Rare-variant collapsing analyses for complex traits: guidelines and applications.

The first phase of genome-wide association studies (GWAS) assessed the role of common variation in human disease. Advances optimizing and economizing high-throughput sequencing have enabled a second phase of association studies that assess the contribution of rare variation to complex disease in all protein-coding genes. Unlike the early microarray-based studies, sequencing-based studies catalogue the full range of genetic variation, including the evolutionarily youngest forms. Although the experience with common variants helped establish relevant standards for genome-wide studies, the analysis of rare variation introduces several challenges that require novel analysis approaches.

Targeted long-read sequencing identifies missing disease-causing variation.

Despite widespread clinical genetic testing, many individuals with suspected genetic conditions lack a precise diagnosis, limiting their opportunity to take advantage of state-of-the-art treatments. In some cases, testing reveals difficult-to-evaluate structural differences, candidate variants that do not fully explain the phenotype, single pathogenic variants in recessive disorders, or no variants in genes of interest. Thus, there is a need for better tools to identify a precise genetic diagnosis in individuals when conventional testing approaches have been exhausted. We performed targeted long-read sequencing (T-LRS) using adaptive sampling on the Oxford Nanopore platform on 40 individuals, 10 of whom lacked a complete molecular diagnosis. We computationally targeted up to 151 Mbp of sequence per individual and searched for pathogenic substitutions, structural variants, and methylation differences using a single data source. We detected all genomic aberrations-including single-nucleotide variants, copy number changes, repeat expansions, and methylation differences-identified by prior clinical testing. In 8/8 individuals with complex structural rearrangements, T-LRS enabled more precise resolution of the mutation, leading to changes in clinical management in one case. In ten individuals with suspected Mendelian conditions lacking a precise genetic diagnosis, T-LRS identified pathogenic or likely pathogenic variants in six and variants of uncertain significance in two others. T-LRS accurately identifies pathogenic structural variants, resolves complex rearrangements, and identifies Mendelian variants not detected by other technologies. T-LRS represents an efficient and cost-effective strategy to evaluate high-priority genes and regions or complex clinical testing results.

Correcting signal biases and detecting regulatory elements in STARR-seq data.

High-throughput reporter assays such as self-transcribing active regulatory region sequencing (STARR-seq) have made it possible to measure regulatory element activity across the entire human genome at once. The resulting data, however, present substantial analytical challenges. Here, we identify technical biases that explain most of the variance in STARR-seq data. We then develop a statistical model to correct those biases and to improve detection of regulatory elements. This approach substantially improves precision and recall over current methods, improves detection of both activating and repressive regulatory elements, and controls for false discoveries despite strong local correlations in signal.

Focused goodness of fit tests for gene set analyses.

Gene set-based signal detection analyses are used to detect an association between a trait and a set of genes by accumulating signals across the genes in the gene set. Since signal detection is concerned with identifying whether any of the genes in the gene set are non-null, a goodness-of-fit (GOF) test can be used to compare whether the observed distribution of gene-level tests within the gene set agrees with the theoretical null distribution. Here, we present a flexible gene set-based signal detection framework based on tail-focused GOF statistics. We show that the power of the various statistics in this framework depends critically on two parameters: the proportion of genes within the gene set that are non-null and the degree of separation between the null and alternative distributions of the gene-level tests. We give guidance on which statistic to choose for a given situation and implement the methods in a fast and user-friendly R package, wHC (https://github.com/mqzhanglab/wHC). Finally, we apply these methods to a whole exome sequencing study of amyotrophic lateral sclerosis.

Ultra-rare genetic variation in relapsing polychondritis: a whole-exome sequencing study.

OBJECTIVE: Relapsing polychondritis (RP) is a systemic inflammatory disease of unknown aetiology. The objective of this study was to examine the contribution of rare genetic variations to RP. METHODS: We performed a case-control exome-wide rare variant association analysis that included 66 unrelated European American cases with RP and 2923 healthy controls (HC). Gene-level collapsing analysis was performed using Firth's logistics regression. Exploratory pathway analysis was performed using three different methods: Gene Set Enrichment Analysis, sequence kernel association test and higher criticism test. Plasma DCBLD2 levels were measured in patients with RP and HC using ELISA. RESULTS: In the collapsing analysis, RP was associated with a significantly higher burden of ultra-rare damaging variants in the DCBLD2 gene (7.6% vs 0.1%, unadjusted OR=79.8, p=2.93×10-7). Plasma DCBLD2 protein levels were significantly higher in RP than in HC (median 4.06 ng/µL vs 0.05 ng/µL, p<0.001). The pathway analysis revealed a statistically significant enrichment of genes in the tumour necrosis factor signalling pathway driven by rare damaging variants in RELB, RELA and REL using higher criticism test weighted by eigenvector centrality. CONCLUSIONS: This study identified specific rare variants in the DCBLD2 gene as a putative genetic risk factor for RP. These findings should be validated in additional patients with RP and supported by future functional experiments.

The genetics of neuropsychiatric diseases: looking in and beyond the exome.

Next-generation sequencing, which allows genome-wide detection of rare and de novo mutations, is transforming neuropsychiatric disease genetics through identifying on an unprecedented scale genes and protein-coding mutations that confer risk. Although understanding how regulatory variants influence risk remains a challenge, we are likely transitioning into a phase of neuropsychiatric disease genetics in which the rate-limiting step may no longer be gene discovery. Instead, the future will concentrate more on the biological and clinical translation of the torrent of specific risk mutations identified through next-generation sequencing. Here, we review the recent progress that resulted specifically from exome sequencing and emphasize the need for rigorous statistical evaluation of the expanding data sets, as well as expanded functional analysis of implicated proteins and mutations. Then, we introduce some of the expected opportunities and challenges investigators face when moving beyond the exome. Finally, we briefly highlight the challenge of deriving translational benefit from the progress in genetics.

Causal Genetic Variants in Stillbirth.

BACKGROUND: In the majority of cases, the cause of stillbirth remains unknown despite detailed clinical and laboratory evaluation. Approximately 10 to 20% of stillbirths are attributed to chromosomal abnormalities. However, the causal nature of single-nucleotide variants and small insertions and deletions in exomes has been understudied. METHODS: We generated exome sequencing data for 246 stillborn cases and followed established guidelines to identify causal variants in disease-associated genes. These genes included those that have been associated with stillbirth and strong candidate genes. We also evaluated the contribution of 18,653 genes in case-control analyses stratified according to the degree of depletion of functional variation (described here as "intolerance" to variation). RESULTS: We identified molecular diagnoses in 15 of 246 cases of stillbirth (6.1%) involving seven genes that have been implicated in stillbirth and six disease genes that are good candidates for phenotypic expansion. Among the cases we evaluated, we also found an enrichment of loss-of-function variants in genes that are intolerant to such variation in the human population (odds ratio, 2.15; 95% confidence interval [CI], 1.46 to 3.06). Loss-of-function variants in intolerant genes were concentrated in genes that have not been associated with human disease (odds ratio, 2.22; 95% CI, 1.41 to 3.34), findings that differ from those in two postnatal clinical populations that were also evaluated in this study. CONCLUSIONS: Our findings establish the diagnostic utility of clinical exome sequencing to evaluate the role of small genomic changes in stillbirth. The strength of the novel risk signal (as generated through the stratified analysis) was similar to that in known disease genes, which indicates that the genetic cause of stillbirth remains largely unknown. (Funded by the Institute for Genomic Medicine.).

Loss of tubulin deglutamylase CCP1 causes infantile-onset neurodegeneration.

A set of glutamylases and deglutamylases controls levels of tubulin polyglutamylation, a prominent post-translational modification of neuronal microtubules. Defective tubulin polyglutamylation was first linked to neurodegeneration in the Purkinje cell degeneration (pcd) mouse, which lacks deglutamylase CCP1, displays massive cerebellar atrophy, and accumulates abnormally glutamylated tubulin in degenerating neurons. We found biallelic rare and damaging variants in the gene encoding CCP1 in 13 individuals with infantile-onset neurodegeneration and confirmed the absence of functional CCP1 along with dysregulated tubulin polyglutamylation. The human disease mainly affected the cerebellum, spinal motor neurons, and peripheral nerves. We also demonstrate previously unrecognized peripheral nerve and spinal motor neuron degeneration in pcd mice, which thus recapitulated key features of the human disease. Our findings link human neurodegeneration to tubulin polyglutamylation, entailing this post-translational modification as a potential target for drug development for neurodegenerative disorders.

