Causal Relationship between White Matter Tracts and Psychiatric Disorders: A Mendelian Randomization Study

Causal Relationship between White Matter Tracts and Psychiatric Disorders: A Mendelian Randomization Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Causal Relationship between White Matter Tracts and Psychiatric Disorders: A Mendelian Randomization Study Kai Yuan, Yifan Yu, Sizhi Ai, Tianye Jia, Lin Xiao, Yanping Bao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6505046/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract White matter tracts (WMTs), which are responsible for information transmission in the brain, are closely associated with the onset, development, and prognosis of psychiatric disorders, yet the underlying causal mechanisms of their associations remain largely unclear. Thus, we employed two-sample bidirectional Mendelian randomization (MR) to explore the causality between WMTs and 10 psychiatric disorders. The sample sizes of summary-level datasets were ranged from 14,307 to 1,222,882. We found that changes in WMTs are associated with the risk of 8 types of psychiatric disorders, one standard deviation change in WMTs can increase or reduce the risk of psychiatric disorders by 2.2–71.4%. In the reverse MR analysis, we discovered that alcohol use disorder also increases the probability of specific WMT abnormalities. Our study provides novel insights into the potential causal association between WMTs and psychiatric disorders, indicating that specific characteristics of WMTs may serve as potential biomarkers for psychiatric disorders. Biological sciences/Computational biology and bioinformatics/Genome informatics Health sciences/Risk factors Health sciences/Diseases/Psychiatric disorders Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Psychiatric disorders impose a substantial burden on global health, as evidenced by the Global Burden of Diseases (GBD) 2021 1 , which highlights 8 psychiatric disorders among the top 25 disease burdens. Recently, a large body of literature has shown that white matter tracts (WMTs), which are responsible for information transmission, are closely related to the onset, development, and prognosis of psychiatric disorders 2 – 5 . Researchers often utilize diffusion tensor imaging (DTI) sequence of magnetic resonance imaging (MRI) to visualize WMTs and assess their integrity 6 , 7 , as well as measure potential damage to axons 8 , myelin 8 , and cell membranes 8 . These functional changes in DTI 9 , 10 are characterized by five parameters: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and mode of anisotropy (MO). Different WMTs exhibit variations in both structure and function. For readers seeking further explanations, please refer to Table 1 . Table 1 The glossary of white matter tracts. White matter tract Type Description CST Brainstem tract This structure can be identified at medulla and the pons level, but should also contain corticopontine and corticobular tracts. This tract is a part of the left PLIC 31 . CR Projection tract This structure is divided into three regions: ACR, SCR and PCR. The divisions are made at the middle of the genu and splenium of the corpus callosum, which are arbitrarily chosen and not based on anatomic or functional boundaries. This region includes the thalamic radiations (thalamocortical, corticothalamic fibers) and parts of the long corticofugal pathways, such as the corticospinal, corticopontine, and corticobulbar tracts. The boundary of the CR and the IC is defied at the axial level where the IC and EC merge. ALIC Projection tract The anterior thalamic radiation and fronto-pontine fibers are the major contributors in this region. PLIC Projection tract The superior thalamic radiation and long corticofugal pathways, such as the corticospinal tract and the fronto- and parieto-pontine fibers, are the major constitutes. RLIC Projection tract In this region, the posterior thalamic radiation (corticothalamic and thalamo-cortical fibers, including the optic radiation) is the major constituent, but can also include the parieto-, occipito- and temporopontine fibers. The boundary with SS is arbitrarily defined at the middle of the SCC. SLF Association fiber This tract is located at the dorsolateral regions of the CR and contains connections between the frontal, parietal, occipital, and the temporal lobes including language-related areas (Broca’s, Geschwind’sand Wernicke’s territories.) This tract is associated with working memory performance, attention 49 and language-related 50 functions. SFO Association fiber This tract is located at the superior edge of the ALIC (anterior thalamic radiation) and the boundary is not always clear. Only the frontal region is identifiable and projection to the parietal lobe cannot be segmented. It has been suggested that this tract is a part of the anterior thalamic radiation and not an association fiber. UNC Association fiber This tract connects the frontal lobe (orbital cortex) and the anterior temporal lobe. It can be discretely identified where the two lobes are connected but not within the frontal and the temporal lobes where it merges with other tracts. This tract is associated with language-related 50 function and its damage will lead to inattentive-emotional symptoms and cognitive deficits 51 . IFO/UNC IFO/ILF Association fiber The IFO connects the frontal lobe and the occipital lobe. In the frontal lobe, this partition also includes the frontal projection of the UNC. In the temporal and occipital lobe, the IFO merges with the ILF, which is segmented as a different partition. The IFO is the longest association fiber running medially in the temporal lobe and connects the frontal lobe with the occipital, temporal and superior parietal cortex 31 . And it poses language-related 50 functions and its damage will cause inattention and emotional problems 51 . SS Association fiber The IFO/ILF merges with projection fibers from the RLIC and forms a large, sheet-like, sagittal structure, called the SS. This region, therefore should include both association and projection fibers. The boundary of the SS and the PCR is also arbitrarily defined at the axial level of the SCC. EC Association fiber This region, located lateral to the IC, is believed to contain association fibers, such as the ALF and IFO and commissural fibers. Because of the limited image resolution, the external and extreme capsules are not resolved. CG Association fiber This tract connects the frontal lobe with the amygdala 31 , carrying information from the cingulate gyrus to the hippocampus. The entire pathway from the frontal lobe can be clearly identified. In the WMPM, the CG in the cingulate gyrus and the hippocampal regions is separated at the axial level of the SCC and denoted as CGC and CGH, respectively. It is associated with emotion and fear extinction 31 . FX/ST Association fiber These tracts are both related to the limbic system: the FX to the hippocampus, and the ST to the amygdala. Both tracts project to the septum and the hypothalamus. With current image resolution capabilities, these two tracts cannot be distinguished in the hippocampal area, and both tracts are labeled as FX. The ST can be discretely identified in the amygdala and the dorsal thalamus. BCC Commissural fiber The body of CC. This tract interconnects parietal and temporal cortices 49 , connecting bilateral premotor, primary motor, and primary sensory cortex 52 . Its damage will cause sensory and motor processing abnormalities. GCC Commissural fiber The genus of CC. This tract connects the bilateral frontal cortex and is involved in sensory and visuospatial processing 32 , 33 . Its damage will cause social functioning impairment 32 or disassociative symptoms 31 . SCC Commissural fiber The splenium of CC. This tract receives input from the occipital lobes 49 . The definition and description of white matter tracts were from Mori et al 10 . Abbreviations: ACR: anterior corona radiata; ALIC: anterior limb of internal capsule; ATR: anterior thalamic radiation; BCC: body of corpus callosum; CC: corpus callosum; CGC: cingulum connecting to cingulate gyrus; CGH: cingulum connecting to hippocampus; CR: corona radiata; CST: corticospinal tract; EC: external capsule; FX/ST: fornix and stria terminalis; FX: Fornix (column and body of fornix); GCC: genu of corpus callosum; IC: internal capsule; ICP: inferior cerebellar peduncle; IFO/ILF: inferior fronto-occipital fasciculus/inferior longitudinal fasciculus; IFO/UNC: inferior fronto-occipital fasciculus/uncinate fasciculus; ILF: inferior longitudinal fasciculus; PCR: posterior corona radiata; PLIC: posterior limb of internal capsule; PTR: posterior thalamic radiation (include optic radiation); RLIC: retrolenticular part of internal capsule; SCC: splenium of corpus callosum; SCR: superior corona radiata; SFO: superior fronto-occipital fasciculus; SLF: superior longitudinal fasciculus; SS: Sagittal stratum; STR: superior thalamic radiation; UNC: uncinate fasciculus. Research has indicated that certain characteristics of WMTs could potentially serve as diagnostic markers for psychiatric disorders 11 . For example, patients with major depressive disorder (MDD) who had suffered non-suicidal self-injury showed reduced WMT integrity compared to healthy controls 12 and appeared to have more severe dysfunction of the kynurenine/tryptophan pathway 13 . Compared with MDD, the alterations of WMTs in patients with bipolar disorder (BD) were more evident 14 . However, many studies focus solely on the FA value to represent changes in the properties of WMTs, as summarized in Table S1 , while neglecting the contributions of other parameters such as MD, RD, AD, and MO. In addition, although many studies have shown associations between WMTs and psychiatric disorders, the causality and directions of these associations are still unclear. Therefore, the lack of clarity about the causal association between WMTs and psychiatric disorders, as well as the neglection of other important parameters, has limited the application of DTI techniques in the field of psychiatric disorders 9 . While causal inference in observational studies was commonly confounded by complex factors, such as the medications and environment 15 , mendelian randomization (MR) studies can effectively mitigate these confounding effects by utilizing genetic polymorphisms as instrumental variables (IVs) for exposure 16 . The core idea of MR is to "infer causality between exposure and outcome using genetic variants as instrumental variables, leveraging the free and random assortment of genes and the stability of genotypes against environmental influences to avoid confounding bias in traditional epidemiology effectively." 17 Current MR research has explored the causal association between imaging-derived phenotype and 10 psychiatric disorders 18 , between brain functional networks and 12 psychiatric disorders 19 , between brain structure and Alzheimer's disease (ALZ) 20 , and between cortical structure, white matter microstructure, and neurodegenerative diseases 21 . These studies revealed some causal associations between brain structure and function and neuropsychiatric disorders. However, these studies either fail to uncover the causalities between WMTs and psychiatric disorders. Thus, we conducted bidirectional mendelian randomization (MR) in this study to explore the causal associations between WMTs and psychiatric disorders. Results Overview of MR The research design is illustrated in Fig. 1 . and the baseline characteristics of genome-wide association studies (GWAS) are described in Table 2 . All Mendelian randomization (MR) processes followed the Burgess 22 and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines 23 and the checklist was in Table S2 . In this article, we found causal associations of 11 types of WMTs with 8 psychiatric disorders and found reverse causal associations of AUD with 3 types of WMTs. All the subsequent reported results have passed FDR correction. Among all included GWAS data of psychiatric disorders, only AUD and PTSD included a small portion of UK Biobank data, with the sample overlap being < 5%. The stepwise screening results of IVs and the control of confounding factors and outliers were detailed in Table S3 - Table S2 5 . Overall, we have conducted rigorous IV screening and ensured the control of confounders and outliers. During statistical analysis, the F-values of the IVs in the final results were all > 20, and the achieved statistical power were > 90%. Table 2 Description of all GWAS summary-level data. GWAS summary-level data of White matter tracts Source Parameters Number of tracts Number of DTI traits Sample (Total) Pubmed ID White matter tracts Zhao et al. 34 AD, FA, MD, MO, RD 21 110 33292 34140357 GWAS summary-level data of Psychiatric Disorders (Bidirectional MR) Disease Source Sample (cases) Sample (control) Sample (Total) Ancestry Pubmed ID Alcohol use disorder 113325 639923 753248 EUR 38062264 iPSYCH 3141 18970 22111 EUR MVP 80028 368113 448141 EUR UKB - - - EUR FinnGen 8866 209926 218792 EUR QIMR 10785 10848 21633 EUR PGC 9938 30992 40930 EUR Yale-Penn 3 567 1074 1641 EUR Attention deficit hyperactivity disorder 38691 186843 225534 EUR 36702997 iPSYCH - - - EUR deCODE - - - EUR PGC - - - EUR Autism disorder 18381 27969 46350 EUR 30804558 iPSYCH 13076 22664 35740 EUR PGC 5305 5305 10610 EUR FinnGen - - - EUR Bipolar disorder PGC 41917 371549 413466 EUR 34002096 Cannabis use disorder 42281 843744 886025 EUR 37985822 PGC, deCODE 14522 298941 313463 EUR MVP 22260 423587 445847 EUR iPSYCH 4733 95657 100390 EUR MGB 456 24088 24544 EUR Yale-Penn 3 310 1471 1781 EUR Major depression 184270 529044 713314 EUR 37464041 iPSYCH 29158 38142 67300 EUR FinnGen 28098 228817 256915 EUR MVP 83810 166405 250215 EUR PGC 43204 95680 138884 EUR Opioid Use Disorder MVP, PGC, iPSYCH, FinnGen, Partners Biobank, BioVU, and Yale-Penn 3 15,251 554,186 569437 EUR 35879402 Post-traumatic stress disorder PGC 137136 1085746 1222882 EUR 38637617 Schizophrenia PGC 52017 75889 127906 EUR 35396580 Tourette syndrome PGC 4819 9488 14307 EUR 30818990 GWAS summary-level data of Psychiatric Disorders (Replication) Disease Source Sample (cases) Sample (control) Sample (Total) Ancestry Pubmed ID Alcohol use disorder FinnGen 18695 435038 453733 EUR - Attention deficit hyperactivity disorder FinnGen 3702 445327 449029 EUR - Bipolar disorder FinnGen 8209 394756 402965 EUR - Schizophrenia FinnGen 6933 439144 446077 EUR - All download links were in supplementary materials. Abbreviations: AD: Axial diffusivities; deCODE: deCODE Genetics; DTI: Diffusion tensor imaging; EUR: European; FA: Fractional anisotropy; iPSYCH: The Lundbeck Foundation Integrative Psychiatric Research; MD: Mean diffusivities; MGB: Mass General Brigham Biobank; MVP: the Million Veteran Program; MO: Mode of anisotropy; PGC: Psychiatric Genomics Consortium; QIMR: QIMR Berghofer Medical Research Institute; RD: Radial diuffsivities; UKB: UK Biobank. Causal effects of white matter tracts on ADHD and TS As shown in Fig. 2 and Table S22 , we uncovered causal associations of the column and body of fornix (FX) with ADHD, as well as of the fornix (cres) / stria terminalis (FXST) with TS. Specifically, for ADHD, a one s.d. increase in MO of the FX was associated with a 31·3% increase in the odds of ADHD (OR = 1·313, 95% CI = 1·117 ~ 1·543, P uncorrected < 9·96 × 10 − 4 ). Conversely, for TS, a one s.d. increase in MO of the FXST was linked to a 71·4% decrease in the odds of TS (OR = 0·286, 95% CI = 0·166 ~ 0·493, P uncorrected < 6·61 × 10 − 6 ). Causal effects of white matter tracts on BD and SCZ As shown in Fig. 2 and Table S22 , we identified a causal association of FXST and BD, as well as of FXST, superior corona radiata (SCR), and retrolenticular part of internal capsule (RLIC) with SCZ. We found that a one s.d. increase in MO of the FXST was associated with a 33·4% decrease in odds of BD (OR = 0·666, 95% CI = 0·544 ~ 0·816, P uncorrected < 8·63 × 10 − 5 ). Specifically, one s.d. increase in FA of the FXST was associated with a 37·6% decrease in SCZ risk (OR = 0·724, 95% CI = 0·618 ~ 0·849, P uncorrected < 6·74 × 10 − 5 ). Conversely, one s.d. increase in FA of the SCR led to 16·0% elevation in SCZ risk (OR = 1·160, 95% CI = 1·058 − 1·272, P uncorrected < 1·09 × 10 − 4 ). Additionally, one s.d. increase in MO of RLIC was linked to a 22·5% (OR = 1·225, 95% CI = 1·105-1·357, P uncorrected < 1·55 × 10 − 3 ) increase in odds of SCZ. Causal effects of white matter tracts on MDD, OUD, and AUD As shown in Fig. 2 and Table S22 , we identified causal associations of the genu of corpus callosum (GCC) with MDD and OUD, as well as of the inferior fronto-occipital fasciculus (IFO) with between AUD. For OUD, one s.d. increase in MD and RD of the GCC region was linked to 11·4% (OR = 0·886, 95% CI = 0·824 ~ 0·952, P uncorrected < 9·30 × 10 − 4 ) and 12·7% (OR = 0·873, 95% CI = 0·803 ~ 0·949, P uncorrected < 1·48 × 10 − 3 ) decreases in risk respectively. Similarly, one s.d. increase in MD was linked to 12·2% elevation in MDD risk (OR = 1·122, 95% CI = 1·054 ~ 1·195, P uncorrected < 3·12 × 10 − 4 ). Furthermore, one s.d. increase in AD of the IFO was associated with a 7·9% decrease in AUD risk (OR = 0·921, 95% CI = 0·883 ~ 0·961, P uncorrected < 1·51 × 10 − 4 ). Causal effects of white matter tracts on PTSD As shown in Fig. 2 and Table S22 , we uncovered causal associations between PTSD and 7 WMTs: cingulum connecting to hippocampus (CGH), FXST, posterior corona radiata (PCR), RLIC, superior fronto-occipital fasciculus (SFO), superior longitudinal fasciculus (SLF) and uncinate fasciculus (UNC). one s.d. increase in FA for PCR, SFO, SLF, and UNC were found to decrease the risk of PTSD by 2·2%-3·8% (OR = 0·962 ~ 0·978). Furthermore, one s.d. increases in MD of CGH, SFO and UNC, RD of CGH, FXST, PCR, RLIC and SLF, and AD of PCR contributed to an increased risk of PTSD (OR = 1·017 − 1·083). Lastly, one s.d. increase in MO of the ACR was associated with a 6% decrease in the odds of PTSD (OR = 0·940, 95% CI = 0·895 ~ 0·988). More details please refer to Supplementary Materials. Reverse Mendelian randomization As shown in Fig. 3 . and Table S22 , we identified reverse causal associations of AUD with CGC, body of corpus callosum (BCC) and ACR. Higher risks of AUD was associated with decreased FA (IVW β = -0·246, 95% CI = -0·392~-0·093, P uncorrected < 1·53 × 10 − 3 ) and increased RD (IVW β = 0·259, 95% CI = 0·111 ~ 0·406, P uncorrected < 5·84 × 10 − 4 ) of CGC, increased MD (IVW β = 0·236 95% CI = 0·074 ~ 0·398, P uncorrected < 4·22 × 10 − 3 ) and increased AD (IVW β = 0·229, 95% CI = 0·075 ~ 0·383, P uncorrected < 3·60 × 10 − 3 ) in BCC. Multivariate Mendelian randomization and validation We conducted multivariate MR analysis 24 on SCZ, OUD, and PTSD, and only the association between FXST_RD and PTSD was retained. More details can be found in Table S23 . Subsequently, we partially validated our results using GWAS data for ADHD, AUD, BD, and SCZ from the FinnGen R11 database. The causal association between FX_MO and ADHD in the forward MR analysis was replicated (P < 0·05 in the replication datasets). Although BD did not yield identical replication results, it produced similar results (with different WMT parameters but biologically comparable meanings), which could also be considered successful replication. Mini-review for DTI and psychiatric disorders We have compiled articles published in the past 5 years that explored the relationship between WMTs and psychiatric disorders (32 articles included from 868 search results on PubMed, see Supplementary Text and Table S1 . Our review revealed that the majority of current research focuses on the association between changes in FA (32/32) values of WMTs and psychiatric disorders, while discussions on MD (7/32), AD (3/32), and RD (9/32) parameters were relatively limited. ADHD, ASD, SCZ, BD, MDD, and PTSD were the major psychiatric disorders researchers reported. Additionally, the distribution pattern of WMTs that showed significant associations with psychiatric disorders was regionally dependent, especially in the limbic system, thalamic radiation, and corpus callosum. These studies reveal the close association between WMTs and psychiatric disorders. Discussion To our knowledge, the current study represents the most comprehensive investigation to date of the causal associations between WMTs and psychiatric disorders. As summarized in Fig. 4 ., our findings not only establish forward causal associations between 24 WMTs-psychiatric disorder pairs but also reveal reverse causal associations between AUD and WMTs. These results indicate that WMTs may hold the potential to act as target regions for diagnosis or intervention in psychiatric disorders. Recent MR studies have investigated the causal associations between imaging-derived phenotype (IDP) and psychiatric disorders, encompassing brain functional networks 19 , cortical structures 20 , white matter microstructure 21 , 25 , and combinations of the three 19 . Building upon previous research, we have incorporated additional DTI parameters, expanded the scope to include more psychiatric disorders potentially related to WMTs, and significantly increased the sample sizes of psychiatric disorders. Guo et al. 18 utilized the GWAS data from Smith et al. 26 to validate the causal associations between IDP (which includes WMTs) and 10 psychiatric disorders, uncovering causal associations between WMTs and SCZ, as well as anorexia nervosa. Comparing with Guo et al 18 , we have incorporated additional DTI parameters (AD and RD), and expanded the scope to include more psychiatric disorders potentially related to WMTs (AUD, OUD, CUD, and PD). To minimize the potential bias, we excluded psychiatric disorders with a high risk of producing diagnosis bias based on our clinical experience. For example, general anxious disorder and panic disorders are classified equally as anxious disorders in the database. But classifying them this way makes the different illnesses seem more similar than they really are, which makes it difficult for us to get interpretable results from data. Furthermore, we significantly increased the sample sizes (ADHD: from 53,293 to 225,534; BD: from 51,710 to 413,466; MDD: from 142,646 to 713,314; PTSD: from 146,660 to 1,222,882) of psychiatric disorders. Moreover, we implemented more specialized control measures to overcome limitations inherent in previous confounder and outlier removal methods. As previous GWAS studies had demonstrated, the genetic traits of brain structure were significantly stronger than those of brain functional networks 26 . Therefore, compared to brain networks, utilizing SNPs related to brain structure would be more advantageous in revealing causal associations from a genetic perspective, yielding results with greater generalizability. Although imaging indicators cannot fully represent the actual neurobiological processes occurring, current studies tend to interpret decreases in FA and increases in MD as indicators of white matter myelin integrity damage 6 , 7 or axonal damage 8 . MO designates the type of anisotropy as a continuous measure, indicating differences in diffusion tensor shape ranging from planar (flattened cylinders) to linear (tubes) 7 , which can help to map the direction of the WMTs. When dealing with more complex fiber structures where multiple fiber orientations coexist within a single voxel, techniques such as fiber orientation distribution are employed to measure this complexity. In such cases, MO can be utilized to signify the predominant or mean orientation when numerous fiber directions are identified. Although the association between WMTs and psychiatric disorders varies according to age, gender, medication, population (e.g., twins), disease severity stratification, and imaging control methods, as our mini review shown ( Supplementary Materials ), the preponderance of evidence currently supported that decrease in FA, increase in MD and RD, and alteration of AD were associated with the risk of psychiatric disorders and the severity of progression. Similarly, our MR results supported this tendency and aligned closely with 3 distinct patterns of neuropathology observed in the investigation of white matter fiber tracts in patients 5 : (1) developmental abnormalities in limbic fibers (CGH, FX, and FXST), (2) abnormal maturation in long-range association fibers (IFO), and (3) severe developmental abnormalities and accelerated aging in callosal fibers (GCC and BCC). The first main finding of this study is the causal associations between limbic system-related fibers (FXST or FX) and psychiatric disorders (SCZ, BD, ADHD, and TS). FXST, composed of FX and ST, is a fiber bundle associated with the limbic system 10 : the FX connects to the hippocampus, and the ST connects to the amygdala. Thus, alterations in FXST may contribute to emotional dysregulation 27 , 28 (in SCZ, BD, TS, related to the amygdala) or cognitive impairments 27 , 28 (in ADHD, related to the hippocampus). We speculated that the similar results of ADHD, TS, BD and SCZ are partly coming from the overlap of their similar pathogenesis and could partly explain the similarity of some clinical symptom (such as mood instability). Furthermore, we notice that the significant results of FXST or FX are mostly related to the MO parameter, which potentially suggests that the loss of complexity in WMTs may be the underlying process of psychiatric disorders. In conjunction with the existing evidence of close associations between the limbic cortical regions, limbic fibers and psychiatric disorders 10 , 27 , 28 , we can conclude that both the cortex and WMTs of the limbic system are inextricably linked to the risk of ADHD, TS, BD and SCZ. The second main finding of this study is the results of PTSD and other psychiatric disorders. The results of PTSD suggested that it is an environmentally dependent psychiatric disorder with a relatively weaker association with genetics, which aligned with previous research 29 – 31 . Furthermore, we found a causal association between GCC and MDD, while GCC has connections with the frontal cortex and is involved in sensory and visuospatial processing 32 , 33 , and it is closely related to social functioning impairment and dissociative symptoms 31 . Besides, the associations between WMTs and AUD in forward MR and reverse MR indicated that the substance itself (alcohol) is a significant risk factor in the occurrence of substance abuse disorders, but genetic factors are also influential. This study had several limitations. Firstly, some overlap (< 5%) was unavoidable in the samples. Secondly, we could not assess the impact of population and diagnostic stratification on the research results due to the use of publicly available GWAS databases. Thirdly, imaging data are indirect evidence, which limits our ability to infer the exact neurobiological processes from them. Fourthly, although we have controlled for both confounding factors and outliers, there could still be omitted factors and also potentially overall controlled factors (which is unnecessary and likely cause false negative). Lastly, caution should be exercised when interpreting the clinical implications of OR values estimated by MR, as it utilizes risk single nucleotide polymorphisms (SNP) of exposure to explore the lifelong impact of exposures on outcomes, rather than the effects of specific interventions over a period of time, and cannot be equated with RCT studies. In summary, our findings suggested potential causal associations between WMTs characteristics and psychiatric disorders through mendelian Randomization, which exhibited both similarities and differences—a pattern contingent upon the clinical feature similarities among different psychiatric disorders. This genetically informed approach suggests that specific WMTs characteristics could serve as hallmarks for psychiatric disorders 11 , providing referential targeted regions for psychiatric disorders (diagnosis or intervention), thereby facilitating future clinical practice and scientific research. Methods Data Sources Data on white matter microstructure were obtained from Zhao et al. 34 . Zhao et al. conducted a genome-wide association study (GWAS) on white matter microstructure using dMRI data from 33,292 individuals in the UK Biobank (UKB) British cohort, reporting a cumulative total of 9,023,710 SNPs (Chromosomes 1–22). The analysis method for white matter microstructure was derived from DTI models using the ENIGMA-DTI pipeline. They analyzed five primary DTI metrics (FA, MD, AD, RD, MO) across 21 brain white matter tracts (WMTs), generating 110 DTI phenotypes for each individual. A detailed description of the GWAS can be found in Table S3 . The GWAS summary statistics are publicly available at Zenodo ( https://zenodo.org/records/4549730 ) , and the results can also be browsed through the BIG-KP knowledge portal ( https://bigkp.org/ ). We sourced data from public databases for psychiatric disorders, prioritizing GWAS-summary-level data when available. To minimize bias in results due to sample overlap between exposure and outcome, as well as ethnic differences, our selection criteria for psychiatric disorder GWAS data were as follows: (1) exclusion of UKB data; (2) inclusion of individuals of European descent (EUR); (3) if UKB data were included, the total sample size had to exceed 732,640 (ensuring a maximum sample overlap rate of < 5%). According to Burgess et al., a 5% sample overlap in MR studies is estimated to introduce a bias of < 0·15% 35 . The GWAS data on 10 psychiatric disorders, including attention deficit hyperactivity disorder (ADHD) 2 , autism spectrum disorder (ASD) 36 , schizophrenia (SCZ) 37 , BD 38 , MDD 39 , post-traumatic stress disorder (PTSD) 40 , alcohol use disorder (AUD) 41 , opioid use disorder (OUD) 42 , cannabis use disorder (CUD) 43 and Tourette syndrome (TS) 44 , were sourced from public databases. The GWAS sample size of psychiatric disorders ranged from 14,307 to 1,222,882. Missing data processing We performed SNP position conversion based on chromosome (CHR) and position (POS) for GWAS data lacking SNP ID using the GRCh37 reference. We matched the missing effective allele frequencies (EAF) using data from the author’s tutorial or the 1000 Genomes Project. If EAF remained missing after this step, we substituted it with EAF = 0·5. Selection of instrumental variables (IVs) MR analysis relies on three crucial assumptions: (1) the IVs should be associated with the exposure; (2) the IVs should be independent of confounding factors; (3) the IVs should only influence the outcome through the exposure directly. These assumptions necessitate a strong correlation between IVs and exposure and independence, and the pleiotropy of IVs does not interfere with the association between the exposure and the outcome 16 . The first two assumptions are primarily ensured through the selection of IVs. We selected SNPs with P < 5 × 10 − 8 to satisfy the strong correlation requirement. To ensure the independence of SNPs, we initially remove linkage disequilibrium (LD) due to close spatial associations, with parameters set as: r 2 = 0·001, window size = 1,000 kb, gene reference = 1000 GENOME (EUR). Subsequently, we retrieved all traits corresponding to each SNP that passed both the P-value and LD screenings on LDlink ( https://ldlink.nih.gov/?tab=home ) and excluded those SNPs unrelated to brain imaging changes and psychiatric disorders, thereby controlling for confounding factors. These confounding factors include socioeconomic status, smoking, alcohol consumption, and many other factors that were not individually listed in previous MRs. Furthermore, to mitigate the impact of weak IVs, we calculated the F-statistic to measure the strength of IVs, with an F-statistic > 10 indicating a low risk of using weak instruments in MR analysis. Parameters calculated alongside the F-statistic included R 2 (the proportion of variance in the exposure explained by the genetic IVs), n (the sample size of the GWAS for the exposure), and k (the number of genetic IVs for the exposure). Bidirectional Mendelian randomization Before conducting the MR analysis, we excluded variants with an EAF < 0·01. Furthermore, to ensure that the SNPs of the IVs originated from the same direction of the DNA strand and could be utilized in both the exposure and outcome datasets, we harmonized the exposure and outcome data and removed palindromic SNPs with EAF close to 0·5 (which could introduce potential strand flip problems). For the forward MR, WMTs were used as exposure, and psychiatric disorders as outcome, while for the reverse MR, exposure and outcome were exchanged. The primary analytical method for MR was inverse variance weighted (IVW), supplementary analytical methods included Wald-ratio, MR weight median, MR Egger, and MR weighted mode. The statistical significance threshold for association was set at P-value < 0.05 with a false discovery rate (FDR) corrected by disease type using the IVW method. The odds ratio (OR) was used to represent the magnitude of the causal effect. Leveraging the multivariate regression concept in multivariate MR 24 , we explored the presence of dominant traits in WMTs. To examine the generalization of the forward causal association between WMTs and psychiatric disorders, we revalidated the forward MR with the FinnGen R11 dataset 45 . Sensitivity analysis and outlier screening We employed MR-PRESSO, MR Egger intercept, Cochran's Q statistic 46 , 47 , and leave-one-out analysis for sensitivity and pleiotropy assessments. Initially, we used the MR-PRESSO global test to examine horizontal pleiotropy. The intercept of MR Egger represented the average pleiotropy of all IVs, with a value different from zero indicating the presence of directional pleiotropy. Heterogeneity was evaluated using Cocharan’s Q. Lastly, we performed a "leave-one-out" analysis to test whether specific SNPs drove the causal association. Selection and interpretation of results As Carter suggested, we should interpret and promote findings from MR studies with extreme caution 48 . Without rigorous validation and empirical verification, MR research is essentially a statistical exercise concerning genes, but we hoped that our results would be well-explained both statistically and biologically. Therefore, results meeting the following criteria were selected: (1) Results must be statistically significant and pass pleiotropy and heterogeneity tests, which means all results' FDR_p 0·05, MR_egger_intercept p > 0·05. Of the 4 methods used in the primary MR analysis, at least 3 must have results in the same direction; (2) Changes in different parameters of WMTs should be mechanistically plausible: this implies consistency in the changes of FA, AD, RD, and MD. Thus, when a WMT exhibited a causal association with a psychiatric disorder, we compared the data of its nominally significant FA, MD, AD, and RD parameters (p < 0·05) to obtain credibility for the neurobiological mechanism. Therefore, we ensured that the direction of change in FA is opposite to that of MD, the change in FA is opposite to at least one of the changes in RD and AD, and the change in MD is in the same direction as at least one of the changes in RD and AD. For instance, a decrease in FA (indicating loss of WMT integrity) theoretically corresponded to increases in MD (cellular damage) and RD (myelin sheath damage), and decrease in AD (axonal damage), which was explainable both neurobiologically and mathematically. Meanwhile, since we indiscriminately excluded SNPs related to non-brain imaging changes and psychiatric disorders when selecting IVs, to avoid overly stringent control due to our exclusions, we conducted MR separately with both pre- and post-exclusion IVs. We identified confounding factors that could reverse significance and performed manual descriptive interpretations to determine whether the results aligned with existing empirical evidence. R packages and codes All MR analyses were carried out in the R environment (version 4.4.1), using TwoSampleMR (version 0.6.5), MendelianRandomization (version 0.10.0) and Oneclick (version 5.1.10). All codes we used were provided at https://github.com/Yifan-xyy/Code_for_MR.git . Mini-Review To obtain a brief overview of observational evidence regarding the association between WMTs and psychiatric disorders, we conducted a literature search on PubMed for articles published from August 2019 to August 2024. The keywords included "white matter tracts," "DTI," "Schizophrenia," "Bipolar disorder," "Cannabis use disorder," "Opioid use disorder," "Alcohol use disorder," "Post-traumatic stress disorder," "Major depressive disorder," "Autism disorder," "Tourette syndrome," and "Attention deficit hyperactivity disorder." Subsequently, we collected the observational findings of varying parameter changes in different WMTs across various psychiatric disorders. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported in part by the National Natural Science Foundation of China (no. 82371499). Authors' contributions Yu YF, Yuan K and Ai SZ proposed the topic and main idea. Yu YF, Ai SZ and Yuan K were responsible for data acquisition, analysis, or interpretation of data. Yu YF wrote the initial draft. Yuan K, Ai SZ, Jia TY, Lin X, Chang SH, Bao YP, Sun J, Gao T and Shi J commented on and revised the manuscript. All authors contributed to the final draft of the manuscript. Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. The code utilized in this study can be obtained through email inquiry or download from link: https://github.com/Yifan-xyy/Code_for_MR.git. References Global incidence: prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 403 , 2133–2161 (2024) Demontis, D., et al.: Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat. 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Nature. 613 , 508–518 (2023) Bowden, J., et al.: Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int. J. Epidemiol. 48 , 728–742 (2019) Greco, M.F., Minelli, C., Sheehan, N.A., Thompson, J.R.: Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat. Med. 34 , 2926–2940 (2015) Carter, A.R., Fraser, A., Howe, L.D., Harris, S., Hughes, A.: Why caution should be applied when interpreting and promoting findings from Mendelian randomisation studies. Gen. Psychiatr. 36 , e101047 (2023) Hu, R., Stavish, C., Leibenluft, E., Linke, J.O.: White Matter Microstructure in Individuals With and At Risk for Bipolar Disorder: Evidence for an Endophenotype From a Voxel-Based Meta-analysis. Biol. Psychiatry Cogn. Neurosci. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6505046","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":452262586,"identity":"0dfcb6a1-f0cc-4877-97bd-10b3588a4d99","order_by":0,"name":"Kai Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYJACxgYGCTkGHhCTjQQtxiRrYUhsIFqLwY3kZw9n1Fikb+c5Y8DwoewwA//sBkJa0swNNxyTyN3Z22PAOOPcYQaJOwcIaUkwk3zAJpG74TyPATNv22EGA4kEQlrSv0k++CeRbgDS8pc4LTlmkhvbJBIMzvYYMDMSo0XyzJsyyZl9EoY7e44VHOw5l84jcYOAFr7j6dske77VyZvzJG988KPMWo5/BgEtCgdgLgRiEJsHv3ogkG9A0jIKRsEoGAWjACsAAL+TQ8y2ET7ZAAAAAElFTkSuQmCC","orcid":"","institution":"Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders","correspondingAuthor":true,"prefix":"","firstName":"Kai","middleName":"","lastName":"Yuan","suffix":""},{"id":452262587,"identity":"24b106c4-c823-4adf-b792-8e2269db1544","order_by":1,"name":"Yifan Yu","email":"","orcid":"https://orcid.org/0000-0002-7455-6525","institution":"Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Yu","suffix":""},{"id":452262588,"identity":"7ea5d85f-5923-4835-8b3b-1933f666ae77","order_by":2,"name":"Sizhi Ai","email":"","orcid":"https://orcid.org/0000-0001-5701-1235","institution":"The Affiliated Brain Hospital, Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sizhi","middleName":"","lastName":"Ai","suffix":""},{"id":452262589,"identity":"315bca22-aad2-4eab-a59a-52c26634ac40","order_by":3,"name":"Tianye Jia","email":"","orcid":"https://orcid.org/0000-0001-5399-2953","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Tianye","middleName":"","lastName":"Jia","suffix":""},{"id":452262590,"identity":"536692af-6a1f-4dc9-8586-7807d2c21df4","order_by":4,"name":"Lin Xiao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Xiao","suffix":""},{"id":452262591,"identity":"a48799c2-dfdb-46e9-98a5-ead464ef9ac0","order_by":5,"name":"Yanping Bao","email":"","orcid":"https://orcid.org/0000-0002-1881-0939","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yanping","middleName":"","lastName":"Bao","suffix":""},{"id":452262592,"identity":"e12e8338-e6ab-40ef-afa2-70f42f6f4878","order_by":6,"name":"Suhua Chang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Suhua","middleName":"","lastName":"Chang","suffix":""},{"id":452262593,"identity":"490f17d8-1bd9-44b7-b527-c67de05b4827","order_by":7,"name":"Jie Sun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Sun","suffix":""},{"id":452262594,"identity":"40cbe7f6-5d12-47f8-9fe7-36b8330feaff","order_by":8,"name":"Teng Gao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Teng","middleName":"","lastName":"Gao","suffix":""},{"id":452262595,"identity":"7ff51785-11fd-4d98-b924-6e9aa4501eb4","order_by":9,"name":"Jie Shi","email":"","orcid":"https://orcid.org/0000-0001-6567-8160","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2025-04-22 13:56:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6505046/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6505046/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82283259,"identity":"cd23096c-4da0-4310-81bc-cbc1108f3bf5","added_by":"auto","created_at":"2025-05-08 15:45:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":481997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow of the causal inference between WMTs and psychiatric disorders. \u003c/strong\u003eAbbreviations: AD: axial diffusivities; ADHD: attention deficit hyperactivity disorder; ASD: autism disorder; AUD: alcohol use disorder; BD: bipolar disorder; CUD: cannabis use disorder; FA: fractional anisotropy; MD: mean diffusivities; MDD: major depression disorder; MO: mode of anisotropy; MR: mendelian randomization; PTSD: post-traumatic stress disorder; RD: radial diffusivities; SCZ: schizophrenia; TS: Tourette syndrome; WMTs: white matter tracts; OUD: opioid use disorder.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6505046/v1/651818f8abc70c268d213428.png"},{"id":82283583,"identity":"069e183d-7b99-4d10-9999-4b075e57eb04","added_by":"auto","created_at":"2025-05-08 15:53:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":813021,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausalities in the forward MR. \u003c/strong\u003eThe forest plot illustrates the significant causalities. The effect estimates displayed in the figure were calculated by IVW method, representing the OR of psychiatric disorders per 1 s.d. change in WMTs. And the error bars repersent the 95% CI. All statistical tests were two-sided, and only significant results (FDR p \u0026lt;0.05) that passed heterogeneity and pleiotropytests, sensitivity analysis, and confounding factor adjustment were presented in the figure.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6505046/v1/c341ee16c40dbfb7aacb9c65.png"},{"id":82283261,"identity":"b1d98346-b212-40be-a244-d66e32d9e5aa","added_by":"auto","created_at":"2025-05-08 15:45:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":262135,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausalities in the reverse MR. \u003c/strong\u003eThe forest plot illustrates the significant causalities. The effect estimates displayed in the figure were calculated by IVW method. And the error bars repersent the 95% CI. All statistical tests were two-sided, and only significant results (FDR p \u0026lt;0.05) that passed heterogeneity and pleiotropytests, sensitivity analysis, and confounding factor adjustment were presented in the figure.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6505046/v1/4faf8bd9afd879ded8db2483.png"},{"id":82283260,"identity":"afb14068-a571-45a1-aaa8-89751af013a3","added_by":"auto","created_at":"2025-05-08 15:45:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":484456,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of results. \u003c/strong\u003eAD: axial diffusivities; FA: fractional anisotropy; MD: mean diffusivities; MO: diffusion tensor mode; RD: radial diffusivities.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6505046/v1/4818e3bc957b1f5ed37b8256.png"},{"id":82284461,"identity":"ff478b8b-77df-4f82-acbf-87be949cea43","added_by":"auto","created_at":"2025-05-08 16:01:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3235051,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6505046/v1/094d7920-db4f-4b38-988a-60df03d7d554.pdf"},{"id":82283271,"identity":"d60a98bb-1d98-4967-b2b8-bae94154a8c4","added_by":"auto","created_at":"2025-05-08 15:45:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2813343,"visible":true,"origin":"","legend":"Supplementary Materials","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6505046/v1/c68aeed8bc41c1b53cec1963.docx"},{"id":82283267,"identity":"dba2e210-18a3-445e-9a93-92fda1a6414d","added_by":"auto","created_at":"2025-05-08 15:45:06","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7947309,"visible":true,"origin":"","legend":"Supplementary Materials-2","description":"","filename":"Supplementarymaterials2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6505046/v1/0f1d6ce48e94b5e14e05cd53.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Causal Relationship between White Matter Tracts and Psychiatric Disorders: A Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePsychiatric disorders impose a substantial burden on global health, as evidenced by the Global Burden of Diseases (GBD) 2021\u003csup\u003e1\u003c/sup\u003e, which highlights 8 psychiatric disorders among the top 25 disease burdens. Recently, a large body of literature has shown that white matter tracts (WMTs), which are responsible for information transmission, are closely related to the onset, development, and prognosis of psychiatric disorders \u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Researchers often utilize diffusion tensor imaging (DTI) sequence of magnetic resonance imaging (MRI) to visualize WMTs and assess their integrity \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, as well as measure potential damage to axons\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, myelin \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, and cell membranes \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. These functional changes in DTI \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e are characterized by five parameters: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and mode of anisotropy (MO). Different WMTs exhibit variations in both structure and function. For readers seeking further explanations, please refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe glossary of white matter tracts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite matter tract\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrainstem tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis structure can be identified at medulla and the pons level, but should also contain corticopontine and corticobular tracts. This tract is a part of the left PLIC \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProjection tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis structure is divided into three regions: ACR, SCR and PCR. The divisions are made at the middle of the genu and splenium of the corpus callosum, which are arbitrarily chosen and not based on anatomic or functional boundaries. This region includes the thalamic radiations (thalamocortical, corticothalamic fibers) and parts of the long corticofugal pathways, such as the corticospinal, corticopontine, and corticobulbar tracts. The boundary of the CR and the IC is defied at the axial level where the IC and EC merge.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProjection tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe anterior thalamic radiation and fronto-pontine fibers are the major contributors in this region.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProjection tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe superior thalamic radiation and long corticofugal pathways, such as the corticospinal tract and the fronto- and parieto-pontine fibers, are the major constitutes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRLIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProjection tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn this region, the posterior thalamic radiation (corticothalamic and thalamo-cortical fibers, including the optic radiation) is the major constituent, but can also include the parieto-, occipito- and temporopontine fibers. The boundary with SS is arbitrarily defined at the middle of the SCC.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis tract is located at the dorsolateral regions of the CR and contains connections between the frontal, parietal, occipital, and the temporal lobes including language-related areas (Broca\u0026rsquo;s, Geschwind\u0026rsquo;sand Wernicke\u0026rsquo;s territories.) This tract is associated with working memory performance, attention \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and language-related \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e functions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis tract is located at the superior edge of the ALIC (anterior thalamic radiation) and the boundary is not always clear. Only the frontal region is identifiable and projection to the parietal lobe cannot be segmented. It has been suggested that this tract is a part of the anterior thalamic radiation and not an association fiber.