Induced pluripotent stem cell line generated from a patient with differences in sex development (DSD) and multiple genetic variants including a large deletion in NR5A1.

The nuclear receptor subfamily 5, Group A, Member 1 (NR5A1) gene encodes steroidogenic factor 1 (SF1), which is necessary for development of steroid hormone-producing tissues including the gonad and adrenal gland. An induced pluripotent stem cell line (iPSC) LCHi002-B was generated from a participant with differences (disorders) of sex development (DSD) and multiple genetic variants including a large deletion in NR5A1, and three single nucleotide changes in DYNC2H1, PDE4D, and ZFPM2. The line presented typical morphology, expressed stem cell markers, differentiated into three germ layers, had normal karyotype, was mycoplasma-free, and carried mutations in NR5A1, DYNC2H1, PDE4D, and ZFPM2.

Exome sequencing of fetuses with congenital diaphragmatic hernia supports a causal role for NR2F2, PTPN11, and WT1 variants.

Background: To identify genes associated with congenital diaphragmatic hernia (CDH) to help understand the etiology and inform prognosis.

Methods: We performed exome sequencing on fetuses with CDH and their parents to identify rare genetic variants likely to mediate risk. We reviewed prenatal characteristics and neonatal outcomes.

Generation of two human induced pluripotent stem cell lines from a patient with complete androgen insensitivity syndrome with a hemizygous single nucleotide variant in the androgen receptor (AR) gene.

Complete Androgen Insensitivity Syndrome (CAIS) is a difference of sex development (DSD) caused by loss of function of the androgen receptor (AR) gene. Patients typically identify as female and have a 46,XY karyotype. Two induced pluripotent stem cell lines (iPSCs), LCHi001-A and LCHi001-B, were generated from a participant with CAIS with AR mutation: c.

Clinical impact of splicing in neurodevelopmental disorders.

Clinical exome sequencing is frequently used to identify gene-disrupting variants in individuals with neurodevelopmental disorders. While splice-disrupting variants are known to contribute to these disorders, clinical interpretation of cryptic splice variants outside of the canonical splice site has been challenging. Here, we discuss papers that improve such detection.

Predicting Splicing from Primary Sequence with Deep Learning.

The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population.

Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder.

Whole-genome sequencing (WGS) has facilitated the first genome-wide evaluations of the contribution of de novo noncoding mutations to complex disorders. Using WGS, we identified 255,106 de novo mutations among sample genomes from members of 1902 quartet families in which one child, but not a sibling or their parents, was affected by autism spectrum disorder (ASD). In contrast to coding mutations, no noncoding functional annotation category, analyzed in isolation, was significantly associated with ASD.

An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder.

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories.