Genetic architecture of the limbic white matter microstructure in aging and Alzheimer’s Disease

Genetic architecture of the limbic white matter microstructure in aging and Alzheimer’s Disease | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Genetic architecture of the limbic white matter microstructure in aging and Alzheimer’s Disease Anna Lorenz , Aditi Sathe , Yisu Yang , Alaina Durant , Yiyang Wu , Michael E. Kim , Chenyu Gao , Nancy R. Newlin , Karthik Ramadass , Praitayini Kanakaraj , Nazirah Mohd Khairi , Zhiyuan Li , Tianyuan Yao , Yuankai Huo , Logan Dumitrescu , Niranjana Shashikumar , Kimberly R. Pechman , Shannon L. Risacher , Lori L. Beason-Held , Yang An , Konstantinos Arfanakis , Guray Erus , Christos Davatzikos , Mohamad Habes , Di Wang , Duygu Tosun , Arthur W. Toga , Paul M. Thompson , Elizabeth C. Mormino , Panpan Zhang , Kurt Schilling , Alzheimer’s Disease Neuroimaging Initiative (ADNI) , The BIOCARD Study Team , The Alzheimer’s Disease Sequencing Project (ADSP) , Marilyn Albert , Walter Kukull , Sarah A. Biber , Bennett A. Landman , Sterling C. Johnson , Barbara Bendlin , Julie Schneider , David A. Bennett , Angela L. Jefferson , Susan M. Resnick , Andrew J. Saykin , Timothy J. Hohman , Derek B. Archer doi: https://doi.org/10.1101/2025.05.19.25327915 Anna Lorenz 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA 5 Vanderbilt Genetics Institute, Vanderbilt University Medical Center , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aditi Sathe 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yisu Yang 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alaina Durant 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yiyang Wu 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael E. Kim 3 Department of Computer Science, Vanderbilt University Nashville , TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chenyu Gao 4 Department of Electrical and Computer Engineering, Vanderbilt University , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nancy R. Newlin 3 Department of Computer Science, Vanderbilt University Nashville , TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karthik Ramadass 3 Department of Computer Science, Vanderbilt University Nashville , TN, 37232, USA 4 Department of Electrical and Computer Engineering, Vanderbilt University , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Praitayini Kanakaraj 3 Department of Computer Science, Vanderbilt University Nashville , TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nazirah Mohd Khairi 4 Department of Electrical and Computer Engineering, Vanderbilt University , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhiyuan Li 4 Department of Electrical and Computer Engineering, Vanderbilt University , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tianyuan Yao 3 Department of Computer Science, Vanderbilt University Nashville , TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuankai Huo 4 Department of Electrical and Computer Engineering, Vanderbilt University , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Logan Dumitrescu 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA 2 Department of Neurology, Vanderbilt University Medical Center , Nashville, TN, 37232, USA 5 Vanderbilt Genetics Institute, Vanderbilt University Medical Center , Nashville, TN, 37232, USA 6 Vanderbilt Brain Institute, Vanderbilt University Medical Center Nashville , TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Niranjana Shashikumar 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kimberly R. Pechman 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shannon L. Risacher 9 Department of Radiology and Imaging Sciences, Indiana University School of Medicine , Indianapolis, IN, 46202, USA 10 Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine , Indianapolis, IN, 46202, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lori L. Beason-Held 11 Laboratory for Behavioral Neuroscience, National Institute on Aging, National Institutes of Health , Baltimore, MD, 21224, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yang An 11 Laboratory for Behavioral Neuroscience, National Institute on Aging, National Institutes of Health , Baltimore, MD, 21224, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Konstantinos Arfanakis 12 Department of Biomedical Engineering, Illinois Institute of Technology , Chicago, IL, 60616, USA 13 Rush Alzheimer’s Disease Center, Rush University Medical Center , Chicago, IL, 60612, USA 14 Department of Diagnostic Radiology, Rush University Medical Center , Chicago, IL, 60612, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Guray Erus 15 Department of Radiology, University of Pennsylvania , Philadelphia, PA, 19103, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christos Davatzikos 15 Department of Radiology, University of Pennsylvania , Philadelphia, PA, 19103, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mohamad Habes 16 Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio , San Antonio, TX, 78229, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Di Wang 16 Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio , San Antonio, TX, 78229, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Duygu Tosun 17 Department of Radiology and Biomedical Imaging, University of California San Francisco , San Francisco, CA, 94143, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arthur W. Toga 18 Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of Medicine, University of Southern California , Los Angeles, CA, 90033, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul M. Thompson 19 Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California , Marina del Rey, CA, 90033, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth C. Mormino 20 Department of Neurology and Neurological Sciences, Stanford University School of Medicine , Stanford, CA, 94304, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Panpan Zhang 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA 21 Department of Biostatistics, Vanderbilt University Medical Center Nashville , TN, 37203, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kurt Schilling 22 Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center , Nashville, TN, 37232, USA 23 Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center Nashville , TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marilyn Albert 24 Department of Neurology, Johns Hopkins School of Medicine , Baltimore, MD, 21205, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Walter Kukull 25 National Alzheimer’s Coordinating Center, University of Washington , Seattle, WA, 98195, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah A. Biber 25 National Alzheimer’s Coordinating Center, University of Washington , Seattle, WA, 98195, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bennett A. Landman 5 Vanderbilt Genetics Institute, Vanderbilt University Medical Center , Nashville, TN, 37232, USA 22 Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center , Nashville, TN, 37232, USA 23 Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center Nashville , TN, 37232, USA 26 Department of Biomedical Engineering, Vanderbilt University , Nashville, TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sterling C. Johnson 27 Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin , Madison, WI, 53792, USA 28 Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin , Madison, WI, 53726, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Barbara Bendlin 27 Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin , Madison, WI, 53792, USA 28 Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin , Madison, WI, 53726, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julie Schneider 13 Rush Alzheimer’s Disease Center, Rush University Medical Center , Chicago, IL, 60612, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site David A. Bennett 13 Rush Alzheimer’s Disease Center, Rush University Medical Center , Chicago, IL, 60612, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Angela L. Jefferson 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA 2 Department of Neurology, Vanderbilt University Medical Center , Nashville, TN, 37232, USA 6 Vanderbilt Brain Institute, Vanderbilt University Medical Center Nashville , TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Susan M. Resnick 11 Laboratory for Behavioral Neuroscience, National Institute on Aging, National Institutes of Health , Baltimore, MD, 21224, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrew J. Saykin 9 Department of Radiology and Imaging Sciences, Indiana University School of Medicine , Indianapolis, IN, 46202, USA 10 Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine , Indianapolis, IN, 46202, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Timothy J. Hohman 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA 2 Department of Neurology, Vanderbilt University Medical Center , Nashville, TN, 37232, USA 5 Vanderbilt Genetics Institute, Vanderbilt University Medical Center , Nashville, TN, 37232, USA 6 Vanderbilt Brain Institute, Vanderbilt University Medical Center Nashville , TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Derek B. Archer 1 Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine , Nashville, TN, 37232, USA 2 Department of Neurology, Vanderbilt University Medical Center , Nashville, TN, 37232, USA 5 Vanderbilt Genetics Institute, Vanderbilt University Medical Center , Nashville, TN, 37232, USA 6 Vanderbilt Brain Institute, Vanderbilt University Medical Center Nashville , TN, 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: derek.archer{at}vumc.org Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Limbic white matter (WM) abnormalities are prevalent in aging and Alzheimer’s disease (AD), yet their underlying biological mechanisms remain unclear. This study aims to identify the genetic architecture of limbic WM microstructure in older adults by leveraging harmonized data from multiple cohorts, including those enriched for cognitively impaired individuals. Methods We analyzed diffusion MRI (dMRI) data from 2,614 non-Hispanic White older adults (mean age = 73.7 ± 9.8 years; 57% female; 26% cognitively impaired) across 7 harmonized aging cohorts. WM microstructure was assessed in 7 limbic tracts, including the cingulum, fornix, inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and transcallosal tracts of the inferior, middle, and superior temporal gyri (ITG, MTG, STG) using advanced diffusion MRI metrics corrected for free-water (FW): fractional anisotropy (FA FWcorr ), axial diffusivity (AxD FWcorr ), mean diffusivity (MD FWcorr ), radial diffusivity (RD FWcorr ). We performed heritability estimations, genome-wide association studies (GWAS) and post-GWAS analyses (genetic covariance, gene-level and pathway analysis, transcriptome-wide association [TWAS] studies). The AD relevance of the discovered variants was explored using bulk RNA-seq data from caudate, dorsolateral prefrontal, and posterior cingulate cortex human brain tissues. Results Limbic WM microstructure demonstrated significant heritability (estimates between 0.26 and 0.60, p FDR < 0.05 for 15 of 35 tract-by-microstructure combinations). GWAS identified 6 genome-wide significant loci ( p < 5.0×10 −8 ) associated with WM microstructure. Notably, for MTG RD FWcorr , we identified a locus on chromosome 18 (lead SNP: rs12959877) comprising 38 SNPs that are eQTLs for CDH19 , a gene involved in cell adhesion and highly expressed in oligodendrocytes. Other significant associations involved SNPs near KC6, SENP5, RORA, FAM107B , and MIR548A1 . Bulk RNA-seq analyses revealed that brain tissue expression of RORA, FAM107B , and KC6 was significantly associated with cognitive decline and several AD pathologies ( p FDR < 0.05). Post-GWAS analyses identified the genes SERPINA12 and DNAJB14 , and highlighted the involvement of insulin signaling, immune response, and neurotrophic pathways. Genetic covariance analyses indicated shared genetic architecture between limbic WM and lipid profiles (e.g., HDL cholesterol), cardiovascular traits, and neurological conditions (e.g., multiple sclerosis) ( p FDR < 0.05). Conclusion This multi-cohort imaging genetics study identified several novel genes and biological pathways associated with limbic WM microstructure in an aging population enriched for cognitive impairment. The association of several identified genes with cognitive decline and AD pathology underscores their AD relevance. Our findings further suggest that the genetic underpinnings of limbic WM microstructure are linked to vascular health and inflammation, highlighting these pathways as promising avenues for future AD-related therapeutic development. Introduction The integrity of nerve fibers and the myelination of axons, which characterize white matter (WM) microstructure, are essential for efficient cognitive processing by enabling communication across different brain regions. 1 , 2 However, WM undergoes substantial changes during aging and in neurodegenerative diseases such as Alzheimer’s disease (AD). 3 While AD has typically been viewed as a disorder primarily affecting gray matter, emerging evidence highlights distinct WM abnormalities, including axonal loss, demyelination, and microglial activation, even in early disease stages. 4 – 6 Our group and others have demonstrated that WM abnormalities, particularly within the limbic system, are prevalent across the AD diagnostic continuum, 7 with accelerated WM degeneration in individuals exhibiting abnormal aging 8 and carriers of the apolipoprotein ( APOE ) ε4 allele. 9 These WM changes can manifest before the appearance of AD pathologies such as amyloid-β plaques and neurofibrillary tangles, 5 are detectable up to 22 years before symptom onset, 10 , 11 and contribute to cognitive decline independently of gray matter atrophy such as hippocampal volume loss. 12 Diffusion MRI (dMRI) is a non-invasive technique that allows for the in vivo quantification of WM microstructure by measuring water diffusion. 13 – 16 By estimating the magnitude and directionally of diffusivity within each voxel, the method can quantify several conventional metrics, including fractional anisotropy (FA CONV ), axial diffusivity (AxD CONV ), mean diffusivity (MD CONV ), and radial diffusivity (RD CONV ). FA CONV reflects the directional dependence of water diffusion, with higher values typically indicating compact WM tracts. AxD CONV measures diffusion along the primary axis of fibers, where lower values can indicate axonal damage. MD CONV represents the average diffusion rate, providing a general tissue density and cellularity measure. RD CONV measures diffusion perpendicular to the primary axis, with higher values often suggesting demyelination. 