Spectrin-based skeleton as an actor in cell signaling

This review focuses on the recent advances in functions of spectrins in non-erythroid cells. We discuss new data concerning the commonly known role of the spectrin-based skeleton in control of membrane organization, stability and shape, and tethering protein mosaics to the cellular motors and to all major filament systems. Particular effort has been undertaken to highlight recent advances linking spectrin to cell signaling phenomena and its participation in signal transduction pathways in many cell types.     

Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits

We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.     

Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs

Schizophrenia is a complex disorder caused by both genetic and environmental factors. Using 9,087 affected individuals, 12,171 controls and 915,354 imputed SNPs from the Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (PGC-SCZ), we estimate that 23% (s.e. = 1%) of variation in liability to schizophrenia is captured by SNPs. We show that a substantial proportion of this variation must be the result of common causal variants, that the variance explained by each chromosome is linearly related to its length (r = 0.89, P = 2.6 × 10(-8)), that the genetic basis of schizophrenia is the same in males and females, and that a disproportionate proportion of variation is attributable to a set of 2,725 genes expressed in the central nervous system (CNS; P = 7.6 × 10(-8)). These results are consistent with a polygenic genetic architecture and imply more individual SNP associations will be detected for this disease as sample size increases.     

Complex reorganization and predominant non-homologous repair following chromosomal breakage in karyotypically balanced germline rearrangements and transgenic integration

We defined the genetic landscape of balanced chromosomal rearrangements at nucleotide resolution by sequencing 141 breakpoints from cytogenetically interpreted translocations and inversions. We confirm that the recently described phenomenon of 'chromothripsis' (massive chromosomal shattering and reorganization) is not unique to cancer cells but also occurs in the germline, where it can resolve to a relatively balanced state with frequent inversions. We detected a high incidence of complex rearrangements (19.2%) and substantially less reliance on microhomology (31%) than previously observed in benign copy-number variants (CNVs). We compared these results to experimentally generated DNA breakage-repair by sequencing seven transgenic animals, revealing extensive rearrangement of the transgene and host genome with similar complexity to human germline alterations. Inversion was the most common rearrangement, suggesting that a combined mechanism involving template switching and non-homologous repair mediates the formation of balanced complex rearrangements that are viable, stably replicated and transmitted unaltered to subsequent generations.     

Mutations in the PCNA-binding domain of CDKN1C cause IMAGe syndrome

IMAGe syndrome (intrauterine growth restriction, metaphyseal dysplasia, adrenal hypoplasia congenita and genital anomalies) is an undergrowth developmental disorder with life-threatening consequences. An identity-by-descent analysis in a family with IMAGe syndrome identified a 17.2-Mb locus on chromosome 11p15 that segregated in the affected family members. Targeted exon array capture of the disease locus, followed by high-throughput genomic sequencing and validation by dideoxy sequencing, identified missense mutations in the imprinted gene CDKN1C (also known as P57KIP2) in two familial and four unrelated patients. A familial analysis showed an imprinted mode of inheritance in which only maternal transmission of the mutation resulted in IMAGe syndrome. CDKN1C inhibits cell-cycle progression, and we found that targeted expression of IMAGe-associated CDKN1C mutations in Drosophila caused severe eye growth defects compared to wild-type CDKN1C, suggesting a gain-of-function mechanism. All IMAGe-associated mutations clustered in the PCNA-binding domain of CDKN1C and resulted in loss of PCNA binding, distinguishing them from the mutations of CDKN1C that cause Beckwith-Wiedemann syndrome, an overgrowth syndrome.     

Extremely low-coverage sequencing and imputation increases power for genome-wide association studies

Genome-wide association studies (GWAS) have proven to be a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here, we show that extremely low-coverage sequencing (0.1-0.5×) captures almost as much of the common (>5%) and low-frequency (1-5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r(2) of 0.71 using off-target data (0.24× average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome-sequencing data sets, we show that association statistics obtained using extremely low-coverage sequencing data attain similar P values at known associated variants as data from genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in extremely low-coverage sequencing can yield several times the effective sample size of GWAS based on SNP array data and a commensurate increase in statistical power.