To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.

High-throughput sequencing of related individuals has become an important tool for studying human disease. However, owing to technical complexity and lack of available tools, most pedigree-based sequencing studies rely on an ad hoc combination of suboptimal analyses. Here we present pedigree-VAAST (pVAAST), a disease-gene identification tool designed for high-throughput sequence data in pedigrees. pVAAST uses a sequence-based model to perform variant and gene-based linkage analysis. Linkage information is then combined with functional prediction and rare variant case-control association information in a unified statistical framework. pVAAST outperformed linkage and rare-variant association tests in simulations and identified disease-causing genes from whole-genome sequence data in three human pedigrees with dominant, recessive and de novo inheritance patterns. The approach is robust to incomplete penetrance and locus heterogeneity and is applicable to a wide variety of genetic traits. pVAAST maintains high power across studies of monogenic, high-penetrance phenotypes in a single pedigree to highly polygenic, common phenotypes involving hundreds of pedigrees.

The determination of the relationship between a pair of individuals is a fundamental application of genetics. Previously, we and others have demonstrated that identity-by-descent (IBD) information generated from high-density single-nucleotide polymorphism (SNP) data can greatly improve the power and accuracy of genetic relationship detection. Whole-genome sequencing (WGS) marks the final step in increasing genetic marker density by assaying all single-nucleotide variants (SNVs), and thus has the potential to further improve relationship detection by enabling more accurate detection of IBD segments and more precise resolution of IBD segment boundaries. However, WGS introduces new complexities that must be addressed in order to achieve these improvements in relationship detection. To evaluate these complexities, we estimated genetic relationships from WGS data for 1490 known pairwise relationships among 258 individuals in 30 families along with 46 population samples as controls. We identified several genomic regions with excess pairwise IBD in both the pedigree and control datasets using three established IBD methods: GERMLINE, fastIBD, and ISCA. These spurious IBD segments produced a 10-fold increase in the rate of detected false-positive relationships among controls compared to high-density microarray datasets. To address this issue, we developed a new method to identify and mask genomic regions with excess IBD. This method, implemented in ERSA 2.0, fully resolved the inflated cryptic relationship detection rates while improving relationship estimation accuracy. ERSA 2.0 detected all 1st through 6th degree relationships, and 55% of 9th through 11th degree relationships in the 30 families. We estimate that WGS data provides a 5% to 15% increase in relationship detection power relative to high-density microarray data for distant relationships. Our results identify regions of the genome that are highly problematic for IBD mapping and introduce new software to accurately detect 1st through 9th degree relationships from whole-genome sequence data.

Frontotemporal dementia and amyotrophic lateral sclerosis are clinically and pathologically overlapping disorders with shared genetic causes. We previously identified a disease locus on chromosome 16p12.1-q12.2 with genome-wide significant linkage in a large European Australian family with autosomal dominant inheritance of frontotemporal dementia and amyotrophic lateral sclerosis and no mutation in known amyotrophic lateral sclerosis or dementia genes. Here we demonstrate the segregation of a novel missense variant in CYLD (c.2155A>G, p.M719V) within the linkage region as the genetic cause of disease in this family. Immunohistochemical analysis of brain tissue from two CYLD p.M719V mutation carriers showed widespread glial CYLD immunoreactivity. Primary mouse neurons transfected with CYLDM719V exhibited increased cytoplasmic localization of TDP-43 and shortened axons. CYLD encodes a lysine 63 deubiquitinase and CYLD cutaneous syndrome, a skin tumour disorder, is caused by mutations that lead to reduced deubiquitinase activity. In contrast with CYLD cutaneous syndrome-causative mutations, CYLDM719V exhibited significantly increased lysine 63 deubiquitinase activity relative to the wild-type enzyme (paired Wilcoxon signed-rank test P = 0.005). Overexpression of CYLDM719V in HEK293 cells led to more potent inhibition of the cell signalling molecule NF-κB and impairment of autophagosome fusion to lysosomes, a key process in autophagy. Although CYLD mutations appear to be rare, CYLD’s interaction with at least three other proteins encoded by frontotemporal dementia and/or amyotrophic lateral sclerosis genes (TBK1, OPTN and SQSTM1) suggests that it may play a central role in the pathogenesis of these disorders. Mutations in several frontotemporal dementia and amyotrophic lateral sclerosis genes, including TBK1, OPTN and SQSTM1, result in a loss of autophagy function. We show here that increased CYLD activity also reduces autophagy function, highlighting the importance of autophagy regulation in the pathogenesis of frontotemporal dementia and amyotrophic lateral sclerosis.

Adeno-associated virus (AAV) gene therapy for neurological diseases was revolutionized by the discovery that AAV9 crosses the blood-brain barrier (BBB) after systemic administration. Transformative results have been documented in various inherited diseases, but overall neuronal transduction efficiency is relatively low. The recent development of AAV-PHP.B with *60-fold higher efficiency than AAV9 in transducing the adult mouse brain was the major first step toward acquiring the ability to deliver genes to the majority of cells in the central nervous system (CNS). However, little is known about the mechanism utilized by AAV to cross the BBB, and how it may diverge across species. In this study, we show that AAV-PHP.

Approximately half of the familial aggregation of breast cancer remains unexplained. A multiple-case breast cancer family exome sequencing study identified three likely pathogenic mutations in RINT1 (NM_021930.4) not present in public sequencing databases: RINT1 c.343C>T (p.Q115X), c.1132_1134del (p.M378del) and c.1207G>T (p.D403Y). Based on this finding, a population-based case-control mutation-screening study was conducted and identified 29 carriers of rare (MAF < 0.5%), likely pathogenic variants: 23 in 1,313 early-onset breast cancer cases and 6 in 1,123 frequency-matched controls (OR=3.24, 95%CI 1.29-8.17; p=0.013). RINT1 mutation screening of probands from 798 multiple-case breast cancer families identified 4additional carriers of rare genetic variants. Analysis of the incidence of first primary cancers in families of women in RINT1-mutation carrying families estimated that carriers were at increased risks of Lynch syndrome-spectrum cancers (SIR 3.35, 95% CI 1.7-6.0; P=0.005), particularly for relatives diagnosed with cancer under age 60 years (SIR 10.9, 95%CI 4.7-21; P=0.0003).

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The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.