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis tract connects the frontal lobe (orbital cortex) and the anterior temporal lobe. It can be discretely identified where the two lobes are connected but not within the frontal and the temporal lobes where it merges with other tracts. This tract is associated with language-related \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e function and its damage will lead to inattentive-emotional symptoms and cognitive deficits \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFO/UNC\u003c/p\u003e \u003cp\u003eIFO/ILF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe IFO connects the frontal lobe and the occipital lobe. In the frontal lobe, this partition also includes the frontal projection of the UNC. In the temporal and occipital lobe, the IFO merges with the ILF, which is segmented as a different partition. The IFO is the longest association fiber running medially in the temporal lobe and connects the frontal lobe with the occipital, temporal and superior parietal cortex \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. And it poses language-related \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e functions and its damage will cause inattention and emotional problems \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe IFO/ILF merges with projection fibers from the RLIC and forms a large, sheet-like, sagittal structure, called the SS. This region, therefore should include both association and projection fibers. The boundary of the SS and the PCR is also arbitrarily defined at the axial level of the SCC.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis region, located lateral to the IC, is believed to contain association fibers, such as the ALF and IFO and commissural fibers. Because of the limited image resolution, the external and extreme capsules are not resolved.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis tract connects the frontal lobe with the amygdala \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, carrying information from the cingulate gyrus to the hippocampus. The entire pathway from the frontal lobe can be clearly identified. In the WMPM, the CG in the cingulate gyrus and the hippocampal regions is separated at the axial level of the SCC and denoted as CGC and CGH, respectively. It is associated with emotion and fear extinction \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFX/ST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThese tracts are both related to the limbic system: the FX to the hippocampus, and the ST to the amygdala. Both tracts project to the septum and the hypothalamus. With current image resolution capabilities, these two tracts cannot be distinguished in the hippocampal area, and both tracts are labeled as FX. The ST can be discretely identified in the amygdala and the dorsal thalamus.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommissural fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe body of CC. This tract interconnects parietal and temporal cortices \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, connecting bilateral premotor, primary motor, and primary sensory cortex \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Its damage will cause sensory and motor processing abnormalities.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommissural fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe genus of CC. This tract connects the bilateral frontal cortex and is involved in sensory and visuospatial processing \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Its damage will cause social functioning impairment \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e or disassociative symptoms \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommissural fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe splenium of CC. This tract receives input from the\u0026nbsp;occipital lobes \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eThe definition and description of white matter tracts were from Mori et al \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Abbreviations: ACR: anterior corona radiata; ALIC: anterior limb of internal capsule; ATR: anterior thalamic radiation; BCC: body of corpus callosum; CC: corpus callosum; CGC: cingulum connecting to cingulate gyrus; CGH: cingulum connecting to hippocampus; CR: corona radiata; CST: corticospinal tract; EC: external capsule; FX/ST: fornix and stria terminalis; FX: Fornix (column and body of fornix); GCC: genu of corpus callosum; IC: internal capsule; ICP: inferior cerebellar peduncle; IFO/ILF: inferior fronto-occipital fasciculus/inferior longitudinal fasciculus; IFO/UNC: inferior fronto-occipital fasciculus/uncinate fasciculus; ILF: inferior longitudinal fasciculus; PCR: posterior corona radiata; PLIC: posterior limb of internal capsule; PTR: posterior thalamic radiation (include optic radiation); RLIC: retrolenticular part of internal capsule; SCC: splenium of corpus callosum; SCR: superior corona radiata; SFO: superior fronto-occipital fasciculus; SLF: superior longitudinal fasciculus; SS: Sagittal stratum; STR: superior thalamic radiation; UNC: uncinate fasciculus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResearch has indicated that certain characteristics of WMTs could potentially serve as diagnostic markers for psychiatric disorders \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. For example, patients with major depressive disorder (MDD) who had suffered non-suicidal self-injury showed reduced WMT integrity compared to healthy controls \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and appeared to have more severe dysfunction of the kynurenine/tryptophan pathway \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Compared with MDD, the alterations of WMTs in patients with bipolar disorder (BD) were more evident \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, many studies focus solely on the FA value to represent changes in the properties of WMTs, as summarized in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, while neglecting the contributions of other parameters such as MD, RD, AD, and MO. In addition, although many studies have shown associations between WMTs and psychiatric disorders, the causality and directions of these associations are still unclear. Therefore, the lack of clarity about the causal association between WMTs and psychiatric disorders, as well as the neglection of other important parameters, has limited the application of DTI techniques in the field of psychiatric disorders \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile causal inference in observational studies was commonly confounded by complex factors, such as the medications and environment \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, mendelian randomization (MR) studies can effectively mitigate these confounding effects by utilizing genetic polymorphisms as instrumental variables (IVs) for exposure \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The core idea of MR is to \"infer causality between exposure and outcome using genetic variants as instrumental variables, leveraging the free and random assortment of genes and the stability of genotypes against environmental influences to avoid confounding bias in traditional epidemiology effectively.\" \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Current MR research has explored the causal association between imaging-derived phenotype and 10 psychiatric disorders \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, between brain functional networks and 12 psychiatric disorders \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, between brain structure and Alzheimer's disease (ALZ) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and between cortical structure, white matter microstructure, and neurodegenerative diseases \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. These studies revealed some causal associations between brain structure and function and neuropsychiatric disorders. However, these studies either fail to uncover the causalities between WMTs and psychiatric disorders. Thus, we conducted bidirectional mendelian randomization (MR) in this study to explore the causal associations between WMTs and psychiatric disorders.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview of MR\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe research design is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. and the baseline characteristics of genome-wide association studies (GWAS) are described in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All Mendelian randomization (MR) processes followed the Burgess \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and the checklist was in \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e. In this article, we found causal associations of 11 types of WMTs with 8 psychiatric disorders and found reverse causal associations of AUD with 3 types of WMTs. All the subsequent reported results have passed FDR correction. Among all included GWAS data of psychiatric disorders, only AUD and PTSD included a small portion of UK Biobank data, with the sample overlap being \u0026lt;\u0026thinsp;5%. The stepwise screening results of IVs and the control of confounding factors and outliers were detailed in \u003cb\u003eTable S3 - Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e5\u003c/b\u003e. Overall, we have conducted rigorous IV screening and ensured the control of confounders and outliers. During statistical analysis, the F-values of the IVs in the final results were all \u0026gt;\u0026thinsp;20, and the achieved statistical power were \u0026gt;\u0026thinsp;90%.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of all GWAS summary-level data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eGWAS summary-level data of White matter tracts\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSource\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eParameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNumber of tracts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNumber of DTI traits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSample\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(Total)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ePubmed ID\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite matter tracts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZhao et al.\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAD, FA, MD, MO, RD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34140357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eGWAS summary-level data of Psychiatric Disorders (Bidirectional MR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSource\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSample\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(cases)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSample\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(control)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSample\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(Total)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAncestry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ePubmed ID\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eAlcohol use disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e639923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e753248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e38062264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eiPSYCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMVP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e368113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e448141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e209926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e218792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQIMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYale-Penn 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAttention deficit hyperactivity disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e36702997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eiPSYCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edeCODE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAutism disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e30804558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eiPSYCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBipolar disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e371549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e413466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34002096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eCannabis use disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e843744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e886025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e37985822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGC, deCODE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e298941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e313463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMVP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e423587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e445847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eiPSYCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYale-Penn 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMajor depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e529044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e713314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e37464041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eiPSYCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e256915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMVP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e250215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpioid Use Disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMVP, PGC, iPSYCH, FinnGen, Partners Biobank, BioVU, and Yale-Penn 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e554,186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e569437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35879402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-traumatic stress disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1085746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1222882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38637617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchizophrenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35396580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTourette syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30818990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eGWAS summary-level data of Psychiatric Disorders (Replication)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003cp\u003e(cases)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003cp\u003e(control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003cp\u003e(Total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAncestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePubmed ID\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e435038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e453733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttention deficit hyperactivity disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e445327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e449029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBipolar disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e394756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e402965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchizophrenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e439144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e446077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAll download links were in supplementary materials. Abbreviations: AD: Axial diffusivities; deCODE: deCODE Genetics; DTI: Diffusion tensor imaging; EUR: European; FA: Fractional anisotropy; iPSYCH: The Lundbeck Foundation Integrative Psychiatric Research; MD: Mean diffusivities; MGB: Mass General Brigham Biobank; MVP: the Million Veteran Program; MO: Mode of anisotropy; PGC: Psychiatric Genomics Consortium; QIMR: QIMR Berghofer Medical Research Institute; RD: Radial diuffsivities; UKB: UK Biobank.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCausal effects of white matter tracts on ADHD and TS\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cb\u003eTable S22\u003c/b\u003e, we uncovered causal associations of the column and body of fornix (FX) with ADHD, as well as of the fornix (cres) / stria terminalis (FXST) with TS. Specifically, for ADHD, a one s.d. increase in MO of the FX was associated with a 31\u0026middot;3% increase in the odds of ADHD (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;313, 95% CI\u0026thinsp;=\u0026thinsp;1\u0026middot;117\u0026thinsp;~\u0026thinsp;1\u0026middot;543, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 9\u0026middot;96 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). Conversely, for TS, a one s.d. increase in MO of the FXST was linked to a 71\u0026middot;4% decrease in the odds of TS (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;286, 95% CI\u0026thinsp;=\u0026thinsp;0\u0026middot;166\u0026thinsp;~\u0026thinsp;0\u0026middot;493, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 6\u0026middot;61 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eCausal effects of white matter tracts on BD and SCZ\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cb\u003eTable S22\u003c/b\u003e, we identified a causal association of FXST and BD, as well as of FXST, superior corona radiata (SCR), and retrolenticular part of internal capsule (RLIC) with SCZ. We found that a one s.d. increase in MO of the FXST was associated with a 33\u0026middot;4% decrease in odds of BD (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;666, 95% CI\u0026thinsp;=\u0026thinsp;0\u0026middot;544\u0026thinsp;~\u0026thinsp;0\u0026middot;816, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 8\u0026middot;63 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Specifically, one s.d. increase in FA of the FXST was associated with a 37\u0026middot;6% decrease in SCZ risk (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;724, 95% CI\u0026thinsp;=\u0026thinsp;0\u0026middot;618\u0026thinsp;~\u0026thinsp;0\u0026middot;849, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 6\u0026middot;74 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Conversely, one s.d. increase in FA of the SCR led to 16\u0026middot;0% elevation in SCZ risk (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;160, 95% CI\u0026thinsp;=\u0026thinsp;1\u0026middot;058\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026middot;272, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 1\u0026middot;09 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). Additionally, one s.d. increase in MO of RLIC was linked to a 22\u0026middot;5% (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;225, 95% CI\u0026thinsp;=\u0026thinsp;1\u0026middot;105-1\u0026middot;357, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 1\u0026middot;55 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) increase in odds of SCZ.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eCausal effects of white matter tracts on MDD, OUD, and AUD\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cb\u003eTable S22\u003c/b\u003e, we identified causal associations of the genu of corpus callosum (GCC) with MDD and OUD, as well as of the inferior fronto-occipital fasciculus (IFO) with between AUD. For OUD, one s.d. increase in MD and RD of the GCC region was linked to 11\u0026middot;4% (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;886, 95% CI\u0026thinsp;=\u0026thinsp;0\u0026middot;824\u0026thinsp;~\u0026thinsp;0\u0026middot;952, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 9\u0026middot;30 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and 12\u0026middot;7% (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;873, 95% CI\u0026thinsp;=\u0026thinsp;0\u0026middot;803\u0026thinsp;~\u0026thinsp;0\u0026middot;949, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 1\u0026middot;48 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) decreases in risk respectively. Similarly, one s.d. increase in MD was linked to 12\u0026middot;2% elevation in MDD risk (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;122, 95% CI\u0026thinsp;=\u0026thinsp;1\u0026middot;054\u0026thinsp;~\u0026thinsp;1\u0026middot;195, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 3\u0026middot;12 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). Furthermore, one s.d. increase in AD of the IFO was associated with a 7\u0026middot;9% decrease in AUD risk (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;921, 95% CI\u0026thinsp;=\u0026thinsp;0\u0026middot;883\u0026thinsp;~\u0026thinsp;0\u0026middot;961, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 1\u0026middot;51 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eCausal effects of white matter tracts on PTSD\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cb\u003eTable S22\u003c/b\u003e, we uncovered causal associations between PTSD and 7 WMTs: cingulum connecting to hippocampus (CGH), FXST, posterior corona radiata (PCR), RLIC, superior fronto-occipital fasciculus (SFO), superior longitudinal fasciculus (SLF) and uncinate fasciculus (UNC). one s.d. increase in FA for PCR, SFO, SLF, and UNC were found to decrease the risk of PTSD by 2\u0026middot;2%-3\u0026middot;8% (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;962\u0026thinsp;~\u0026thinsp;0\u0026middot;978). Furthermore, one s.d. increases in MD of CGH, SFO and UNC, RD of CGH, FXST, PCR, RLIC and SLF, and AD of PCR contributed to an increased risk of PTSD (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;017\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026middot;083). Lastly, one s.d. increase in MO of the ACR was associated with a 6% decrease in the odds of PTSD (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;940, 95% CI\u0026thinsp;=\u0026thinsp;0\u0026middot;895\u0026thinsp;~\u0026thinsp;0\u0026middot;988). More details please refer to \u003cb\u003eSupplementary Materials.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReverse Mendelian randomization\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. and \u003cb\u003eTable S22\u003c/b\u003e, we identified reverse causal associations of AUD with CGC, body of corpus callosum (BCC) and ACR. Higher risks of AUD was associated with decreased FA (IVW β = -0\u0026middot;246, 95% CI = -0\u0026middot;392~-0\u0026middot;093, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 1\u0026middot;53 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and increased RD (IVW β\u0026thinsp;=\u0026thinsp;0\u0026middot;259, 95% CI\u0026thinsp;=\u0026thinsp;0\u0026middot;111\u0026thinsp;~\u0026thinsp;0\u0026middot;406, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 5\u0026middot;84 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) of CGC, increased MD (IVW β\u0026thinsp;=\u0026thinsp;0\u0026middot;236 95% CI\u0026thinsp;=\u0026thinsp;0\u0026middot;074\u0026thinsp;~\u0026thinsp;0\u0026middot;398, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 4\u0026middot;22 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and increased AD (IVW β\u0026thinsp;=\u0026thinsp;0\u0026middot;229, 95% CI\u0026thinsp;=\u0026thinsp;0\u0026middot;075\u0026thinsp;~\u0026thinsp;0\u0026middot;383, P\u003csub\u003euncorrected\u003c/sub\u003e \u0026lt; 3\u0026middot;60 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) in BCC.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariate Mendelian randomization and validation\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe conducted multivariate MR analysis\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e on SCZ, OUD, and PTSD, and only the association between FXST_RD and PTSD was retained. More details can be found in \u003cb\u003eTable S23\u003c/b\u003e. Subsequently, we partially validated our results using GWAS data for ADHD, AUD, BD, and SCZ from the FinnGen R11 database. The causal association between FX_MO and ADHD in the forward MR analysis was replicated (P\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;05 in the replication datasets). Although BD did not yield identical replication results, it produced similar results (with different WMT parameters but biologically comparable meanings), which could also be considered successful replication.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMini-review for DTI and psychiatric disorders\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe have compiled articles published in the past 5 years that explored the relationship between WMTs and psychiatric disorders (32 articles included from 868 search results on PubMed, see \u003cb\u003eSupplementary Text and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. Our review revealed that the majority of current research focuses on the association between changes in FA (32/32) values of WMTs and psychiatric disorders, while discussions on MD (7/32), AD (3/32), and RD (9/32) parameters were relatively limited. ADHD, ASD, SCZ, BD, MDD, and PTSD were the major psychiatric disorders researchers reported. Additionally, the distribution pattern of WMTs that showed significant associations with psychiatric disorders was regionally dependent, especially in the limbic system, thalamic radiation, and corpus callosum. These studies reveal the close association between WMTs and psychiatric disorders.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo our knowledge, the current study represents the most comprehensive investigation to date of the causal associations between WMTs and psychiatric disorders. As summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e., our findings not only establish forward causal associations between 24 WMTs-psychiatric disorder pairs but also reveal reverse causal associations between AUD and WMTs. These results indicate that WMTs may hold the potential to act as target regions for diagnosis or intervention in psychiatric disorders.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRecent MR studies have investigated the causal associations between imaging-derived phenotype (IDP) and psychiatric disorders, encompassing brain functional networks \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, cortical structures \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, white matter microstructure \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and combinations of the three \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Building upon previous research, we have incorporated additional DTI parameters, expanded the scope to include more psychiatric disorders potentially related to WMTs, and significantly increased the sample sizes of psychiatric disorders. Guo et al. \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e utilized the GWAS data from Smith et al. \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e to validate the causal associations between IDP (which includes WMTs) and 10 psychiatric disorders, uncovering causal associations between WMTs and SCZ, as well as anorexia nervosa. Comparing with Guo et al \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, we have incorporated additional DTI parameters (AD and RD), and expanded the scope to include more psychiatric disorders potentially related to WMTs (AUD, OUD, CUD, and PD). To minimize the potential bias, we excluded psychiatric disorders with a high risk of producing diagnosis bias based on our clinical experience. For example, general anxious disorder and panic disorders are classified equally as anxious disorders in the database. But classifying them this way makes the different illnesses seem more similar than they really are, which makes it difficult for us to get interpretable results from data. Furthermore, we significantly increased the sample sizes (ADHD: from 53,293 to 225,534; BD: from 51,710 to 413,466; MDD: from 142,646 to 713,314; PTSD: from 146,660 to 1,222,882) of psychiatric disorders. Moreover, we implemented more specialized control measures to overcome limitations inherent in previous confounder and outlier removal methods. As previous GWAS studies had demonstrated, the genetic traits of brain structure were significantly stronger than those of brain functional networks \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Therefore, compared to brain networks, utilizing SNPs related to brain structure would be more advantageous in revealing causal associations from a genetic perspective, yielding results with greater generalizability.\u003c/p\u003e \u003cp\u003eAlthough imaging indicators cannot fully represent the actual neurobiological processes occurring, current studies tend to interpret decreases in FA and increases in MD as indicators of white matter myelin integrity damage\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e or axonal damage\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. MO designates the type of anisotropy as a continuous measure, indicating differences in diffusion tensor shape ranging from planar (flattened cylinders) to linear (tubes)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, which can help to map the direction of the WMTs. When dealing with more complex fiber structures where multiple fiber orientations coexist within a single voxel, techniques such as fiber orientation distribution are employed to measure this complexity. In such cases, MO can be utilized to signify the predominant or mean orientation when numerous fiber directions are identified. Although the association between WMTs and psychiatric disorders varies according to age, gender, medication, population (e.g., twins), disease severity stratification, and imaging control methods, as our mini review shown (\u003cb\u003eSupplementary Materials\u003c/b\u003e), the preponderance of evidence currently supported that decrease in FA, increase in MD and RD, and alteration of AD were associated with the risk of psychiatric disorders and the severity of progression. Similarly, our MR results supported this tendency and aligned closely with 3 distinct patterns of neuropathology observed in the investigation of white matter fiber tracts in patients \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e: (1) developmental abnormalities in limbic fibers (CGH, FX, and FXST), (2) abnormal maturation in long-range association fibers (IFO), and (3) severe developmental abnormalities and accelerated aging in callosal fibers (GCC and BCC).\u003c/p\u003e \u003cp\u003eThe first main finding of this study is the causal associations between limbic system-related fibers (FXST or FX) and psychiatric disorders (SCZ, BD, ADHD, and TS). FXST, composed of FX and ST, is a fiber bundle associated with the limbic system \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e: the FX connects to the hippocampus, and the ST connects to the amygdala. Thus, alterations in FXST may contribute to emotional dysregulation \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e (in SCZ, BD, TS, related to the amygdala) or cognitive impairments \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e (in ADHD, related to the hippocampus). We speculated that the similar results of ADHD, TS, BD and SCZ are partly coming from the overlap of their similar pathogenesis and could partly explain the similarity of some clinical symptom (such as mood instability). Furthermore, we notice that the significant results of FXST or FX are mostly related to the MO parameter, which potentially suggests that the loss of complexity in WMTs may be the underlying process of psychiatric disorders. In conjunction with the existing evidence of close associations between the limbic cortical regions, limbic fibers and psychiatric disorders\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, we can conclude that both the cortex and WMTs of the limbic system are inextricably linked to the risk of ADHD, TS, BD and SCZ.\u003c/p\u003e \u003cp\u003eThe second main finding of this study is the results of PTSD and other psychiatric disorders. The results of PTSD suggested that it is an environmentally dependent psychiatric disorder with a relatively weaker association with genetics, which aligned with previous research\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Furthermore, we found a causal association between GCC and MDD, while GCC has connections with the frontal cortex and is involved in sensory and visuospatial processing \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and it is closely related to social functioning impairment and dissociative symptoms \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Besides, the associations between WMTs and AUD in forward MR and reverse MR indicated that the substance itself (alcohol) is a significant risk factor in the occurrence of substance abuse disorders, but genetic factors are also influential.\u003c/p\u003e \u003cp\u003eThis study had several limitations. Firstly, some overlap (\u0026lt;\u0026thinsp;5%) was unavoidable in the samples. Secondly, we could not assess the impact of population and diagnostic stratification on the research results due to the use of publicly available GWAS databases. Thirdly, imaging data are indirect evidence, which limits our ability to infer the exact neurobiological processes from them. Fourthly, although we have controlled for both confounding factors and outliers, there could still be omitted factors and also potentially overall controlled factors (which is unnecessary and likely cause false negative). Lastly, caution should be exercised when interpreting the clinical implications of OR values estimated by MR, as it utilizes risk single nucleotide polymorphisms (SNP) of exposure to explore the lifelong impact of exposures on outcomes, rather than the effects of specific interventions over a period of time, and cannot be equated with RCT studies.\u003c/p\u003e \u003cp\u003eIn summary, our findings suggested potential causal associations between WMTs characteristics and psychiatric disorders through mendelian Randomization, which exhibited both similarities and differences\u0026mdash;a pattern contingent upon the clinical feature similarities among different psychiatric disorders. This genetically informed approach suggests that specific WMTs characteristics could serve as hallmarks for psychiatric disorders \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, providing referential targeted regions for psychiatric disorders (diagnosis or intervention), thereby facilitating future clinical practice and scientific research.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData on white matter microstructure were obtained from Zhao et al. \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Zhao et al. conducted a genome-wide association study (GWAS) on white matter microstructure using dMRI data from 33,292 individuals in the UK Biobank (UKB) British cohort, reporting a cumulative total of 9,023,710 SNPs (Chromosomes 1\u0026ndash;22). The analysis method for white matter microstructure was derived from DTI models using the ENIGMA-DTI pipeline. They analyzed five primary DTI metrics (FA, MD, AD, RD, MO) across 21 brain white matter tracts (WMTs), generating 110 DTI phenotypes for each individual. A detailed description of the GWAS can be found in \u003cb\u003eTable S3\u003c/b\u003e. The GWAS summary statistics are publicly available at Zenodo (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/records/4549730\u003c/span\u003e\u003cspan address=\"https://zenodo.org/records/4549730\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, and the results can also be browsed through the BIG-KP knowledge portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bigkp.org/\u003c/span\u003e\u003cspan address=\"https://bigkp.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe sourced data from public databases for psychiatric disorders, prioritizing GWAS-summary-level data when available. To minimize bias in results due to sample overlap between exposure and outcome, as well as ethnic differences, our selection criteria for psychiatric disorder GWAS data were as follows: (1) exclusion of UKB data; (2) inclusion of individuals of European descent (EUR); (3) if UKB data were included, the total sample size had to exceed 732,640 (ensuring a maximum sample overlap rate of \u0026lt;\u0026thinsp;5%). According to Burgess et al., a 5% sample overlap in MR studies is estimated to introduce a bias of \u0026lt;\u0026thinsp;0\u0026middot;15% \u003csup\u003e35\u003c/sup\u003e. The GWAS data on 10 psychiatric disorders, including attention deficit hyperactivity disorder (ADHD) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, autism spectrum disorder (ASD) \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, schizophrenia (SCZ) \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, BD \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, MDD \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, post-traumatic stress disorder (PTSD) \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, alcohol use disorder (AUD) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, opioid use disorder (OUD) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, cannabis use disorder (CUD) \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and Tourette syndrome (TS) \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, were sourced from public databases. The GWAS sample size of psychiatric disorders ranged from 14,307 to 1,222,882.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMissing data processing\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe performed SNP position conversion based on chromosome (CHR) and position (POS) for GWAS data lacking SNP ID using the GRCh37 reference. We matched the missing effective allele frequencies (EAF) using data from the author\u0026rsquo;s tutorial or the 1000 Genomes Project. If EAF remained missing after this step, we substituted it with EAF\u0026thinsp;=\u0026thinsp;0\u0026middot;5.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSelection of instrumental variables (IVs)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMR analysis relies on three crucial assumptions: (1) the IVs should be associated with the exposure; (2) the IVs should be independent of confounding factors; (3) the IVs should only influence the outcome through the exposure directly. These assumptions necessitate a strong correlation between IVs and exposure and independence, and the pleiotropy of IVs does not interfere with the association between the exposure and the outcome\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The first two assumptions are primarily ensured through the selection of IVs. We selected SNPs with P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e to satisfy the strong correlation requirement. To ensure the independence of SNPs, we initially remove linkage disequilibrium (LD) due to close spatial associations, with parameters set as: r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0\u0026middot;001, window size\u0026thinsp;=\u0026thinsp;1,000 kb, gene reference\u0026thinsp;=\u0026thinsp;1000 GENOME (EUR). Subsequently, we retrieved all traits corresponding to each SNP that passed both the P-value and LD screenings on LDlink (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ldlink.nih.gov/?tab=home\u003c/span\u003e\u003cspan address=\"https://ldlink.nih.gov/?tab=home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e and excluded those SNPs unrelated to brain imaging changes and psychiatric disorders, thereby controlling for confounding factors. These confounding factors include socioeconomic status, smoking, alcohol consumption, and many other factors that were not individually listed in previous MRs. Furthermore, to mitigate the impact of weak IVs, we calculated the F-statistic to measure the strength of IVs, with an F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 indicating a low risk of using weak instruments in MR analysis. Parameters calculated alongside the F-statistic included R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (the proportion of variance in the exposure explained by the genetic IVs), n (the sample size of the GWAS for the exposure), and k (the number of genetic IVs for the exposure).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBidirectional Mendelian randomization\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBefore conducting the MR analysis, we excluded variants with an EAF\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;01. Furthermore, to ensure that the SNPs of the IVs originated from the same direction of the DNA strand and could be utilized in both the exposure and outcome datasets, we harmonized the exposure and outcome data and removed palindromic SNPs with EAF close to 0\u0026middot;5 (which could introduce potential strand flip problems).\u003c/p\u003e \u003cp\u003eFor the forward MR, WMTs were used as exposure, and psychiatric disorders as outcome, while for the reverse MR, exposure and outcome were exchanged. The primary analytical method for MR was inverse variance weighted (IVW), supplementary analytical methods included Wald-ratio, MR weight median, MR Egger, and MR weighted mode. The statistical significance threshold for association was set at P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with a false discovery rate (FDR) corrected by disease type using the IVW method. The odds ratio (OR) was used to represent the magnitude of the causal effect. Leveraging the multivariate regression concept in multivariate MR \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, we explored the presence of dominant traits in WMTs. To examine the generalization of the forward causal association between WMTs and psychiatric disorders, we revalidated the forward MR with the FinnGen R11 dataset \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis and outlier screening\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe employed MR-PRESSO, MR Egger intercept, Cochran's Q statistic\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, and leave-one-out analysis for sensitivity and pleiotropy assessments. Initially, we used the MR-PRESSO global test to examine horizontal pleiotropy. The intercept of MR Egger represented the average pleiotropy of all IVs, with a value different from zero indicating the presence of directional pleiotropy. Heterogeneity was evaluated using Cocharan\u0026rsquo;s Q. Lastly, we performed a \"leave-one-out\" analysis to test whether specific SNPs drove the causal association.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSelection and interpretation of results\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs Carter suggested, we should interpret and promote findings from MR studies with extreme caution \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Without rigorous validation and empirical verification, MR research is essentially a statistical exercise concerning genes, but we hoped that our results would be well-explained both statistically and biologically. Therefore, results meeting the following criteria were selected: (1) Results must be statistically significant and pass pleiotropy and heterogeneity tests, which means all results' FDR_p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;05, MR PPRESSO_Global_test p\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026middot;05, MR_egger_intercept p\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026middot;05. Of the 4 methods used in the primary MR analysis, at least 3 must have results in the same direction; (2) Changes in different parameters of WMTs should be mechanistically plausible: this implies consistency in the changes of FA, AD, RD, and MD. Thus, when a WMT exhibited a causal association with a psychiatric disorder, we compared the data of its nominally significant FA, MD, AD, and RD parameters (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;05) to obtain credibility for the neurobiological mechanism. Therefore, we ensured that the direction of change in FA is opposite to that of MD, the change in FA is opposite to at least one of the changes in RD and AD, and the change in MD is in the same direction as at least one of the changes in RD and AD. For instance, a decrease in FA (indicating loss of WMT integrity) theoretically corresponded to increases in MD (cellular damage) and RD (myelin sheath damage), and decrease in AD (axonal damage), which was explainable both neurobiologically and mathematically.\u003c/p\u003e \u003cp\u003eMeanwhile, since we indiscriminately excluded SNPs related to non-brain imaging changes and psychiatric disorders when selecting IVs, to avoid overly stringent control due to our exclusions, we conducted MR separately with both pre- and post-exclusion IVs. We identified confounding factors that could reverse significance and performed manual descriptive interpretations to determine whether the results aligned with existing empirical evidence.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eR packages and codes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll MR analyses were carried out in the R environment (version 4.4.1), using TwoSampleMR (version 0.6.5), MendelianRandomization (version 0.10.0) and Oneclick (version 5.1.10). All codes we used were provided at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Yifan-xyy/Code_for_MR.git\u003c/span\u003e\u003cspan address=\"https://github.com/Yifan-xyy/Code_for_MR.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMini-Review\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo obtain a brief overview of observational evidence regarding the association between WMTs and psychiatric disorders, we conducted a literature search on PubMed for articles published from August 2019 to August 2024. The keywords included \"white matter tracts,\" \"DTI,\" \"Schizophrenia,\" \"Bipolar disorder,\" \"Cannabis use disorder,\" \"Opioid use disorder,\" \"Alcohol use disorder,\" \"Post-traumatic stress disorder,\" \"Major depressive disorder,\" \"Autism disorder,\" \"Tourette syndrome,\" and \"Attention deficit hyperactivity disorder.\" Subsequently, we collected the observational findings of varying parameter changes in different WMTs across various psychiatric disorders.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by the National Natural Science Foundation of China (no. 82371499).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYu YF, Yuan K and Ai SZ proposed the topic and main idea. Yu YF, Ai SZ and Yuan K were responsible for data acquisition, analysis, or interpretation of data. Yu YF wrote the initial draft. Yuan K, Ai SZ, Jia TY, Lin X, Chang SH, Bao YP, Sun J, Gao T and Shi J commented on and revised the manuscript. All authors contributed to the final draft of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article and its additional files. The code utilized in this study can be obtained through email inquiry or download from link: https://github.com/Yifan-xyy/Code_for_MR.git.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal incidence: prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. \u003cb\u003e403\u003c/b\u003e, 2133\u0026ndash;2161 (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemontis, D., et al.: Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat. 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Brain Res. \u003cb\u003e598\u003c/b\u003e, 143\u0026ndash;153 (1992)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6505046/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6505046/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhite matter tracts (WMTs), which are responsible for information transmission in the brain, are closely associated with the onset, development, and prognosis of psychiatric disorders, yet the underlying causal mechanisms of their associations remain largely unclear. Thus, we employed two-sample bidirectional Mendelian randomization (MR) to explore the causality between WMTs and 10 psychiatric disorders. The sample sizes of summary-level datasets were ranged from 14,307 to 1,222,882. We found that changes in WMTs are associated with the risk of 8 types of psychiatric disorders, one standard deviation change in WMTs can increase or reduce the risk of psychiatric disorders by 2.2\u0026ndash;71.4%. In the reverse MR analysis, we discovered that alcohol use disorder also increases the probability of specific WMT abnormalities. Our study provides novel insights into the potential causal association between WMTs and psychiatric disorders, indicating that specific characteristics of WMTs may serve as potential biomarkers for psychiatric disorders.\u003c/p\u003e","manuscriptTitle":"Causal Relationship between White Matter Tracts and Psychiatric Disorders: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 15:45:02","doi":"10.21203/rs.3.rs-6505046/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"51d3226a-2e5a-48b3-9a31-daf5e2db9b53","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":48098023,"name":"Biological sciences/Computational biology and bioinformatics/Genome informatics"},{"id":48098024,"name":"Health sciences/Risk factors"},{"id":48098025,"name":"Health sciences/Diseases/Psychiatric disorders"}],"tags":[],"updatedAt":"2025-08-05T16:00:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-08 15:45:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6505046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6505046","identity":"rs-6505046","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}