Imaging genetics aims to uncover the hidden relationship between imaging quantitative traits (QTs) and genetic markers [e.g., single nucleotide polymorphism (SNP)] and brings valuable insights into the pathogenesis of complex diseases, such as cancers and cognitive disorders (e.g., Alzheimer’s disease). However, most linear models in imaging genetics did not explicitly model the inner relationship among QTs, which might miss some potential efficiency gains from information borrowing across brain regions. In this work, we developed a novel Bayesian regression framework for identifying significant associations between QTs and genetic markers while explicitly modeling spatial dependency between QTs, with the main contributions as follows. First, we developed a spatial-correlated multitask linear mixed-effects model to account for dependencies between QTs. We incorporated a population-level mixed-effects term into the model, taking full advantage of the dependent structure of brain imaging-derived QTs. Second, we implemented the model in the Bayesian framework and derived a Markov chain Monte Carlo (MCMC) algorithm to achieve the model inference. Further, we incorporated the MCMC samples with the Cauchy combination test to examine the association between SNPs and QTs, which avoided computationally intractable multitest issues. The simulation studies indicated improved power of our proposed model compared with classical models where inner dependencies of QTs were not modeled. We also applied the new spatial model to an imaging dataset obtained from the Alzheimer’s Disease Neuroimaging Initiative database (https://adni.loni.usc.edu). The implementation of our method is available at https://github.com/ZhibinPU/spatialmultitasklmm.git.
AsensioEM, Ortega-AzorínC, BarragánR, et al.Association between microbiome-related human genetic variants and fasting plasma glucose in a high-cardiovascular-risk Mediterranean population. Medicina, 2022; 58(9):1238.
2.
BeaulacC, WuS, GibsonE, et al.Neuroimaging feature extraction using a neural network classifier for imaging genetics. BMC Bioinformatics, 2023; 24(1):271.
3.
BeckTN, NicolasE, KoppMC, et al.Adaptors for disorders of the brain? The cancer signaling proteins NEDD9, CASS4, and PTK2B in Alzheimer’s disease. Oncoscience, 2014; 1(7):486–503.
4.
CaoX, WangX, ZhangS, et al.Gene-based association tests using GWAS summary statistics and incorporating EQTL. Sci Rep, 2022; 12(1):3553.
5.
ChenD, ZhouXZ, LeeTH. Death-associated protein kinase 1 as a promising drug target in cancer and Alzheimer’s disease. Recent Pat Anticancer Drug Discov, 2019; 14(2):144–157.
6.
ChibnikLB, ShulmanJM, LeurgansSE, et al.Cr1 is associated with amyloid plaque burden and age-related cognitive decline. Ann Neurol, 2011; 69(3):560–569.
7.
ChoiJ, LuD, BegMF, et al.; for the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The contribution plot: Decomposition and graphical display of the RV coefficient, with application to genetic and brain imaging biomarkers of Alzheimer’s disease. Hum Hered, 2019; 84(2):59–72.
8.
DahlA, IotchkovaV, BaudA, et al.A multiple-phenotype imputation method for genetic studies. Nat Genet, 2016; 48(4):466–472.
9.
DeschampsI, BaumSR, GraccoVL. On the role of the supramarginal gyrus in phonological processing and verbal working memory: Evidence from RTMS studies. Neuropsychologia, 2014; 53:39–46.
10.
EberlyLE, CasellaG. Estimating Bayesian credible intervals. Journal of Statistical Planning and Inference, 2003; 112(1–2):115–132.
11.
ElliottLT, SharpK, Alfaro-AlmagroF, et al.Genome-wide association studies of brain imaging phenotypes in uk biobank. Nature, 2018; 562(7726):210–216.
12.
FusiN, LippertC, LawrenceND, et al.Warped linear mixed models for the genetic analysis of transformed phenotypes. Nat Commun, 2014; 5(1):4890.
13.
GerberAJ, PetersonBS, MuñozKE, et al.Imaging genetics. J Am Acad Child Adolesc Psychiatry, 2009; 48(4):356–361.
14.
GreenlawK, SzeferE, GrahamJ, et al.; Alzheimer’s Disease Neuroimaging Initiative. A Bayesian group sparse multi-task regression model for imaging genetics. Bioinformatics, 2017; 33(16):2513–2522.
15.
HazratiL-N, Van CauwenbergheC, BrooksPL, et al.Genetic association of CR1 with Alzheimer’s disease: A tentative disease mechanism. Neurobiol Aging, 2012; 33(12):2949.e5–2949.e12.
16.
HespanholL, VallioCS, CostaLM, et al.Understanding and interpreting confidence and credible intervals around effect estimates. Braz J Phys Ther, 2019; 23(4):290–301.
17.
HibarDP, SteinJL, KohannimO, et al.; Alzheimer’s Disease Neuroimaging Initiative. Voxelwise gene-wide association study (vgenewas): multivariate gene-based association testing in 731 elderly subjects. Neuroimage, 2011; 56(4):1875–1891.
18.
HoshiH, KobayashiM, HirataY, et al.Decreased beta-band activity in left supramarginal gyrus reflects cognitive decline: Evidence from a large clinical dataset in patients with dementia. Hum Brain Mapp, 2023; 44(17):6214–6226.
19.
HuangJ, LingCX. Using auc and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng, 2005; 17(3):299–310.
20.