13 – 16 These conventional metrics, however, derived from single-tensor models can be confounded by partial volume effects of brain tissue with extracellular free water (FW), such as cerebrospinal fluid. Advanced bi-tensor models address this by estimating and correcting these metrics for FW contributions, yielding more biologically precise metrics of WM microstructure (FA FWcorr , AxD FWcorr , RD Fwcorr, MD FWcorr ) that differentiate between extracellular (i.e., FW) and intracellular contribution to water diffusion. 17 Prior studies suggest that these FW-corrected metrics offer a more comprehensive assessment of WM neurodegenerative patterns in AD. 7 – 9 , 12 In vivo measurements of WM microstructure have enabled the examination of the underlying biological mechanisms involved and have thus far demonstrated that WM microstructure across the brain is significantly influenced by genetic factors. Twin studies have reported high heritability for dMRI metrics (up to 82%), indicating substantial genetic contributions to variations in WM microstructure. 18 , 19 Large-scale genome-wide association studies (GWAS) primarily in cognitively unimpaired populations, such as the UK Biobank, have identified more than 100 genomic regions influencing WM microstructure and have revealed genetic links between WM integrity and various clinical traits such as stroke, major depressive disorder, schizophrenia, and attention deficit hyperactivity disorder. 20 , 21 However, these studies may not fully capture genetic pathways specific to neurodegenerative processes due to the underrepresentation of cognitively impaired individuals. Investigations focused on AD have implicated candidate genes, such as APOE and BIN1 , in WM alterations. 22 – 25 Additionally, an increased genetic predisposition for AD correlates with reduced cingulum bundle FA CONV 26 and several AD risk variants in TMEM106B, PTK2B, WNT3 , and APOE are associated with WM microstructure in older adults. 27 Nevertheless, a more comprehensive understanding of the genetic architecture and biological pathways influencing WM microstructural differences in older adults, particularly those at risk for or with AD, remains limited due to the scarcity of combined diffusion and genetic data in such cohorts. Therefore, examining genetic influences on WM microstructure in cohorts enriched for cognitive impairment is critical for advancing our understanding of neurodegenerative processes in aging and AD. Large-scale harmonization efforts, such as the Alzheimer’s Disease Sequencing Project Phenotype Harmonization Consortium (ADSP-PHC), in combination with advanced FW-corrected dMRI methods presents an unprecedented opportunity to investigate these imaging genetic associations. This study assessed the role of genetic factors in WM microstructure alterations across 7 tracts of the limbic system, including the cingulum, fornix, inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and the transcallosal tracts of the inferior temporal gyrus (ITG), the middle temporal gyrus (MTG), and the superior temporal gyrus (STG). These tracts are integral to memory function and show substantial alterations in aging and AD. 7 , 12 , 24 By analyzing harmonized FW-corrected dMRI and genetic data from 2,614 older adults (26% cognitively impaired) across 7 established aging cohorts, we aim to identify novel genetic variants and biological pathways associated with limbic WM microstructure, ultimately providing insights into the mechanisms of WM degeneration in the context of aging and AD. Methods Participants Diffusion MRI (dMRI) and genetic data were leveraged from 7 cohorts, including the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD), Baltimore Longitudinal Study of Aging (BLSA), National Alzheimer’s Coordinating Center (NACC), Religious Orders Study and Rush Memory and Aging Project (ROSMAP), Vanderbilt Memory and Aging Project (VMAP), and the Wisconsin Registry for Alzheimer’s Prevention (WRAP). Data collection for ADNI ( https://adni.loni.usc.edu ) began in 2004 as a public-private partnership, gathering data from cognitively unimpaired individuals, those with mild cognitive impairment (MCI), and participants with dementia due to AD. The project’s goal was to investigate the associations between serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, clinical and neuropsychological assessments, and the progression from MCI to early AD. 28 ADNI-GO, ADNI2, and ADNI3 phases with dMRI data were included in this study. BIOCARD began data collection in 1995 at the National Institute of Mental Health (NIMH) and was later transferred to John Hopkins University with the goal of identifying preclinical biomarkers of cognitive decline and investigating variables that can predict future progression to AD among cognitively normal middle-aged individuals. Participants undergo extensive longitudinal evaluations, including neuropsychological testing, MRI scans, and collection of blood and cerebrospinal fluid samples. 29 The BLSA neuroimaging substudy began data collection in 1994 and included dementia-free participants aged 55 to 85 years, with up to 10 years of prospective data collection at baseline. In 2009, the cohort was expanded to include BLSA participants aged 20 to 85 years as well as 3T MRI-based acquisition of dMRI data. NACC maintains a centralized data repository for the National Institute of Aging’s (NIA’s) Alzheimer’s Disease Research Centers (ADRC) Program, which currently includes 33 centers and 4 exploratory centers across the United States. 30 – 32 The ROS cohort, which began in 1994, is an ongoing longitudinal study collecting clinical-pathological data on aging and AD. Participants in ROS are 65+ years-old Catholic nuns, priests, and brothers from various groups throughout the US. 33 MAP is a longitudinal study that started in 1997, recruiting cognitively unimpaired participants. 33 The VMAP cohort began longitudinal data collection in 2012 with the goal of understanding the relationship between vascular health and brain aging, enriched in older adults with MCI. 34 The WRAP cohort began data collection in 2001, focusing on middle-aged adults with a parental history of AD. In 2004, the study expanded to include a control group of individuals without a parental history of AD. The primary objective of WRAP is to identify early biomarkers and risk factors for AD before clinical symptoms emerge. 35 , 36 For all cohorts, participants provided written informed consent, and research was conducted in accordance with approved Institutional Review Board protocols. Secondary analysis of these data was approved by the Vanderbilt University Medical School Institutional Review Board. The study included 2,614 participants who were self-reported non-Hispanic White individuals aged between 50.1 and 100.9 years (mean = 73.7 ± 9.8 years) and 57% were female. Table 1 provides an overview of data included for this study from the ADNI, BIOCARD, BLSA, NACC, ROSMAP, VMAP, and WRAP cohorts. View this table: View inline View popup Download powerpoint TABLE 1. Participant characteristics by cohort Diffusion MRI acquisition and preprocessing Across all cohorts, we had 78 different dMRI acquisition protocols – Table S1 provides relevant parameters (e.g., number of directions, b-values, resolution). dMRI data from all cohorts were preprocessed using the PreQual pipeline, which addresses motion, distortions and eddy currents and performs slice-wise imputation. 37 , 38 Imaging sessions were inspected manually by evaluating PDF reports outputted by the PreQual pipeline. DTIFIT was used for the remaining data to compute conventional dMRI metrics, including FA CONV , AxD CONV , MD CONV , and RD CONV . To account for FW content in each voxel, we calculated FW-corrected metrics, including FA FWcorr , AxD FWcorr , MD FWcorr , RD FWcorr and FW. 17 Symmetric normalization and linear interpolation was conducted using the Advanced Normalization Tools (ANTs) package to obtain a standard space representation of these maps by non-linearly registering the FA CONV map to FMRIB58_FA atlas. 39 The obtained warp from the registration was applied to all other microstructural maps. Individuals with significant age-regressed outliers (± 5 standard deviations) in WM tract microstructural values were excluded. 40 WM tractography templates Tractography templates for this study were leveraged from existing resources 12 , 41 , 42 and can be retrieved in a publicly available Zenodo repository. 43 The focus for this study was on 7 WM tracts within the limbic system, specifically the cingulum, fornix, ILF, UF, as well as ITG, MTG, and STG transcallosal tracts. Data harmonization For the dMRI data, a region of interest (ROI) approach was employed to calculate mean conventional and FW-corrected dMRI metrics for all tractography templates for each participant. These values were then harmonized using the Longitudinal ComBat package in R (version 4.1.0) 44 applied to our entire in-house longitudinal dataset, including 5,144 participants across 10,346 imaging timepoints. This harmonization process utilized a batch variable which had varying levels of specificity depending on the cohort. Parameters used to create the batch variable can be found in Table S2 . 7 – 9 The batch variable accounted for various parameters across our cohorts, including scanner name, magnet strength, number of b-values/b-vectors, and resolution – this was optimized to reduce the number of batches while simultaneously accounting for parameters we most anticipated to account for between-batch heterogeneity. In total, we accounted for 34 unique batching levels. Additional covariates for the harmonization included mean-centered age, mean-centered age squared, education, race/ethnicity, cognitive status (cognitively unimpaired or cognitively impaired), APOE -ε4 positivity, APOE -ε2 positivity, and the interaction of mean-centered age and cognitive status. The harmonized values were then scaled by their standard deviation. Next, the dataset was filtered to include only participants with genetic data and further narrowed to the baseline timepoint for cross-sectional analysis. We ultimately used FW-corrected dMRI measures across 7 limbic WM tracts (35 dMRI measures) for each participant. Genetic data quality control and imputation Genetic data were collected with various genotyping arrays across and within cohorts (ADNI: Illumina Human610-Quad BeadChip, Illumina HumanOmniExpress BeadChip, Illumina Omni 2.5 M, Illumnia Global Screening Array v2; BIOCARD: Illumina OmniExpress; BLSA: Illumina HumanOmni2.5 BeadChip, Illumina HumanOmniExpress BeadChip; NACC: several different arrays were used to collect genetic data – acquisition of all genetic data is outlined on the NACC website [ https://naccdata.org/nacc-collaborations/partnerships ]; ROSMAP: Global Screening Array-24 v3.0 BeadChip, Affymetrix GeneChip 6.0, Illumina HumanOmniExpress; VMAP: Illumina HumanOmniExpress; WRAP: Illumina Human610, Illumina OmniExpress). All genetic raw data underwent the same robust quality control and imputation pipelines. 45 Variants that had a genotyping rate less than 95%, a minor allele frequency (MAF) less than 1% or deviated from Hardy-Weinberg Equilibrium ( p 1% of variants), if cryptic relatedness was present (PIHAT > 0.25) or if the reported and genotypic sex were not concordant. Imputation was performed on the University of Michigan Imputation Server using the TOPMed reference panel (hg38) with SHAPEIT phasing. 46 Data were filtered to exclude variants with low imputation quality (R 2 < 0.08), duplicated/multi-allelic variants, and MAF < 1%. Principal components analysis was conducted, and genetic ancestry outliers were excluded. 8 pairs of individuals were related across cohorts and were subsequently removed from the respective cohort with more individuals, namely BLSA, NACC, and ADNI. Statistical analyses SNP-heritability tests Single nucleotide polymorphism (SNP) heritability was estimated for each FW-corrected dMRI metric using Genome-Wide Complex Trait Analysis (GCTA). This method uses restricted maximum likelihood and genetic relatedness matrices to calculate heritability estimates. 47 To account for multiple comparisons, results were adjusted across phenotypes using the False Discovery Rate (FDR) procedure. 48 Genome-wide association testing and meta-analysis GWAS in self-reported non-Hispanic White participants were conducted separately for each cohort (N = 7) for each dMRI metric (N = 5) of each of the limbic WM tracts (N = 7) using PLINK software (Version 1.9, https://www.cog-genomics.org/plink/1.9 ). All models included covariates for age, sex and the first three genetic ancestry principal components. Next, we performed a meta-analysis across cohorts for each dMRI metric using Genome-Wide Association Meta-Analysis (GWAMA). 49 The significance threshold was set a priori to p < 5×10 −8 . We evaluated suggestive loci which had a p < 1×10 −5 . Reported genome-wide associations were filtered to include those present in at least 6 out of 7 cohorts. eQTL analyses, replication, and databases Expression quantitative trait locus (eQTL) analyses were conducted on variants reaching genome-wide significance using the Genotype-Tissue Expression (GTEx) portal ( https://gtexportal.org ). Furthermore, genome-wide significant variants were validated using the Oxford Brain Imaging Genetics Server (BIG40) ( https://open.win.ox.ac.uk/ukbiobank/big40/ ) 50 and ENIGMA-VIS ( https://enigma-brain.org/ ). 51 The genes identified were further explored using several databases including GeneCards ( https://www.genecards.org ), Agora ( https://agora.adknowledgeportal.org ), and Open Targets ( https://www.opentargets.org ). A literature search was also conducted using PubMed and Web of Science to provide further context and insights into the identified genes. Association analysis with cognitive outcomes and AD pathologies using ROSMAP bulk RNA sequencing data Processed bulk RNAseq data in the ROSMAP cohort from three brain tissues ( Table S5a ), including the dorsolateral prefrontal cortex (DLPFC), the posterior cingulate cortex (PCC), and the head of the caudate nucleus (CN), 52 were used to fit linear regression models (for cross-sectional outcomes) and linear mixed-effect models (for longitudinal outcomes) to test associations between cognitive function and AD pathologies with expression of the genes closest to the identified genome-wide significant variants in the meta-analyzed GWAS. Overall cognitive function was represented using a global cognitive score which was derived by converting raw scores from 19 cognitive tests to z-scores and subsequently averaged. For cross-sectional cognitive function, the global cognitive score at last visit before death was used. For the longitudinal model, cognitive trajectory was quantified in a mixed effects regression model. Cognitive trajectory was estimated as the individual-specific annual rate of change in global cognition. AD pathologies included amyloid-β load (immunohistochemistry staining), neurofibrillary tangles (silver staining), tau tangle (immunohistochemistry staining), and neuritic plaque pathology (silver staining) ( https://www.radc.rush.edu/docs/var/overview.htm?category=Pathology ). For all cross-sectional outcomes, covariates included age at death, sex, postmortem interval (PMI), and interval between last visit and death. For the longitudinal models, in addition to the same set of covariates, time was modeled as the number of years between each visit and the final visit. To account for multiple comparisons, results were adjusted for each phenotype using the FDR correction. 48 Gene- and pathway-level analysis The Multimarker Analysis of GenoMic Annotation (MAGMA v1.09) software was used to conduct gene- and pathway-level analyses. 53 Results were adjusted for each phenotype using FDR correction. 48 Genetic covariance analysis and local genetic covariance analysis The meta-analysis results for all dMRI metrics were used to perform genetic covariance analyses with the GWAS summary statistics of 65 complex traits ( Table S9a ) as well as 3,143 brain traits 54 derived from the UK Biobank using the Genetic Covariance Analyzer (GNOVA) software. 55 To further investigate local genetic covariance for significant associations, we used the Super Genetic Covariance Analyzer (SUPERGNOVA) software. 56 To account for multiple comparisons, results were adjusted across dMRI metrics and traits using the FDR procedure. 48 Transcriptome-wide association testing By leveraging predictive models, transcriptome-wide association studies (TWAS) increase the power to detect associations with WM microstructure that might have been missed with the GWAS approach. S-PrediXcan was used to examine associations between genetically predicted gene expression and dMRI metrics, leveraging precomputed predictive gene expression models from all GTEx tissues. 57 To account for multiple comparisons, results were adjusted for each phenotype across genes and tissues using the FDR procedure. 48 Results Heritability of limbic WM microstructure 15 of 35 FW-corrected dMRI metrics across all limbic tracts were heritable with SNP-heritability estimates ranging from 0.26 to 0.60 ( p FDR < 0.05, Figure 1 , Table S3 ). Specifically, all dMRI metrics of the cingulum as well as 4 out of 5 metrics of the fornix and ILF showed significant heritability. Download figure Open in new tab FIGURE 1. SNP-heritability estimates for limbic WM microstructure. SNP heritability of 35 FW-corrected dMRI metrics from 7 WM tracts in the limbic system. Abbreviations: AxD, axial diffusivity; dMRI, diffusion magnetic resonance imaging; FA, fractional anisotropy; FW corr , free water corrected; MD, mean diffusivity; RD, radial diffusivity; WM, white matter. Genome-wide significant signals in proximity to the genes CDH19, KC6, SENP5, RORA, FAM107B , and MIR548A1 were associated with limbic WM microstructure A cross-sectional GWAS for each dMRI metric of each tract was performed using linear regression models covarying for sex, age, and the first three ancestral principal components, followed by a meta-analysis across cohorts ( Table S4a for meta-analysis summary statistics [ p < 0.05]). Figure 2 presents an ideogram illustrating the 500 most significant genomic signals based on smallest p-value associated with WM microstructure. In total, 6 genome-wide significant loci for different tract-by-microstructure combinations were discovered. We identified a locus with 38 genome-wide significant ( p < 5×10 −8 ) SNPs ( Figure 3 , lead SNP: rs12959877, EAF = 0.44, β = 0.002 ± 3.18×10 −4 , p = 5.78×10 −9 , intronic CDH19 ) for MTG RD FWcorr , with additional suggestive associations ( p < 1×10 −5 ) at this site for RD FWcorr of STG and ITG. When evaluating eQTL (GTEx Portal) evidence, we found that all 38 SNPs were eQTLs for the gene CDH19 in lung and spleen tissue ( Table S4b ). This gene is highly expressed in oligodendrocytes and functions as a calcium-dependent cell adhesion glycoprotein. 58 Additional genome-wide significant signals were found in proximity to the genes KC6, SENP5, RORA, FAM107B , and MIR548A1 ( Table 2 ). View this table: View inline View popup Download powerpoint TABLE 2. Statistics for genome-wide significant SNPs Download figure Open in new tab FIGURE 2. Ideogram of genomic signals associated with WM microstructure. Ideogram of selected genomic regions associated with dMRI metrics. Shape codes for the p-value threshold ( p < 5×10 −8 ; p < 1×10 −5 ), color codes for the WM tract. The 500 associations between genetic variants and dMRI metrics with the smallest p-value are represented in this plot. This figure was created using PhenoGram from Richie Lab Visualizations ( https://visualization.ritchielab.org/phenograms/plot ). Abbreviations: AxD, axial diffusivity; FA, fractional anisotropy; FW, free water; ILF, inferior longitudinal fasciculus; ITG, inferior temporal gyrus transcallosal tract; MD, mean diffusivity; MTG, middle temporal gyrus transcallosal tract; RD, radial diffusivity; STG, superior temporal gyrus transcallosal tract; UF, uncinate fasciculus. Download figure Open in new tab FIGURE 3. Genome-wide significant locus near CDH19 associated with MTG RD Fwcorr . a . Location of the 7 limbic WM in the brain, with the MTG colored in light green. b . Manhattan plot of GWAS results for MTG RD FWcorr . The red line indicates genome-wide significance ( p < 5×10 −8 ). The blue line indicates suggestive significance ( p < 1×10 −5 ). c . Forest plot for the effect of variant rs12959877 for each cohort. d . LocusZoom plot for variant rs12959877 on chromosome 18. Abbreviations: ADNI, Alzheimer’s Disease Neuroimaging Initiative; BIOCARD, Predictors of Cognitive Decline Among Normal Individuals; BLSA, Baltimore Longitudinal Study of Aging; chr, chromosome; GWAS, genome-wide association study; MAP, Memory and Aging Project; MTG, middle temporal gyrus transcallosal tract; NACC, National Alzheimer’s Coordinating Center; VMAP, Vanderbilt Memory and Aging Project; ROS, Religious Orders Study; WRAP, Wisconsin Registry for Alzheimer’s Prevention. We validated the identified variants in the Oxford Brain Imaging Genetics Server (BIG40) 50 and ENIGMA-VIS. 51 All of our genome-wide variants were associated with various brain traits such as WM tracts, resting-state functional MRI measures, cortical thickness, and regional and tissue volume ( p < 0.05, Table S4c ). Significant associations between gene expression of RORA, FAM107B , and KC6 with cognitive function and AD pathology To evaluate the AD relevance of the genes identified in the GWAS analysis, we investigated the associations of CDH19, KC6, SENP5, RORA, FAM107B , and MIR548A1 expression profiles in the PCC, DLPFC, and CN with cognitive measures (cross-sectional and longitudinal cognition) and AD pathologies (tau tangles, neuritic plaques, amyloid-β, neurofibrillary tangles). Significant associations were observed for RORA, FAM107B , and KC6 ( Figure 4 , Figure S1 ). Associations for RORA expression included cross-sectional cognition in CN (β = -0.654 ± 0.173; p FDR = 0.011) and DLPFC (β = -0.570 ± 0.145; p FDR = 0.001), longitudinal cognition in CN (β = -0.082 ± 0.016; p FDR = 4.39×10 −5 ) and DLPFC (β = -0.570 ± 0.014; p FDR = 1.31×10 −3 ). Significant AD pathologies for RORA included tau tangles in CN (β = 0.750944 ± 0.204; p FDR = 0.010) and DLPFC (β = 0.756 ± 0.165; p FDR = 7.95×10 −5 ), neurite plaques in CN (β = 0.294 ± 0.081; p FDR = 0.010) and DLPFC (β = 0.165 ± 0.066; p FDR = 0.049), neurofibrillary tangles in DLPFC (β = 0.194 ± 0.051; p FDR = 0.002) and amyloid-β in DLPFC (β = 0.515 ± 0.143; p FDR = 0.003). Download figure Open in new tab FIGURE 4. Gene expression profiles in brain tissues of genes identified through GWAS. a . Heatmap displaying t-statistics for associations between gene expression profiles in three brain tissues for GWAS-identified genes ( RORA, SENP5, KCNK6, CDH19, FAM107B , and MIR548A1 ) with cognitive outcomes and AD pathologies. Only genes with at least one significant association ( p FDR < 0.05) with a cognitive or AD outcome are included. b.–c . Exemplary volcano plots showing the association of FAM107B expression in PCC with b . longitudinal cognitive performance and c . neuritic plaque burden. Significance thresholds are indicated by color. All volcano plots can be found in Figure S1 . Abbreviations: AD, Alzheimer’s Disease; CN, caudate nucleus; DLPFC, dorsolateral prefrontal cortex; FDR, false discovery rate; GWAS, genome-wide association study; PCC, posterior cingulate cortex. For FAM107B gene expression, there were significant association with cross-sectional cognition in DLPFC (β = -0.242 ± 0.066; p FDR = 0.003) and PCC (β = -0.259 ± 0.081; p FDR = 0.015), longitudinal cognition in DLPFC (β = -0.038 ± 0.006; p FDR = 2.21×10 −8 ) and PCC (β = -0.025 ± 0.007; p FDR = 0.005). AD pathologies included tau tangles in DLPFC (β = 0.263 ± 0.075; p FDR = 0.003), neuritic plaques in DLPFC (β = 0.130 ± 0.030; p FDR = 3.43×10 −4 ) and PCC (β = 0.119 ± 0.038; p FDR = 0.018), neurofibrillary tangles in DLPFC (β = 0.102 ± 0.023; p FDR = 2.55×10 −4 ) and amyloid-β in DLPFC (β = 0.325 ± 0.065; p FDR = 3.98×10 −5 ). Furthermore, KC6 expression in CN was associated with neurite plaques (β = -0.055 ± 0.017; p FDR = 0.019). All statistical analyses can be found in Table S5b . Gene- and pathway-level analyses highlight biological mechanisms related to immune function, neurotrophic signaling, and cardiovascular traits The genetic architecture of WM microstructure was also investigated at the gene and pathway level. Gene-level analyses revealed a significant association between cingulum AxD FWcorr and the gene SERPINA12 (z = 4.60, p FDR = 0.038, N SNPs = 132) which is known to influence obesity and atherosclerosis by modulating glycolipid metabolism. 59 – 61 Table S6 depicts the statistics for all gene analyses. Pathway analysis identified significant pathways ( p FDR < 0.05) for ITG FA FWcorr , STG FA FWcorr , and UF AxD FWcorr . Enriched pathways for ITG FA FWcorr were related to immune function, encompassing isotype switching of B cells, B cell-mediated immunity, and the production of immunoglobulins and antibodies. Beyond immune-related pathways, STG FA FWcorr was associated with several neurotrophic signaling pathways involving NTRK3 and NTRK2 via the RAS pathway, which are crucial for nervous system development and survival. This tract was also linked to insulin-like growth factor 1 receptor (IGF1R) signaling and insulin receptor binding pathways. Furthermore, STG FA FWcorr showed associations with cancer mechanisms, including mutant forms and internalization of the epidermal growth factor receptor (EGFR). It also included pathways related to the overexpression of the human epidermal growth factor receptor 2 (HER2) and signaling events mediated by the EphA2 receptor. For UF AxD FWcorr , enriched pathways included photoreceptor cell outer segment organization and the binding of the peptide hormone angiotensin to its receptor. Statistics for all pathway analysis can be found in Table S7 . Genetic covariance between WM microstructure with cardiovascular, lipid, inflammatory, and neurological traits To investigate the extent of shared genetic factors between WM microstructure and complex traits (N = 65), we performed genetic covariance analyses using GNOVA. Traits that exhibited at least one FDR-corrected significant association with one dMRI metric are displayed in Figure 5a . HDL cholesterol demonstrated multiple significant associations with WM microstructure. Specifically, we observed negative covariance with FA FWcorr and AxD FWcorr , alongside positive covariance with MD FWcorr , RD FWcorr , and FW. When examining the local genetic covariance for HDL cholesterol with MD FWcorr and RD FWcorr , multiple distributed genomic regions exhibiting significant covariation were identified ( Figure 5b , Table S8 ). Download figure Open in new tab FIGURE 5. Genetic covariance between complex traits and WM microstructure. a . Genetic covariance between dMRI metrics and complex traits which have shown at least one association with a dMRI metric ( p FDR < 0.05). “*” marks a p FDR < 0.05. b . Local genetic covariance between HDL cholesterol with MD FWcorr and RD FWcorr . Only genomic regions with a p < 0.05 were included. Abbreviations: AxD, axial diffusivity; FA, fractional anisotropy; FDR, false discovery rate; FW, free water; ILF, inferior longitudinal fasciculus; ITG, inferior temporal gyrus transcallosal tract; MD, mean diffusivity; MTG, middle temporal gyrus transcallosal tract; RD, radial diffusivity; STG, superior temporal gyrus transcallosal tract; UF, uncinate fasciculus. Resting heart rate variability traits (SDNN, RMSSD, pvRSAHF) and triglyceride showed negative associations with MD FWcorr , RD FWcorr , and FW. For metabolic traits, type 2 diabetes was positively associated with fornix FA FWcorr and AxD FWcorr , whereas hydroxyvitamin D demonstrated several positive associations with MD FWcorr and RD FWcorr . Moreover, multiple genetic associations between WM microstructure were identified with immune-related diseases, including ulcerative colitis, rheumatoid arthritis, primary sclerosing cholangitis, primary biliary cirrhosis, inflammatory bowel disease, eczema, celiac disease, and asthma. Neurological and psychiatric traits also showed significant associations, encompassing schizophrenia, neuroticism, ischemic stroke, frontotemporal dementia, epilepsy, autism spectrum disorder, anxiety disorder, and amyotrophic lateral sclerosis. Behavioral traits such as subjective well-being, smoking initiation, smoking cessation, cigarettes per day, cannabis dependence, sleep duration, risky behavior, loneliness, internalizing problems, educational attainment, antisocial behavior, aggressive behavior, and age of initiation were linked to WM microstructure. Statistics for all computed tests can be found in Table S9b . Most of the genetic covariance results remained significant and showed similar effect directions after removing the APOE region from the genome ( Figure S2 , Table S10 ). Genetic covariance analysis between FW-corrected dMRI metrics and 3,143 neuroimaging-derived phenotypes from the UK Biobank identified 9,218 significant associations after FDR correction out of 110,005 tests performed ( Table S11 ). Figure S3 highlights the neuroimaging-derived phenotypes with at least 10 significant associations across the FW-corrected dMRI metrics. Notably, substantial genetic overlap was observed with the UK Biobank dMRI measures (e.g., mode of the arcuate fasciculus, FA CONV of the posterior corona radiata) and resting-state network metrics (e.g., NET100_0452, NET100_1425). TWAS identified an association between STG RD FWcorr and DNAJB14 The summary statistics derived from the meta-analyzed GWAS of WM microstructure were used to compute TWAS using S-PrediXcan. A significant association between STG RD FWcorr and the gene DNAJB14 in colon transverse tissue (z = -5.47, p FDR = 0.016, N SNPs = 27) was identified. DNAJB14 is involved in chaperone cofactor-dependent protein refolding and protein-containing complex assembly. 