 

Jarantow SW, Pisors ED, Chiu ML. Introduction to the use of linear and nonlinear regression analysis in quantitative biological assays. Curr Protoc. 2023;3:e801.

Article  CAS  PubMed  Google Scholar 

Kelly CM, McLaughlin RL. Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits. PloS ONE. 2024;19:e0308962.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Burstin J, Salloignon P, Chabert-Martinello M, Magnin-Robert JB, Siol M, Jacquin F, et al. Genetic diversity and trait genomic prediction in a pea diversity panel. BMC Genomics. 2015;16:105.

Article  PubMed  PubMed Central  Google Scholar 

Balanovska E, Lukianova E, Kagazezheva J, Maurer A, Leybova N, Agdzhoyan A, et al. Optimizing the genetic prediction of the eye and hair color for North Eurasian populations. BMC Genomics. 2020;21:527.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kelemen M, Xu Y, Jiang T, Zhao JH, Anderson CA, Wallace C, et al. Performance of deep-learning-based approaches to improve polygenic scores. Nat Commun. 2025;16:5122.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wray NR, Kemper KE, Hayes BJ, Goddard ME, Visscher PM. Complex trait prediction from genome data: contrasting EBV in livestock to PRS in humans: genomic prediction. Genetics. 2019;211:1131–41.

Article  PubMed  PubMed Central  Google Scholar 

He R, Fu J, Ren J, Pan W. Trait imputation enhances nonlinear genetic prediction for some traits. Genetics. 2024;228:iyae148.

Article  CAS  PubMed  Google Scholar 

Chen F, Melton PE, Vinsen K, Mori T, Beilin L, Huang RC. Longitudinal prediction of BMI using explainable AI: integrating polygenic scores, maternal, early-life and familial factors: Epidemiology and Population Health. Int J Obes. 2026;50:1142–9.

Article  Google Scholar 

Wang J, Tiezzi F, Huang Y, Maltecca C, Jiang J. Benchmarking of feed-forward neural network models for genomic prediction of quantitative traits in pigs. Front Genet. 2025;16:1618891.

Article  PubMed  PubMed Central  Google Scholar 

Widen E, Raben TG, Lello L, Hsu SD. Machine learning prediction of biomarkers from SNPs and of disease risk from biomarkers in the UK Biobank. Genes. 2021;12:991.

Article  CAS  PubMed  PubMed Central  Google Scholar