Table 2.

Model performance of APOE-ε4 count, polygenic risk score, and SNP models in dementia genetic prediction, UCLA ATLAS sample, stratified by genetic inferred ancestry^a

		AUPRC	AUROC	F1 score	Accuracy	Precision	Recall	Specificity
Hispanic Latino Americans (N = 610)
APOE	ε4 count	0.308	0.652	0.424	0.707	0.357	0.524	0.754
AD-PRS models
AD EUR PRS	P-significant	0.306	0.619	0.335	0.759	0.389	0.294	0.880
	Gene-annotated	0.288	0.615	0.387	0.397	0.245	0.921	0.260
AD AFR PRS	P-significant	0.312	0.644	0.409	0.692	0.339	0.516	0.738
	Gene-annotated	0.305	0.648	0.427	0.666	0.330	0.603	0.682
AD multi-ancestry PRS	P-significant	0.298	0.626	0.389	0.444	0.252	0.857	0.337
	Gene-annotated	0.298	0.640	0.401	0.448	0.259	0.897	0.331
Multi-PRS models
PRSs using AD GWASs onlyb	P-significant	0.312	0.643	0.415	0.644	0.314	0.611	0.653
	Gene-annotated	0.302	0.646	0.404	0.670	0.322	0.540	0.705
PRSs using AD + Neuro GWASsc	P-significant	0.283	0.617	0.382	0.661	0.306	0.508	0.700
	Gene-annotated	0.309	0.643	0.411	0.662	0.321	0.571	0.686
Elastic Net SNPs models
SNPs from AD GWASs only	P-significant	0.321	0.662	0.408	0.530	0.276	0.786	0.463
	Gene-annotated	0.351	0.679	0.436	0.602	0.308	0.746	0.564
SNPs from AD + Neuro GWASs	P-significant	0.359	0.715	0.472	0.633	0.336	0.794	0.591
	Gene-annotated	0.410	0.728	0.458	0.779	0.463	0.452	0.864
Non-linear SNPs models
SNPs from AD + Neuro GWASs	GBM	0.304	0.634	0.381	0.707	0.337	0.437	0.777
Gene-annotated SNPs	XGBoost	0.298	0.642	0.375	0.710	0.338	0.421	0.785
African Americans (N = 440)
APOE	ε4 count	0.271	0.606	0.388	0.570	0.267	0.714	0.537
AD-PRS models
AD EUR PRS	P-significant	0.221	0.592	0.369	0.432	0.234	0.869	0.329
	Gene-annotated	0.226	0.573	0.348	0.318	0.213	0.952	0.169
AD AFR PRS	P-significant	0.242	0.584	0.322	0.732	0.311	0.333	0.826
	Gene-annotated	0.241	0.581	0.344	0.584	0.246	0.571	0.587
AD multi-ancestry PRS	P-significant	0.234	0.592	0.360	0.386	0.225	0.905	0.264
	Gene-annotated	0.230	0.598	0.370	0.443	0.236	0.857	0.346
Multi-PRS models
PRSs using AD GWASs onlyb	P-significant	0.238	0.589	0.358	0.527	0.242	0.690	0.489
	Gene-annotated	0.233	0.590	0.357	0.484	0.234	0.750	0.421
PRSs using AD + Neuro GWASsc	P-significant	0.187	0.516	0.311	0.195	0.186	0.952	0.017
	Gene-annotated	0.217	0.538	0.087	0.809	0.500	0.048	0.989
Elastic Net SNPs models
SNPs from AD GWASs only	P-significant	0.356	0.669	0.356	0.802	0.471	0.286	0.924
	Gene-annotated	0.421	0.678	0.342	0.834	0.704	0.226	0.978
SNPs from AD + Neuro GWASs	P-significant	0.391	0.704	0.342	0.825	0.606	0.238	0.963
	Gene-annotated	0.446	0.710	0.365	0.834	0.677	0.250	0.972
Non-linear SNPs models
SNPs from AD + Neuro GWASs	GBM	0.225	0.479	0.314	0.186	0.187	0.976	0.000
Gene-annotated SNPs	XGBoost	0.220	0.506	0.139	0.802	0.412	0.083	0.972

Abbreviations: AD Alzheimer’s Disease, APOE apolipoprotein E, AUROC Area Under the ROC Curve, AUPRC Area Under the Precision-Recall Curve, EUR European, GBM Gradient Boosting Machine, GWAS Genome-Wide Association Study, PRS Polygenic Risk Score, SNP Single-Nucleotide Polymorphism.

Notes:

^aAll models (if not other specified) have regressed out age, sex, and ancestry-specific principal components. Thresholds were determined by maximizing absolute Matthews correlation coefficient.

^bAll AD PRSs built with EUR, AFR, and multi-ancestry GWASs using P-significant/gene-annotated SNPs were included in the model at the same time.

^cAll AD PRSs built with EUR, AFR, and multi-ancestry GWASs, and neurodegenerative disease PRS (Parkinson’s disease, progressive supranuclear palsy, Lewy body dementia, and stroke) using P-significant/gene-annotated SNPs were included in the model at the same time.