62 Results ( p < 0.05) for all analyses can be found in Table S12 . Discussion This study investigated the genetic architecture of limbic WM microstructure in a large, harmonized sample of older adults enriched for cognitive impairment, utilizing advanced FW-corrected dMRI metrics. We observed substantial heritability of limbic WM microstructure, especially within tracts such as the cingulum, fornix and ILF. Our meta-analyzed GWAS identified several genetic loci associated with WM characteristics, implicating the genes CDH19, KC6, SENP5, RORA, FAM107B , and MIR548A1 . Furthermore, we identified gene-level association of WM microstructure with DNAJB14 using TWAS and with SERPINA12 using gene-level analysis. Pathway and genetic covariance analyses revealed significant links between limbic WM microstructure and biological processes related to inflammation, vascular health, lipid metabolism, and various neurological conditions. The strongest GWAS signal identified was a locus including 38 SNPs on chromosome 18, with most SNPs acting as eQTLs for CDH19 . This gene, also known as Cadherin 19, encodes a calcium-dependent cell-adhesion glycoprotein highly expressed in oligodendrocytes and Schwann cells, which are crucial for myelin formation and maintenance. 58 This finding suggests a link between genetic variation influencing myelin-related cell adhesion processes and WM microstructural integrity in aging. Another notable association was found near SENP5 on chromosome 3 with fornix AxD FWcorr . SENP5 regulates SUMOylation, a post-translational modification critical for gene expression, DNA repair, and mitochondrial dynamics, and has been implicated in synaptogenesis and synaptic function in mature neurons. 63 , 64 Several other GWAS-identified genes, including RORA, FAM107B and KC6 , showed significant association with their brain tissue expression levels, cognitive decline and AD pathologies (tau tangles, neurite plaques, neurofibrillary tangles and amyloid-β plaques) in our bulk RNA-seq analyses. RORA encodes a protein that binds to hormone response elements upstream of multiple genes to enhance their expression. It regulates key genes involved in neurological functions and is implicated in autism spectrum disorders, highlighting its role in neuronal differentiation and synaptic plasticity. 65 As a nuclear receptor transcription factor, RORA interacts with transcriptional regulators like insulin and brain-derived neurotrophic factors, both associated with AD. 66 Additionally, RORA supports neuronal survival and has a neuroprotective role in Parkinson’s Disease by protecting neurons from oxidative stress. 67 FAM107B is widely expressed in the brain and associated with various brain structural features, including cortical thickness, subcortical volume, and overall brain morphology. 68 KC6 is a less well-characterized RNA gene and has been associated with corneal biology and diseases. 69 This evidence indicates that some of the identified genes are involved in general brain health, which the observed genetic associations between WM microstructure and neurological and psychiatric conditions such as multiple sclerosis, aggressive behavior, and internalizing problems further supported. Our study strongly implicates inflammatory, lipid metabolism, and vascular mechanisms in limbic WM health. RORA is also involved in regulating cholesterol and glucose metabolism and blood vessel morphogenesis. 66 SENP5 , in addition to its importance for brain health, has a critical role in cardiac function by regulating mitochondrial balance. Excessive de-SUMOylation by SENP5 , which is upregulated in human heart failure, has been linked to cardiomyopathies and cardiac dysfunction. 70 , 71 Our post-GWAS analyses supported associations between WM microstructure with lipid and vascular mechanisms, revealing gene-level associations for SERPINA12 and links to insulin and inflammation pathways. Notably, SERPINA12 is elevated in individuals with obesity and is associated with obesity-linked metabolic traits, such as insulin resistance, impaired glycemic control, and cardiovascular disease. 72 The connection between WM with lipid and vascular mechanisms was also demonstrated by genetic overlap between WM microstructure and HDL cholesterol, triglycerides, resting heart rate variability traits, ischemic stroke, type 2 diabetes, and several inflammatory diseases. These findings suggest that cardiovascular risk and associated systemic inflammation may provide a pathophysiological basis for WM alterations observed in later life, which can in turn contribute to risk for neurodegenerative diseases and cognitive decline. Vascular conditions such as hypertension, atherosclerosis, and type 2 diabetes may compromise vascular integrity, leading to reduced blood flow or microvascular damage in the brain. Such vascular challenges adversely affect WM, potentially resulting in axonal and myelin damage. This highlights the importance of intact energy metabolism and the cardiovascular system in brain health. Further research is needed to disentangle signals related to vascular mechanisms from AD specific pathologies. A key strength of this study is the investigation of genetic influence on WM microstructure within well-characterized cohorts of older individuals, including those with cognitive impairment, which enhances our ability to uncover biological mechanisms relevant to aging and in neurodegenerative diseases. Unlike large-scale studies such as UK Biobank and ENIGMA that have predominantly focused on WM in midlife, our study specifically addresses the genetic architecture of WM degeneration in the context of aging and AD risk. Additionally, the use of FW-corrected dMRI metrics provides a more accurate quantification of WM microstructure by mitigating confounds from extracellular free water. This study has several limitations. Our analyses were conducted on non-Hispanic White individuals, which, while reducing confounding from population stratification, limits the generalizability of our findings to more diverse populations. Future research should aim to replicate these findings in larger, multi-ethnic cohorts. The cross-sectional and correlational nature of our analyses precludes causal inferences. While the use of single-shell dMRI data allowed for the inclusion of a large, harmonized dataset, future studies employing multi-shell dMRI approaches, such as NODDI, 73 could provide more detailed insights into distinct tissue compartments and potentially quantify aspects like neuroinflammation. Finally, only a small subset of our cohort (5%) had a formal clinical diagnosis of AD at the time of imaging. Enriching future cohorts with individuals across the AD spectrum may enhance the detection of neurodegeneration-specific genetic signals. Conclusion This imaging genetics study identified several novel genes linked to limbic WM microstructure in multiple, harmonized aging cohorts. Notably, bulk RNA-seq analyses demonstrated that some of these genes were associated with cognitive decline and amyloid-β/tau burden, suggesting AD relevance. Furthermore, our findings revealed that the genetic architecture of limbic WM is strongly tied to vascular health and inflammation, highlighting these pathways as promising avenues for future therapeutic development Data availability Data from ADNI, NACC, ROSMAP, and WRAP is available on NIAGADS ( https://dss.niagads.org/ ). For BIOCARD ( https://biocard.pathology.jhu.edu/resources-for-researchers/ ), BLSA ( https://www.blsa.nih.gov/ ), and VMAP ( https://vmacdata.org/vmap ), data use can be approved through each cohort’s website. The WM GWAS summary statistics