HuangM, NicholsT, HuangC, et al.; Alzheimer’s Disease Neuroimaging Initiative. Fvgwas: Fast voxelwise genome wide association analysis of large-scale imaging genetic data. Neuroimage, 2015; 118:613–627.
21.
KongD, AnB, ZhangJ, et al.L2rm: Low-rank linear regression models for high-dimensional matrix responses. J Am Stat Assoc, 2020; 115(529):403–424.
22.
KunduS, KangJ. Semiparametric bayes conditional graphical models for imaging genetics applications. Stat (Int Stat Inst), 2016; 5(1):322–337.
23.
LaumetG, ChourakiV, Grenier-BoleyB, et al.Systematic analysis of candidate genes for Alzheimer’s disease in a French, genome-wide association study. J Alzheimers Dis, 2010; 20(4):1181–1188.
24.
LehmannB, HensonR, GeerligsL, et al.Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models. Neuroimage, 2021; 225:117480.
25.
LenczT, SzeszkoPR, DeRosseP, et al.A schizophrenia risk gene, znf804a, influences neuroanatomical and neurocognitive phenotypes. Neuropsychopharmacology, 2010; 35(11):2284–2291.
26.
LiY, GrupeA, RowlandC, et al.Dapk1 variants are associated with Alzheimer’s disease and allele-specific expression. Hum Mol Genet, 2006; 15(17):2560–2568.
27.
LingCX, HuangJ, ZhangH. Auc: a better measure than accuracy in comparing learning algorithms. In Advances in Artificial Intelligence: 16th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2003, Halifax, Canada, June 11–13, 2003. Springer; 2003, pp. 329–341.
28.
LittleRJ, RubinDB. Statistical analysis with missing data. John Wiley & Sons; 1987.
29.
LiuY, XieJ. Cauchy combination test: A powerful test with analytic p-value calculation under arbitrary dependency structures. J Am Stat Assoc, 2020; 115(529):393–402.
30.
LiuY, LiuZ, LinX. Ensemble methods for testing a global null. J R Stat Soc Series B Stat Methodol, 2024; 86(2):461–486.
31.
LuD, YeM, HillMC. Analysis of regression confidence intervals and Bayesian credible intervals for uncertainty quantification. Water Resources Research, 2012; 48(9).
32.
PhillipsNR. A longitudinal study of changes in the mtDNA of Alzheimer’s disease patients. PhD thesis, University of North Texas Health Science Center: Fort Worth; 2013.
33.
SongY, GeS, CaoJ, et al.A Bayesian spatial model for imaging genetics. Biometrics, 2022; 78(2):742–753.
34.
SteinJL, HuaX, LeeS, et al.; Alzheimer’s Disease Neuroimaging Initiative. Voxelwise genome-wide association study (VGWAS). Neuroimage, 2010; 53(3):1160–1174.
35.
SunJ, MallerJJ, GuoL, et al.Superior temporal gyrus volume change in schizophrenia: A review on region of interest volumetric studies. Brain Res Rev, 2009; 61(1):14–32.
36.
TangY, LiuX. G2p: A genome-wide-association-study simulation tool for genotype simulation, phenotype simulation and power evaluation. Bioinformatics, 2019; 35(19):3852–3854.
37.
TranM-N, NguyenT-N, DaoV-H. A practical tutorial on variational bayes. arXiv Preprint arXiv:2103.013272021.
38.
TveitA. On the complexity of matrix inversion. Mathematical Note, 2003; 1.
WangC, Martins-BachAB, Alfaro-AlmagroF, et al.Phenotypic and genetic associations of quantitative magnetic susceptibility in UK biobank brain imaging. Nat Neurosci, 2022; 25(6):818–831.
41.
WangH, NieF, HuangH, et al.; For the Alzheimer’s Disease Neuroimaging Initiative. Identifying quantitative trait loci via group-sparse multitask regression and feature selection: An imaging genetics study of the Adni cohort. Bioinformatics, 2012; 28(2):229–237.
42.
WatzaD, LuskCM, DysonG, et al.Copd-dependent effects of genetic variation in key inflammation pathway genes on lung cancer risk. Int J Cancer, 2020; 147(3):747–756.
43.
WuMC, LeeS, CaiT, et al.Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet, 2011; 89(1):82–93.
44.
XingY-Y, YuJ-T, YanW-J, et al.Nedd9 is genetically associated with Alzheimer’s disease in a Han Chinese population. Brain Res, 2011; 1369:230–234.
45.
XuW, XuJ, WangY, et al.The genetic variation of sorcs1 is associated with late-onset Alzheimer’s disease in Chinese Han population. PLoS One, 2013; 8(5):e63621.
46.
YuanM, LinY. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2006; 68(1):49–67.
47.
ZhuH, KhondkerZ, LuZ, et al.Bayesian generalized low rank regression models for neuroimaging phenotypes and genetic markers. Journal of the American Statistical Association, 2014; 109(507):977–990.
48.
ZhuX-C, YuJ-T, JiangT, et al.Cr1 in Alzheimer’s disease. Mol Neurobiol, 2015; 51(2):753–765.
49.
ZhuX-C, WangH-F, JiangT, et al.; Alzheimer’s Disease Neuroimaging Initiative. Effect of cr1 genetic variants on cerebrospinal fluid and neuroimaging biomarkers in healthy, mild cognitive impairment and Alzheimer’s disease cohorts. Mol Neurobiol, 2017; 54(1):551–562.
