Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes

Abstract

The accuracy of previous genetic studies in predicting polygenic psychiatric phenotypes has been limited mainly due to the limited power in distinguishing truly susceptible variants from null variants and the resulting overfitting. A novel prediction algorithm, Smooth-Threshold Multivariate Genetic Prediction (STMGP), was applied to improve the genome-based prediction of psychiatric phenotypes by decreasing overfitting through selecting variants and building a penalized regression model. Prediction models were trained using a cohort of 3685 subjects in Miyagi prefecture and validated with an independently recruited cohort of 3048 subjects in Iwate prefecture in Japan. Genotyping was performed using HumanOmniExpressExome BeadChip Arrays. We used the target phenotype of depressive symptoms and simulated phenotypes with varying complexity and various effect-size distributions of risk alleles. The prediction accuracy and the degree of overfitting of STMGP were compared with those of state-of-the-art models (polygenic risk scores, genomic best linear-unbiased prediction, summary-data-based best linear-unbiased prediction, BayesR, and ridge regression). In the prediction of depressive symptoms, compared with the other models, STMGP showed the highest prediction accuracy with the lowest degree of overfitting, although there was no significant difference in prediction accuracy. Simulation studies suggested that STMGP has a better prediction accuracy for moderately polygenic phenotypes. Our investigations suggest the potential usefulness of STMGP for predicting polygenic psychiatric conditions while avoiding overfitting.

Smooth-threshold multivariate genetic prediction incorporating gene–environment interactions

“…The difference between the STMGP and BLUP methods was pronounced under the NEG2 distribution, which has the heaviest tails among the three effect-size distributions compared. A similar result was observed in the simulation studies of Takahashi et al (2020) .…”

“…For a given screening cutoff value , which gives a SNP set , the estimates of the p regression coefficients are where is the cardinality of . The non-negative tuning parameters γ and τ are set to 1 and , respectively, following previous studies ( Ueki and Tamiya 2016 ; Takahashi et al 2020 ), and is a small constant to avoid singularity of . The corresponding prediction of y i is then , where is an adaptive lasso smooth-thresholding function defined as .…”

“…To examine the performance of the proposed method, we conducted simulation studies based on real SNP-GWAS data analogous to those of Takahashi et al (2020) . We used an ADNI-GWAS dataset obtained from the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( adni.loni.usc.edu ).…”

“…Popular models are linear in the variants (or SNPs), such as Purcell’s gene score ( Purcell et al 2009 ) and genomic best linear unbiased prediction (BLUP) ( Yang et al 2011 ). As an alternative, we developed a statistical method for genetic prediction modeling called smooth-threshold multivariate genetic prediction (STMGP) ( Ueki and Tamiya 2016 ), and Takahashi et al (2020) recently demonstrated that the performance of STMGP was superior to that of other genetic prediction methods for predicting status of depression with actual GWAS data. STMGP is a sparse modeling method based on a multiple linear regression model such as the lasso ( Tibshirani 1996 ) or the elastic net ( Zou and Hastie 2005 ), and it is able to account for the ultrahigh dimensionality of the situation by filtering variants based on the corresponding marginal-effect P -values calculated from univariate regressions arising from a genome-wide scan.…”

“…Recent advances in data platforms now make it possible to integrate feature variables other than variants, such as those associated with lifestyle, clinical variables, imaging, etc. The simplest integration is to enter everything as an additive term in a multiple linear regression model as implemented in Ueki and Tamiya (2016 ) and Takahashi et al (2020) . While such an additive modeling approach is simple and straightforward, there may be cases where other approaches are more appropriate.…”