generated in this study will be made available on NIAGADS. Funding Acknowledgments This study was supported by several funding sources, including K01-EB032898 (KGS), R01-EB017230 (BAL) K01-AG073584 (DBA), U24-AG074855 (TJH), 75N95D22P00141 (TJH), R01-AG059716 (TJH), UL1-TR000445 and UL1-TR002243 (Vanderbilt Clinical Translational Science Award), S10-OD023680 (Vanderbilt’s High-Performance Computer Cluster for Biomedical Research). The research was support in part by the Intramural Research Program of the National Institutes of Health, National Institute on Aging. Study data were obtained from the Vanderbilt Memory and Aging Project (VMAP). VMAP data were collected by Vanderbilt Memory and Alzheimer’s Center Investigators at Vanderbilt University Medical Center. This work was supported by NIA grants R01-AG034962 (PI: Jefferson), R01-AG056534 (PI: Jefferson), U19-AG03655 (PI:Albert) and Alzheimer’s Association IIRG-08-88733 (PI: Jefferson). The data contributed from the Wisconsin Registry for Alzheimer’s Prevention was supported by NIA AG021155, AG027161, AG037639, and AG054047. The BLSA is supported by the Intramural Research Program of the National Institutes of Health, National Institute on Aging. This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Institute on Aging. Data collection and sharing for this project was funded (in part) by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data contributed from MAP/ROS was supported by NIA R01AG017917, P30AG10161, P30AG072975, R01AG056405, UH2NS100599, UH3NS100599, R01AG064233, R01AG015819 and R01AG067482, and the Illinois Department of Public Health (Alzheimer’s Disease Research Fund). Data can be accessed at www.radc.rush.edu . The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADCs : P50 AG005131 (PI James Brewer, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG005138 (PI Mary Sano, PhD), P50 AG005142 (PI Helena Chui, MD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005681 (PI John Morris, MD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG008051 (PI Thomas Wisniewski, MD), P50 AG008702 (PI Scott Small, MD), P30 AG010124 (PI John Trojanowski, MD, PhD), P30 AG010129 (PI Charles DeCarli, MD), P30 AG010133 (PI Andrew Saykin, PsyD), P30 AG072975 (PI Julie Schneider, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG013846 (PI Neil Kowall, MD), P30 AG013854 (PI Robert Vassar, PhD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P30 AG019610 (PI Eric Reiman, MD), P50 AG023501 (PI Bruce Miller, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P30-AG072946 (PI Linda Van Eldik, PhD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P30 AG035982 (PI Russell Swerdlow, MD), P50 AG047266 (PI Todd Golde, MD, PhD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG049638 (PI Suzanne Craft, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Marwan Sabbagh, MD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD). NACC data can be accessed at naccdata.org. Conflict of Interest Statement SCJ has served on advisory boards for Enigma Biomedical and ALZPath in the past two years. AJS receives support from multiple NIH grants (P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, U19 AG024904, R01 LM013463, R01 AG068193, T32 AG071444, U01 AG068057, U01 AG072177, U19 AG074879, and U24 AG074855). He has also received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in kind contribution of PET tracer precursor) and participated in Scientific Advisory Boards (Bayer Oncology, Eisai, Novo Nordisk, and Siemens Medical Solutions USA, Inc) and an Observational Study Monitoring Board (MESA, NIH NHLBI), as well as External Advisory Committees for multiple NIA grants. He also serves as Editor-in-Chief of Brain Imaging and Behavior, a Springer-Nature Journal. Consent Statement All participants provided informed consent in their respective cohort studies. SUPPLEMENTARY FIGURES Download figure Open in new tab FIGURE S1. Gene expression profiles in brain tissues of genes identified through GWAS. Volcano plots of gene expression profiles in brain tissues for the genes RORA, SENP5, KC6, CDH19, FAM107B , and MIR548A1 identified through GWAS associated with cognitive outcomes and AD pathologies. Significance thresholds are indicated by color. Highlighted genes with a p < 0.05 are labeled. Abbreviations: AD, Alzheimer’s Disease; CN, caudate nucleus; DLPFC, dorsolateral prefrontal cortex; GWAS, genome-wide association study; FDR, false discovery rate; PCC, posterior cingulate cortex. Download figure Open in new tab FIGURE S2. Genetic covariance between complex traits and WM microstructure when the APOE region is removed from the genome. Genetic covariance between dMRI metrics (x-axis) and complex traits and diseases (y-axis) when the APOE region is removed from the genome. The traits included have shown at least one FDR-significant association with a dMRI metric. “*” marks genetic covariance with a p FDR < 0.05. Abbreviations: AxD, axial diffusivity; FA, fractional anisotropy; FDR, false discovery rate; FW, free water; ILF, inferior longitudinal fasciculus; ITG, inferior temporal gyrus transcallosal tract; MD, mean diffusivity; MTG, middle temporal gyrus transcallosal tract; RD, radial diffusivity; STG, superior temporal gyrus transcallosal tract; UF, uncinate fasciculus. Download figure Open in new tab FIGURE S3. Genetic covariance between brain traits and WM microstructure. Genetic covariance between dMRI metrics (x-axis) and brain traits (y-axis). The traits included have shown at least ten FDR-significant association with a dMRI metric. “*” marks genetic covariance with a p FDR < 0.05. The y-axis is clustered using Euclidian distance. 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Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Genetic architecture of the limbic white matter microstructure in aging and Alzheimer’s Disease Anna Lorenz , Aditi Sathe , Yisu Yang , Alaina Durant , Yiyang Wu , Michael E. Kim , Chenyu Gao , Nancy R. Newlin , Karthik Ramadass , Praitayini Kanakaraj , Nazirah Mohd Khairi , Zhiyuan Li , Tianyuan Yao , Yuankai Huo , Logan Dumitrescu , Niranjana Shashikumar , Kimberly R. Pechman , Shannon L. Risacher , Lori L. Beason-Held , Yang An , Konstantinos Arfanakis , Guray Erus , Christos Davatzikos , Mohamad Habes , Di Wang , Duygu Tosun , Arthur W. Toga , Paul M. Thompson , Elizabeth C. Mormino , Panpan Zhang , Kurt Schilling , Alzheimer’s Disease Neuroimaging Initiative (ADNI) , The BIOCARD Study Team , The Alzheimer’s Disease Sequencing Project (ADSP) , Marilyn Albert , Walter Kukull , Sarah A. Biber , Bennett A. Landman , Sterling C. Johnson , Barbara Bendlin , Julie Schneider , David A. Bennett , Angela L. Jefferson , Susan M. Resnick , Andrew J. Saykin , Timothy J. Hohman , Derek B. Archer medRxiv 2025.05.19.25327915; doi: https://doi.org/10.1101/2025.05.19.25327915 Share This Article: Copy Citation Tools Genetic architecture of the limbic white matter microstructure in aging and Alzheimer’s Disease Anna Lorenz , Aditi Sathe , Yisu Yang , Alaina Durant , Yiyang Wu , Michael E. Kim , Chenyu Gao , Nancy R. Newlin , Karthik Ramadass , Praitayini Kanakaraj , Nazirah Mohd Khairi , Zhiyuan Li , Tianyuan Yao , Yuankai Huo , Logan Dumitrescu , Niranjana Shashikumar , Kimberly R. Pechman , Shannon L. Risacher , Lori L. Beason-Held , Yang An , Konstantinos Arfanakis , Guray Erus , Christos Davatzikos , Mohamad Habes , Di Wang , Duygu Tosun , Arthur W. Toga , Paul M. Thompson , Elizabeth C. 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