Variants in STXBP3 are Associated with Very Early Onset Inflammatory Bowel Disease, Bilateral Sensorineural Hearing Loss and Immune Dysregulation.

Background And Aims: Very early onset inflammatory bowel disease [VEOIBD] is characterized by intestinal inflammation affecting infants and children less than 6 years of age. To date, over 60 monogenic aetiologies of VEOIBD have been identified, many characterized by highly penetrant recessive or dominant variants in underlying immune and/or epithelial pathways. We sought to identify the genetic cause of VEOIBD in a subset of patients with a unique clinical presentation.

Lung gene expression and single cell analyses reveal two subsets of idiopathic pulmonary fibrosis (IPF) patients associated with different pathogenic mechanisms.

Idiopathic pulmonary fibrosis is a progressive and debilitating lung disease with large unmet medical need and few treatment options. We describe an analysis connecting single cell gene expression with bulk gene expression-based subsetting of patient cohorts to identify IPF patient subsets with different underlying pathogenesis and cellular changes. We reproduced earlier findings indicating the existence of two major subsets in IPF and showed that these subsets display different alterations in cellular composition of the lung.

PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants.

Functional characterization of the noncoding genome is essential for biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring), which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. PINES enables analyses to be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest.

Analysis of Shared Haplotypes amongst Palauans Maps Loci for Psychotic Disorders to 4q28 and 5q23-q31.

To localize genetic variation affecting risk for psychotic disorders in the population of Palau, we genotyped DNA samples from 203 Palauan individuals diagnosed with psychotic disorders, broadly defined, and 125 control subjects using a genome-wide single nucleotide polymorphism array. Palau has unique features advantageous for this study: due to its population history, Palauans are substantially interrelated; affected individuals often, but not always, cluster in families; and we have essentially complete ascertainment of affected individuals. To localize risk variants to genomic regions, we evaluated long-shared haplotypes, ≥10 Mb, identifying clusters of affected individuals who share such haplotypes.

A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies.

One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects.

Most genetic risk for autism resides with common variation.

A key component of genetic architecture is the allelic spectrum influencing trait variability. For autism spectrum disorder (herein termed autism), the nature of the allelic spectrum is uncertain. Individual risk-associated genes have been identified from rare variation, especially de novo mutations.

Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls.

We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers.