Purifying Selection in Deeply Conserved Human Enhancers Is More Consistent than in Coding Sequences

(c) 2014 De Silva et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Evaluating purifying selection in the mitochondrial DNA of various mammalian species

Mitochondrial DNA (mtDNA), the circular DNA molecule inside the mitochondria of all eukaryotic cells, has been shown to be under the effect of purifying selection in several species. Traditional testing of purifying selection has been based simply on ratios of nonsynonymous to synonymous mutations, without considering the relative age of each mutation, which can be determined by phylogenetic analysis of this non-recombining molecule. The incorporation of a mutation time-ordering from phylogeny and of predicted pathogenicity scores for nonsynonymous mutations allow a quantitative evaluation of the effects of purifying selection in human mtDNA. Here, by using this additional information, we show that purifying selection undoubtedly acts upon the mtDNA of other mammalian species/genera, namely Bos sp., Canis lupus, Mus musculus, Orcinus orca, Pan sp. and Sus scrofa. The effects of purifying selection were comparable in all species, leading to a significant major proportion of nonsynonymous variants with higher pathogenicity scores in the younger branches of the tree. We also derive recalibrated mutation rates for age estimates of ancestors of these various species and proposed a correction curve in order to take into account the effects of selection. Understanding this selection is fundamental to evolutionary studies and to the identification of deleterious mutations

A novel familial mutation in the PCSK1 gene that alters the oxyanion hole residue of proprotein convertase 1/3 and impairs its enzymatic activity.

Four siblings presented with congenital diarrhea and various endocrinopathies. Exome sequencing and homozygosity mapping identified five regions, comprising 337 protein-coding genes that were shared by three affected siblings. Exome sequencing identified a novel homozygous N309K mutation in the proprotein convertase subtilisin/kexin type 1 (PCSK1) gene, encoding the neuroendocrine convertase 1 precursor (PC1/3) which was recently reported as a cause of Congenital Diarrhea Disorder (CDD). The PCSK1 mutation affected the oxyanion hole transition state-stabilizing amino acid within the active site, which is critical for appropriate proprotein maturation and enzyme activity. Unexpectedly, the N309K mutant protein exhibited normal, though slowed, prodomain removal and was secreted from both HEK293 and Neuro2A cells. However, the secreted enzyme showed no catalytic activity, and was not processed into the 66 kDa form. We conclude that the N309K enzyme is able to cleave its own propeptide but is catalytically inert against in trans substrates, and that this variant accounts for the enteric and systemic endocrinopathies seen in this large consanguineous kindred

Exome sequencing followed by large-scale genotyping suggests a limited role for moderately rare risk factors of strong effect in schizophrenia.

Schizophrenia is a severe psychiatric disorder with strong heritability and marked heterogeneity in symptoms, course, and treatment response. There is strong interest in identifying genetic risk factors that can help to elucidate the pathophysiology and that might result in the development of improved treatments. Linkage and genome-wide association studies (GWASs) suggest that the genetic basis of schizophrenia is heterogeneous. However, it remains unclear whether the underlying genetic variants are mostly moderately rare and can be identified by the genotyping of variants observed in sequenced cases in large follow-up cohorts or whether they will typically be much rarer and therefore more effectively identified by gene-based methods that seek to combine candidate variants. Here, we consider 166 persons who have schizophrenia or schizoaffective disorder and who have had either their genomes or their exomes sequenced to high coverage. From these data, we selected 5,155 variants that were further evaluated in an independent cohort of 2,617 cases and 1,800 controls. No single variant showed a study-wide significant association in the initial or follow-up cohorts. However, we identified a number of case-specific variants, some of which might be real risk factors for schizophrenia, and these can be readily interrogated in other data sets. Our results indicate that schizophrenia risk is unlikely to be predominantly influenced by variants just outside the range detectable by GWASs. Rather, multiple rarer genetic variants must contribute substantially to the predisposition to schizophrenia, suggesting that both very large sample sizes and gene-based association tests will be required for securely identifying genetic risk factors. © 2012 The American Society of Human Genetics

Missense-depleted regions in population exomes implicate ras superfamily nucleotide-binding protein alteration in patients with brain malformation.

Genomic sequence interpretation can miss clinically relevant missense variants for several reasons. Rare missense variants are numerous in the exome and difficult to prioritise. Affected genes may also not have existing disease association. To improve variant prioritisation, we leverage population exome data to identify intragenic missense-depleted regions (MDRs) genome-wide that may be important in disease. We then use missense depletion analyses to help prioritise undiagnosed disease exome variants. We demonstrate application of this strategy to identify a novel gene association for human brain malformation. We identified de novo missense variants that affect the GDP/GTP-binding site of ARF1 in three unrelated patients. Corresponding functional analysis suggests ARF1 GDP/GTP-activation is affected by the specific missense mutations associated with heterotopia. These findings expand the genetic pathway underpinning neurologic disease that classically includes FLNA. ARF1 along with ARFGEF2 add further evidence implicating ARF/GEFs in the brain. Using functional ontology, top MDR-containing genes were highly enriched for nucleotide-binding function, suggesting these may be candidates for human disease. Routine consideration of MDR in the interpretation of exome data for rare diseases may help identify strong genetic factors for many severe conditions, infertility/reduction in reproductive capability, and embryonic conditions contributing to preterm loss

Molecular analysis of sarcomeric and non-sarcomeric genes in patients with hypertrophic cardiomyopathy.

