A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function

Nat Genet

The function of the majority of genes in the mouse and human genomes remains unknown. The mouse embryonic stem cell knockout resource provides a basis for the characterization of relationships between genes and phenotypes. The EUMODIC consortium developed and validated robust methodologies for the broad-based phenotyping of knockouts through a pipeline comprising 20 disease-oriented platforms. We developed new statistical methods for pipeline design and data analysis aimed at detecting reproducible phenotypes with high power. We acquired phenotype data from 449 mutant alleles, representing 320 unique genes, of which half had no previous functional annotation. We captured data from over 27,000 mice, finding that 83% of the mutant lines are phenodeviant, with 65% demonstrating pleiotropy. Surprisingly, we found significant differences in phenotype annotation according to zygosity. New phenotypes were uncovered for many genes with previously unknown function, providing a powerful basis for hypothesis generation and further investigation in diverse systems.Comment in : Genetic differential calculus. [Nat Genet. 2015] Comment in : Scaling up phenotyping studies. [Nat Biotechnol. 2015

Genome-wide association study of germline variants and breast cancer-specific mortality

BACKGROUND: We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry. METHODS: Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10

The Major Depressive Disorder Hierarchy: Rasch Analysis of 6 items of the Hamilton Depression Scale Covering the Continuum of Depressive Syndrome

<div><p>Objectives</p><p>Melancholic features of depression (MFD) seem to be a unidimensional group of signs and symptoms. However, little importance has been given to the evaluation of what features are related to a more severe disorder. That is, what are the MFD that appear only in the most depressed patients. We aim to demonstrate how each MFD is related to the severity of the major depressive disorder.</p><p>Methods</p><p>We evaluated both the Hamilton depression rating scale (HDRS-17) and its 6-item melancholic subscale (HAM-D6) in 291 depressed inpatients using Rasch analysis, which computes the severity of each MFD. Overall measures of model fit were mean (±SD) of items and persons residual = 0 (±1); low χ² value; p>0.01.</p><p>Results</p><p>For the HDRS-17 model fit, mean (±SD) of item residuals = 0.35 (±1.4); mean (±SD) of person residuals = -0.15 (±1.09); χ² = 309.74; p<0.00001. For the HAM-D6 model fit, mean (±SD) of item residuals = 0.5 (±0.86); mean (±SD) of person residuals = 0.15 (±0.91); χ² = 56.13; p = 0.196. MFD ordered by crescent severity were depressed mood, work and activities, somatic symptoms, psychic anxiety, guilt feelings, and psychomotor retardation.</p><p>Conclusions</p><p>Depressed mood is less severe, while guilt feelings and psychomotor retardation are more severe MFD in a psychiatric hospitalization. Understanding depression as a continuum of symptoms can improve the understanding of the disorder and may improve its perspective of treatment.</p></div

Genome-wide association study of germline variants and breast cancer-specific mortality

Abstract Background: We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry. Methods: Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP). Results: We did not find any variant associated with breast cancer-specific mortality at P &lt; 5 × 10−8. For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10−7, hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84–0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10−7, HR = 1.27, 95% CI = 1.16–1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster. Conclusions: We uncovered germline variants on chromosome 7 at BFDP &lt; 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients