Identification of autosomal recessive disease loci using out-bred nuclear families

A truncating mutation in CEP55 is the likely cause of MARCH, a novel syndrome affecting neuronal mitosis

Background

Hydranencephaly is a congenital anomaly leading to replacement of the cerebral hemispheres with a fluid-filled cyst. The goals of this work are to describe a novel autosomal-recessive syndrome that includes hydranencephaly (multinucleated neurons, anhydramnios, renal dysplasia, cerebellar hypoplasia and hydranencephaly (MARCH)); to identify its genetic cause(s) and to provide functional insight into pathomechanism.

Methods

We used homozygosity mapping and exome sequencing to identify recessive mutations in a single family with three affected fetuses. Immunohistochemistry, RT-PCR and imaging in cell lines, and zebrafish models, were used to explore the function of the gene and the effect of the mutation.

Results

We identified a homozygous nonsense mutation in CEP55 segregating with MARCH. Testing the effect of this allele on patient-derived cells indicated both a reduction of the overall CEP55 message and the production of a message that likely gives rise to a truncated protein. Suppression or ablation of cep55l in zebrafish embryos recapitulated key features of MARCH, most notably renal dysplasia, cerebellar hypoplasia and craniofacial abnormalities. These phenotypes could be rescued by full-length but not truncated human CEP55 message. Finally, we expressed the truncated form of CEP55 in human cells, where we observed a failure of truncated protein to localise to the midbody, leading to abscission failure and multinucleated daughter cells.

Conclusions

CEP55 loss of function mutations likely underlie MARCH, a novel multiple congenital anomaly syndrome. This association expands the involvement of centrosomal proteins in human genetic disorders by highlighting a role in midbody function.

Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data

Autozygosity mapping is a powerful technique for the identification of rare, autosomal recessive, disease‐causing genes. The ease with which this category of disease gene can be identified has greatly increased through the availability of genome‐wide SNP genotyping microarrays and subsequently of exome sequencing. Although these methods have simplified the generation of experimental data, its analysis, particularly when disparate data types must be integrated, remains time consuming. Moreover, the huge volume of sequence variant data generated from next generation sequencing experiments opens up the possibility of using these data instead of microarray genotype data to identify disease loci. To allow these two types of data to be used in an integrated fashion, we have developed AgileVCFMapper, a program that performs both the mapping of disease loci by SNP genotyping and the analysis of potentially deleterious variants using exome sequence variant data, in a single step. This method does not require microarray SNP genotype data, although analysis with a combination of microarray and exome genotype data enables more precise delineation of disease loci, due to superior marker density and distribution.

Simple and efficient identification of rare recessive pathologically important sequence variants from next generation exome sequence data

Massively parallel ("next generation") DNA sequencing (NGS) has quickly become the method of choice for seeking pathogenic mutations in rare uncharacterized monogenic diseases. Typically, before DNA sequencing, protein-coding regions are enriched from patient genomic DNA, representing either the entire genome ("exome sequencing") or selected mapped candidate loci. Sequence variants, identified as differences between the patient's and the human genome reference sequences, are then filtered according to various quality parameters. Changes are screened against datasets of known polymorphisms, such as dbSNP and the 1000 Genomes Project, in the effort to narrow the list of candidate causative variants. An increasing number of commercial services now offer to both generate and align NGS data to a reference genome. This potentially allows small groups with limited computing infrastructure and informatics skills to utilize this technology. However, the capability to effectively filter and assess sequence variants is still an important bottleneck in the identification of deleterious sequence variants in both research and diagnostic settings. We have developed an approach to this problem comprising a user-friendly suite of programs that can interactively analyze, filter and screen data from enrichment-capture NGS data. These programs ("Agile Suite") are particularly suitable for small-scale gene discovery or for diagnostic analysis.