Improved Pathogenic Variant Localization via a Hierarchical Model of Sub-regional Intolerance.

Different parts of a gene can be of differential importance to development and health. This regional heterogeneity is also apparent in the distribution of disease-associated mutations, which often cluster in particular regions of disease-associated genes. The ability to precisely estimate functionally important sub-regions of genes will be key in correctly deciphering relationships between genetic variation and disease. Previous methods have had some success using standing human variation to characterize this variability in importance by measuring sub-regional intolerance, i.e., the depletion in functional variation from expectation within a given region of a gene. However, the ability to precisely estimate local intolerance was restricted by the fact that only information within a given sub-region is used, leading to instability in local estimates, especially for small regions. We show that borrowing information across regions using a Bayesian hierarchical model stabilizes estimates, leading to lower variability and improved predictive utility. Specifically, our approach more effectively identifies regions enriched for ClinVar pathogenic variants. We also identify significant correlations between sub-region intolerance and the distribution of pathogenic variation in disease-associated genes, with AUCs for classifying de novo missense variants in Online Mendelian Inheritance in Man (OMIM) genes of up to 0.86 using exonic sub-regions and 0.91 using sub-regions defined by protein domains. This result immediately suggests that considering the intolerance of regions in which variants are found may improve diagnostic interpretation. We also illustrate the utility of integrating regional intolerance into gene-level disease association tests with a study of known disease-associated genes for epileptic encephalopathy.

A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors.

MOTIVATION: Conservation is broadly used to identify biologically important (epi)genomic regions. In the case of tumor growth, preferential conservation of DNA methylation can be used to identify areas of particular functional importance to the tumor. However, reliable assessment of methylation conservation based on multiple tissue samples per patient requires the decomposition of methylation variation at multiple levels. RESULTS: We developed a Bayesian hierarchical model that allows for variance decomposition of methylation on three levels: between-patient normal tissue variation, between-patient tumor-effect variation and within-patient tumor variation. We then defined a model-based conservation score to identify loci of reduced within-tumor methylation variation relative to between-patient variation. We fit the model to multi-sample methylation array data from 21 colorectal cancer (CRC) patients using a Monte Carlo Markov Chain algorithm (Stan). Sets of genes implicated in CRC tumorigenesis exhibited preferential conservation, demonstrating the model's ability to identify functionally relevant genes based on methylation conservation. A pathway analysis of preferentially conserved genes implicated several CRC relevant pathways and pathways related to neoantigen presentation and immune evasion. Our findings suggest that preferential methylation conservation may be used to identify novel gene targets that are not consistently mutated in CRC. The flexible structure makes the model amenable to the analysis of more complex multi-sample data structures. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in the NCBI GEO Database, under accession code GSE166212. The R analysis code is available at https://github.com/kevin-murgas/DNAmethylation-hierarchicalmodel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Coding and noncoding variants in EBF3 are involved in HADDS and simplex autism.

BACKGROUND: Previous research in autism and other neurodevelopmental disorders (NDDs) has indicated an important contribution of protein-coding (coding) de novo variants (DNVs) within specific genes. The role of de novo noncoding variation has been observable as a general increase in genetic burden but has yet to be resolved to individual functional elements. In this study, we assessed whole-genome sequencing data in 2671 families with autism (discovery cohort of 516 families, replication cohort of 2155 families). We focused on DNVs in enhancers with characterized in vivo activity in the brain and identified an excess of DNVs in an enhancer named hs737. RESULTS: We adapted the fitDNM statistical model to work in noncoding regions and tested enhancers for excess of DNVs in families with autism. We found only one enhancer (hs737) with nominal significance in the discovery (p = 0.0172), replication (p = 2.5 × 10-3), and combined dataset (p = 1.1 × 10-4). Each individual with a DNV in hs737 had shared phenotypes including being male, intact cognitive function, and hypotonia or motor delay. Our in vitro assessment of the DNVs showed they all reduce enhancer activity in a neuronal cell line. By epigenomic analyses, we found that hs737 is brain-specific and targets the transcription factor gene EBF3 in human fetal brain. EBF3 is genome-wide significant for coding DNVs in NDDs (missense p = 8.12 × 10-35, loss-of-function p = 2.26 × 10-13) and is widely expressed in the body. Through characterization of promoters bound by EBF3 in neuronal cells, we saw enrichment for binding to NDD genes (p = 7.43 × 10-6, OR = 1.87) involved in gene regulation. Individuals with coding DNVs have greater phenotypic severity (hypotonia, ataxia, and delayed development syndrome [HADDS]) in comparison to individuals with noncoding DNVs that have autism and hypotonia. CONCLUSIONS: In this study, we identify DNVs in the hs737 enhancer in individuals with autism. Through multiple approaches, we find hs737 targets the gene EBF3 that is genome-wide significant in NDDs. By assessment of noncoding variation and the genes they affect, we are beginning to understand their impact on gene regulatory networks in NDDs.

Incorporating external information to improve sparse signal detection in rare-variant gene-set-based analyses.

Gene-set analyses are used to assess whether there is any evidence of association with disease among a set of biologically related genes. Such an analysis typically treats all genes within the sets similarly, even though there is substantial, external, information concerning the likely importance of each gene within each set. For example, for traits that are under purifying selection, we would expect genes showing extensive genic constraint to be more likely to be trait associated than unconstrained genes. Here we improve gene-set analyses by incorporating such external information into a higher-criticism-based signal detection analysis. We show that when this external information is predictive of whether a gene is associated with disease, our approach can lead to a significant increase in power. Further, our approach is particularly powerful when the signal is sparse, that is when only a small number of genes within the set are associated with the trait. We illustrate our approach with a gene-set analysis of amyotrophic lateral sclerosis (ALS) and implicate a number of gene-sets containing SOD1 and NEK1 as well as showing enrichment of small p values for gene-sets containing known ALS genes. We implement our approach in the R package wHC.

Bayesian estimation of genetic regulatory effects in high-throughput reporter assays.

MOTIVATION: High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of assay results, particularly in the case of rare variants captured from patient DNA. RESULTS: We describe a Bayesian hierarchical model, called Bayesian Inference of Regulatory Differences, which integrates prior information and explicitly accounts for variability between experimental replicates. The model produces substantially more accurate predictions than existing methods when allele frequencies are low, which is of clear advantage in the search for disease-causing variants in DNA captured from patient cohorts. Using the model, we demonstrate a clear tradeoff between variant sequencing coverage and numbers of biological replicates, and we show that the use of additional biological replicates decreases variance in estimates of effect size, due to the properties of the Poisson-binomial distribution. We also provide a power and sample size calculator, which facilitates decision making in experimental design parameters. AVAILABILITY AND IMPLEMENTATION: The software is freely available from www.geneprediction.org/bird. The experimental design web tool can be accessed at http://67.159.92.22:8080. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

De novo and inherited private variants in MAP1B in periventricular nodular heterotopia.

Periventricular nodular heterotopia (PVNH) is a malformation of cortical development commonly associated with epilepsy. We exome sequenced 202 individuals with sporadic PVNH to identify novel genetic risk loci. We first performed a trio-based analysis and identified 219 de novo variants. Although no novel genes were implicated in this initial analysis, PVNH cases were found overall to have a significant excess of nonsynonymous de novo variants in intolerant genes (p = 3.27x10-7), suggesting a role for rare new alleles in genes yet to be associated with the condition. Using a gene-level collapsing analysis comparing cases and controls, we identified a genome-wide significant signal driven by four ultra-rare loss-of-function heterozygous variants in MAP1B, including one de novo variant. In at least one instance, the MAP1B variant was inherited from a parent with previously undiagnosed PVNH. The PVNH was frontally predominant and associated with perisylvian polymicrogyria. These results implicate MAP1B in PVNH. More broadly, our findings suggest that detrimental mutations likely arising in immediately preceding generations with incomplete penetrance may also be responsible for some apparently sporadic diseases.

Arfgef1 haploinsufficiency in mice alters neuronal endosome composition and decreases membrane surface postsynaptic GABAA receptors.

ARFGEF1 encodes a guanine exchange factor involved in intracellular vesicle trafficking, and is a candidate gene for childhood genetic epilepsies. To model ARFGEF1 haploinsufficiency observed in a recent Lennox Gastaut Syndrome patient, we studied a frameshift mutation (Arfgef1fs) in mice. Arfgef1fs/+ pups exhibit signs of developmental delay, and Arfgef1fs/+ adults have a significantly decreased threshold to induced seizures but do not experience spontaneous seizures. Histologically, the Arfgef1fs/+ brain exhibits a disruption in the apical lining of the dentate gyrus and altered spine morphology of deep layer neurons. In primary hippocampal neuron culture, dendritic surface and synaptic but not total GABAA receptors (GABAAR) are reduced in Arfgef1fs/+ neurons with an accompanying decrease in the number of GABAAR-containing recycling endosomes in cell body. Arfgef1fs/+ neurons also display differences in the relative ratio of Arf6+:Rab11+:TrfR+ recycling endosomes. Although the GABAAR-containing early endosomes in Arfgef1fs/+ neurons are comparable to wildtype, Arfgef1fs/+ neurons show an increase in the number of GABAAR-containing lysosomes in dendrite and cell body. Together, the altered endosome composition and decreased neuronal surface GABAAR results suggests a mechanism whereby impaired neuronal inhibition leads to seizure susceptibility.

Secondary analysis of case-control association studies: Insights on weighting-based inference motivate a new specification test.

Case-control sampling is frequently used in genetic association studies to examine the relationship between disease and genetic exposures. Such designs usually collect extensive information on phenotypes beyond the primary disease, whose associations with the genetic exposures are also of great interest. Because the cases are over-sampled, appropriate analysis of secondary phenotypes should take into account this biased sampling design. We previously introduced a weighting-based estimator for appropriate secondary analysis, but have not thoroughly explored its statistical properties. In this article, we revisit our previous estimator to offer new insights and methodological extensions. Specifically, we extend our previous estimator and construct its more general form based on generalized least squares (GLS). Such an extension allows us to connect the GLS estimator with the generalized method of moments and motivates a new specification test designed to assess the adequacy of the disease model or the weights. The specification test statistic measures the weighted discrepancy between the case and control subsample estimators, and asymptotically follows a central Chi-squared distribution under correct disease model specification. We illustrate the GLS estimator and specification test using a case-control sample of peripheral arterial disease, and use simulations to further shed light on the operating characteristics of the specification test.

A case-control collapsing analysis identifies epilepsy genes implicated in trio sequencing studies focused on de novo mutations.

Trio exome sequencing has been successful in identifying genes with de novo mutations (DNMs) causing epileptic encephalopathy (EE) and other neurodevelopmental disorders. Here, we evaluate how well a case-control collapsing analysis recovers genes causing dominant forms of EE originally implicated by DNM analysis. We performed a genome-wide search for an enrichment of "qualifying variants" in protein-coding genes in 488 unrelated cases compared to 12,151 unrelated controls. These "qualifying variants" were selected to be extremely rare variants predicted to functionally impact the protein to enrich for likely pathogenic variants. Despite modest sample size, three known EE genes (KCNT1, SCN2A, and STXBP1) achieved genome-wide significance (p<2.68×10-6). In addition, six of the 10 most significantly associated genes are known EE genes, and the majority of the known EE genes (17 out of 25) originally implicated in trio sequencing are nominally significant (p<0.05), a proportion significantly higher than the expected (Fisher's exact p = 2.33×10-17). Our results indicate that a case-control collapsing analysis can identify several of the EE genes originally implicated in trio sequencing studies, and clearly show that additional genes would be implicated with larger sample sizes. The case-control analysis not only makes discovery easier and more economical in early onset disorders, particularly when large cohorts are available, but also supports the use of this approach to identify genes in diseases that present later in life when parents are not readily available.

Exome-Based Rare-Variant Analyses in CKD.

BACKGROUND: Studies have identified many common genetic associations that influence renal function and all-cause CKD, but these explain only a small fraction of variance in these traits. The contribution of rare variants has not been systematically examined. METHODS: We performed exome sequencing of 3150 individuals, who collectively encompassed diverse CKD subtypes, and 9563 controls. To detect causal genes and evaluate the contribution of rare variants we used collapsing analysis, in which we compared the proportion of cases and controls carrying rare variants per gene. RESULTS: The analyses captured five established monogenic causes of CKD: variants in PKD1, PKD2, and COL4A5 achieved study-wide significance, and we observed suggestive case enrichment for COL4A4 and COL4A3. Beyond known disease-associated genes, collapsing analyses incorporating regional variant intolerance identified suggestive dominant signals in CPT2 and several other candidate genes. Biallelic mutations in CPT2 cause carnitine palmitoyltransferase II deficiency, sometimes associated with rhabdomyolysis and acute renal injury. Genetic modifier analysis among cases with APOL1 risk genotypes identified a suggestive signal in AHDC1, implicated in Xia-Gibbs syndrome, which involves intellectual disability and other features. On the basis of the observed distribution of rare variants, we estimate that a two- to three-fold larger cohort would provide 80% power to implicate new genes for all-cause CKD. CONCLUSIONS: This study demonstrates that rare-variant collapsing analyses can validate known genes and identify candidate genes and modifiers for kidney disease. In so doing, these findings provide a motivation for larger-scale investigation of rare-variant risk contributions across major clinical CKD categories.

Genome-wide association study (GWAS) of human host factors influencing viral severity of herpes simplex virus type 2 (HSV-2).

Herpes simplex virus type 2 (HSV-2) is an incurable viral infection with severity ranging from asymptomatic to frequent recurrences. The viral shedding rate has been shown as a reproducible HSV-2 severity end point that correlates with lesion rates. We used a genome-wide association study (GWAS) to investigate the role of common human genetic variation in HSV-2 severity. We performed a GWAS on 223 HSV-2-positive participants of European ancestry. Severity was measured by viral shedding rate, as defined by the percent of days PCR+ for HSV-2 DNA over at least 30 days. Analyses were performed under linear regression models, adjusted for age, sex, and ancestry. There were no genome-wide significant (p < 5E-08) associations with HSV-2 viral shedding rate. The top nonsignificant SNP (rs75932292, p = 6.77E-08) associated with HSV-2 viral shedding was intergenic, with the nearest known biologically interesting gene (ABCA1) ~130 kbp downstream. Several other SNPs approaching significance were in or near genes with viral or neurological associations, including four SNPs in KIF1B. The current study is the first comprehensive genome-wide investigation of human genetic variation in virologic severity of established HSV-2 infection. However, no significant associations were observed with HSV-2 virologic severity, leaving the exact role of human variation in HSV-2 severity unclear.

Mutations of the Sonic Hedgehog Pathway Underlie Hypothalamic Hamartoma with Gelastic Epilepsy.

Hypothalamic hamartoma (HH) with gelastic epilepsy is a well-recognized drug-resistant epilepsy syndrome of early life.(1) Surgical resection allows limited access to the small deep-seated lesions that cause the disease. Here, we report the results of a search for somatic mutations in paired hamartoma- and leukocyte-derived DNA samples from 38 individuals which we conducted by using whole-exome sequencing (WES), chromosomal microarray (CMA), and targeted resequencing (TRS) of candidate genes. Somatic mutations were identified in genes involving regulation of the sonic hedgehog (Shh) pathway in 14/38 individuals (37%). Three individuals had somatic mutations in PRKACA, which encodes a cAMP-dependent protein kinase that acts as a repressor protein in the Shh pathway, and four subjects had somatic mutations in GLI3, an Shh pathway gene associated with HH. In seven other individuals, we identified two recurrent and three single brain-tissue-specific, large copy-number or loss-of-heterozygosity (LOH) variants involving multiple Shh genes, as well as other genes without an obvious biological link to the Shh pathway. The Shh pathway genes in these large somatic lesions include the ligand itself (SHH and IHH), the receptor SMO, and several other Shh downstream pathway members, including CREBBP and GLI2. Taken together, our data implicate perturbation of the Shh pathway in at least 37% of individuals with the HH epilepsy syndrome, consistent with the concept of a developmental pathway brain disease.