Background: Hypertrophic cardiomyopathy (HCM) is a common genetic heart disorder characterized by unexplained left ventricle hypertrophy associated with non-dilated ventricular chambers. Several genes encoding heart sarcomeric proteins have been associated to HCM, but a small proportion of HCM patients harbor alterations in other non-sarcomeric loci. The variable expression of HCM seems influenced by genetic modifier factors and new sequencing technologies are redefining the understanding of genotype–phenotype relationships, even if the interpretations of the numerous identified variants pose several challenges. Methods and results: We investigated 62 sarcomeric and non-sarcomeric genes in 41 HCM cases and in 3 HCM-related disorders patients. We employed an integrated approach that combines multiple tools for the prediction, annotation and visualization of functional variants. Genotype–phenotype correlations were carried out for inspecting the involvement of each gene in age onset and clinical variability of HCM. The 80% of the non-syndromic patients showed at least one rare non-synonymous variant (nsSNV) and among them, 58% carried alterations in sarcomeric loci, 14% in desmosomal and 7% in other non-sarcomeric ones without any sarcomere change. Statistical analyses revealed an inverse correlation between the number of nsSNVs and age at onset, and a relationship between the clinical variability and number and type of variants. Conclusions: Our results extend the mutational spectrum of HCM and contribute in defining the molecular pathogenesis and inheritance pattern(s) of this condition. Besides, we delineate a specific procedure for the identification of the most likely pathogenetic variants for a next generation sequencing approach embodied in a clinical context

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Identification of Deleterious and Disease Alleles in a General Population and Preterm Labor Patients

With the recent advance in sequencing technology, there have been growing interests in developing new methods to predict disease-causing alleles in a personal genome by integrating functional evidences from sequence conservation, genome-wide association studies and the transcriptional regulatory network. However, even in protein-coding regions, it is not well understood how often and by what mechanism deleterious alleles disrupting strong sequence conservation can become common in population frequency and affect complex traits in humans. Moreover, in non-coding regions, even for known disease-causing genes, it is not clear how sequence conservation can be combined with functional genomic data to predict underlying disease-causing variants. To address the first question, I developed a new likelihood ratio test for sequence conservation to predict deleterious missense alleles in the human genome. By applying the new test to three personal genomes, I find that the presence of only 10% of common deleterious SNPs can be explained by false positives due to multiple hypothesis testing, violation of evolutionary model assumptions, recent gene duplication and relaxation of selective constraints on biological processes. Next, by applying the likelihood ratio test to a general human population, I find that both computationally predicted deleterious SNPs and known disease-associated alleles are enriched within genomic regions that have been influenced by positive selection in the recent past. The observed pattern agrees with the prediction that deleterious alleles can dragged along to higher-than-expected allele frequencies due to the genetic linkage with beneficial alleles by the hitchhiking effect. Second, I developed an integrative strategy to predict disease-causing non-coding variants in FSH receptor, a gene known to be associated with preterm birth, as a proof of principle. I sequenced protein-coding and conserved non-coding regions in preterm and term mothers, and conducted fine-mapping and transcription factor binding site analysis to narrow down the causal non-coding variants. Here, I find that in non-coding regions the causal variants can be resolved better by accounting for the expected effects of binding site mutations on the transcription regulatory network in addition to sequence conservation. These results indicate that the comparative genomics will provide the new opportunity to explore deleterious and disease-causing genetic variation at an unprecedentedly high resolution across the genome and in a population especially if functional genomics can be integrated with comparative genomics

Integrated genomic and transcriptomic analyses of radiation-induced malignancies

Cancer is a genetic disease caused by an unregulated expansion of a clone of cells (Sompayrac, 2004). The genetic abnormalities in cancer are the consequences of defective DNA replication, repair, maintenance, and modification, genetic background, and exposure to mutagens (Alexandrov et al., 2013). Ionizing radiation (IR), a mutagen exposed to cancer patients during clinical radiotherapy (RT), can cause DNA damage, genomic instability, and mutagenesis (Sherborne et al., 2015). While RT has been effective in treating cancer, it increases the risk of second malignant neoplasm (SMN), a severe delayed complication associated with mainly pediatric cancer survivors many decades after the treatment of their first cancer (Robison & Hudson, 2014). As the mortality of patients with childhood cancer has been decreasing, cases of radiation-induced cancers has been increasing (Robison & Hudson, 2014). The considerable contribution by RT to SMN risk illustrate the need to characterize the genetic mechanism directly responsible for radiation-induced malignancies. To better our understanding of the mutational landscape of SMNs, our specific aims are to identify potential driver mutations implicated in radiation-induced malignancies through genome and transcriptome analysis and to assess whether genetic background, specifically germline polymorphisms and mutations in tumor suppressor gene TP53, has an impact on the formation of secondary malignancies