ABSTRACT
Forward genetic studies use meiotic mapping to adduce evidence that a particular mutation, normally induced by a germline mutagen, is causative of a particular phenotype. Particularly in small pedigrees, cosegregation of multiple mutations, occasional unawareness of mutations, and paucity of homozygotes may lead to erroneous declarations of cause and effect. We sought to improve the identification of mutations causing immune phenotypes in mice by creating Candidate Explorer (CE), a machine-learning software program that integrates 67 features of genetic mapping data into a single numeric score, mathematically convertible to the probability of verification of any putative mutation-phenotype association. At this time, CE has evaluated putative mutation-phenotype associations arising from screening damaging mutations in â¼55% of mouse genes for effects on flow cytometry measurements of immune cells in the blood. CE has therefore identified more than half of genes within which mutations can be causative of flow cytometric phenovariation in Mus musculus The majority of these genes were not previously known to support immune function or homeostasis. Mouse geneticists will find CE data informative in identifying causative mutations within quantitative trait loci, while clinical geneticists may use CE to help connect causative variants with rare heritable diseases of immunity, even in the absence of linkage information. CE displays integrated mutation, phenotype, and linkage data, and is freely available for query online.
Subject(s)
Germ-Line Mutation/genetics , Leukocytes/metabolism , Machine Learning , Meiosis/genetics , Algorithms , Animals , Automation , Female , Flow Cytometry , Male , Mice, Inbred C57BL , Phenotype , Probability , Reproducibility of Results , SoftwareABSTRACT
OBJECTIVE: Apathy is a common behavioral symptom of Huntington disease (HD). This systematic review describes current evidence on the pathophysiology, assessment, and frequency of apathy in HD. METHODS: This systematic review was conducted in accordance with PRISMA guidelines. Using a comprehensive search strategy, the investigators searched the MEDLINE, Embase, and PsycINFO databases. All studies that evaluated apathy in HD patients with a valid scale and reported apathy frequency or scores were included. Apathy scores were analyzed by mean or standardized mean differences in accordance with Cochrane guidelines. RESULTS: A total of 1,085 records were screened and 80 studies were ultimately included. The Problem Behaviors Assessment-Short was the most frequently used apathy assessment tool. Apathy frequency generally ranged from 10%-33% in premanifest HD to 24%-76% in manifest HD. A meta-analysis of 5,311 records of patients with premanifest HD showed significantly higher apathy scores, with a standardized mean difference of 0.41 (CI=0.29-0.52; p<0.001). A comparison of 1,247 patients showed significantly higher apathy scores in manifest than premanifest HD, with a mean difference of 1.87 (CI=1.48-2.26; p<0.001). There was evidence of involvement of various cortical and subcortical brain regions in HD patients with apathy. CONCLUSIONS: Apathy was more frequent among individuals with premanifest HD compared with those in a control group and among individuals with manifest HD compared with those with premanifest HD. Considering the complexity and unique pattern of development in neurodegenerative disease, further studies are required to explore the pathophysiology of apathy in HD.