Origins of chromosomal rearrangement hotspots in the human genome: evidence from the AZFa deletion hotspots.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: The origins of the recombination hotspots that are a common feature of both allelic and non-allelic homologous recombination in the human genome are poorly understood. We have investigated, by comparative sequencing, the evolution of two hotspots of non-allelic homologous recombination on the Y chromosome that lie within paralogous sequences known to sponsor deletions resulting in male infertility. RESULTS: These recombination hotspots are characterized by signatures of concerted evolution, which indicate that gene conversion between paralogs has been predominant in shaping their recent evolution. By contrast, the paralogous sequences that surround the hotspots exhibit little evidence of gene conversion. A second feature of these rearrangement hotspots is the extreme interspecific sequence divergence (around 2.5%) that places them among the most divergent orthologous sequences between humans and chimpanzees. CONCLUSIONS: Several hominid-specific gene conversion events have rendered these hotspots better substrates for chromosomal rearrangements in humans than in chimpanzees or gorillas. Monte Carlo simulations of sequence evolution suggest that extreme sequence divergence is a direct consequence of gene conversion between paralogs. We propose that the coincidence of signatures of concerted evolution and recurrent breakpoints of chromosomal rearrangement (mapped at the sequence level) may enable the identification of putative rearrangement hotspots from analysis of comparative sequences from great apes

Shotgun haplotyping: a novel method for surveying allelic sequence variation

Haplotypic sequences contain significantly more information than genotypes of genetic markers and are critical for studying disease association and genome evolution. Current methods for obtaining haplotypic sequences require the physical separation of alleles before sequencing, are time consuming and are not scaleable for large surveys of genetic variation. We have developed a novel method for acquiring haplotypic sequences from long PCR products using simple, high-throughput techniques. This method applies modified shotgun sequencing protocols to sequence both alleles concurrently, with read-pair information allowing the two alleles to be separated during sequence assembly. Although the haplotypic sequences can be assembled manually from the resultant data using pre-existing sequence assembly software, we have devised a novel heuristic algorithm to automate assembly and remove human error. We validated the approach on two long PCR products amplified from the human genome and confirmed the accuracy of our sequences against full-length clones of the same alleles. This method presents a simple high-throughput means to obtain full haplotypic sequences potentially up to 20 kb in length and is suitable for surveying genetic variation even in poorly-characterized genomes as it requires no prior information on sequence variation

IMPROVE-DD: Integrating Multiple Phenotype Resources Optimises Variant Evaluation in genetically determined Developmental Disorders

Diagnosing rare developmental disorders using genome-wide sequencing data commonly necessitates review of multiple plausible candidate variants, often using ontologies of categorical clinical terms. We show that Integrating Multiple Phenotype Resources Optimizes Variant Evaluation in Developmental Disorders (IMPROVE-DD) by incorporating additional classes of data commonly available to clinicians and recorded in health records. In doing so, we quantify the distinct contributions of sex, growth, and development in addition to Human Phenotype Ontology (HPO) terms and demonstrate added value from these readily available information sources. We use likelihood ratios for nominal and quantitative data and propose a classifier for HPO terms in this framework. This Bayesian framework results in more robust diagnoses. Using data systematically collected in the Deciphering Developmental Disorders study, we considered 77 genes with pathogenic/likely pathogenic variants in ≥10 individuals. All genes showed at least a satisfactory prediction by receiver operating characteristic when testing on training data (AUC ≥ 0.6), and HPO terms were the best predictor for the majority of genes, though a minority (13/77) of genes were better predicted by other phenotypic data types. Overall, classifiers based upon multiple integrated phenotypic data sources performed better than those based upon any individual source, and importantly, integrated models produced notably fewer false positives. Finally, we show that IMPROVE-DD models with good predictive performance on cross-validation can be constructed from relatively few individuals. This suggests new strategies for candidate gene prioritization and highlights the value of systematic clinical data collection to support diagnostic programs

Fast-evolving noncoding sequences in the human genome

BACKGROUND: Gene regulation is considered one of the driving forces of evolution. Although protein-coding DNA sequences and RNA genes have been subject to recent evolutionary events in the human lineage, it has been hypothesized that the large phenotypic divergence between humans and chimpanzees has been driven mainly by changes in gene regulation rather than altered protein-coding gene sequences. Comparative analysis of vertebrate genomes has revealed an abundance of evolutionarily conserved but noncoding sequences. These conserved noncoding (CNC) sequences may well harbor critical regulatory variants that have driven recent human evolution. RESULTS: Here we identify 1,356 CNC sequences that appear to have undergone dramatic human-specific changes in selective pressures, at least 15% of which have substitution rates significantly above that expected under neutrality. The 1,356 'accelerated CNC' (ANC) sequences are enriched in recent segmental duplications, suggesting a recent change in selective constraint following duplication. In addition, single nucleotide polymorphisms within ANC sequences have a significant excess of high frequency derived alleles and high F(ST)values relative to controls, indicating that acceleration and positive selection are recent in human populations. Finally, a significant number of single nucleotide polymorphisms within ANC sequences are associated with changes in gene expression. The probability of variation in an ANC sequence being associated with a gene expression phenotype is fivefold higher than variation in a control CNC sequence. CONCLUSION: Our analysis suggests that ANC sequences have until very recently played a role in human evolution, potentially through lineage-specific changes in gene regulation

Exome Sequencing for Prenatal Detection of Genetic Abnormalities in Fetal Ultrasound Anomalies: An Economic Evaluation.

INTRODUCTION: In light of the prospective Prenatal Assessment of Genomes and Exomes (PAGE) study, this paper aimed to determine the additional costs of using exome sequencing (ES) alongside or in place of chromosomal microarray (CMA) in a fetus with an identified congenital anomaly. METHODS: A decision tree was populated using data from a prospective cohort of women undergoing invasive diagnostic testing. Four testing strategies were evaluated: CMA, ES, CMA followed by ES ("stepwise"); CMA and ES combined. RESULTS: When ES is priced at GBP 2,100 (EUR 2,407/USD 2,694), performing ES alone prenatally would cost a further GBP 31,410 (EUR 36,001/USD 40,289) per additional genetic diagnosis, whereas the stepwise would cost a further GBP 24,657 (EUR 28,261/USD 31,627) per additional genetic diagnosis. When ES is priced at GBP 966 (EUR 1,107/USD 1,239), performing ES alone prenatally would cost a further GBP 11,532 (EUR 13,217/USD 14,792) per additional genetic diagnosis, whereas the stepwise would cost a further additional GBP 11,639 (EUR 13,340/USD 14,929) per additional genetic diagnosis. The sub-group analysis suggests that performing stepwise on cases indicative of multiple anomalies at ultrasound scan (USS) compared to cases indicative of a single anomaly, is more cost-effective compared to using ES alone. DISCUSSION/CONCLUSION: Performing ES alongside CMA is more cost-effective than ES alone, which can potentially lead to improvements in pregnancy management. The direct effects of test results on pregnancy outcomes were not examined; therefore, further research is recommended to examine changes on the projected incremental cost-effectiveness ratios

Reduced reproductive success is associated with selective constraint on human genes

Genome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants1. Although genes under the strongest selective constraint are highly enriched for associations with Mendelian disorders, most of these genes are not associated with disease and therefore the nature of the selection acting on them is not known2. Here we show that genetic variants that damage these genes are associated with markedly reduced reproductive success, primarily owing to increased childlessness, with a stronger effect in males than in females. We present evidence that increased childlessness is probably mediated by genetically associated cognitive and behavioural traits, which may mean that male carriers are less likely to find reproductive partners. This reduction in reproductive success may account for 20% of purifying selection against heterozygous variants that ablate protein-coding genes. Although this genetic association may only account for a very minor fraction of the overall likelihood of being childless (less than 1%), especially when compared to more influential sociodemographic factors, it may influence how genes evolve over time

Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization.

BACKGROUND: Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined. RESULTS: We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses. CONCLUSION: Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

DECIPHER: Supporting the interpretation and sharing of rare disease phenotype-linked variant data to advance diagnosis and research.

Funder: European Molecular Biology Laboratory; Id: http://dx.doi.org/10.13039/100013060DECIPHER (https://www.deciphergenomics.org) is a free web platform for sharing anonymized phenotype-linked variant data from rare disease patients. Its dynamic interpretation interfaces contextualize genomic and phenotypic data to enable more informed variant interpretation, incorporating international standards for variant classification. DECIPHER supports almost all types of germline and mosaic variation in the nuclear and mitochondrial genome: sequence variants, short tandem repeats, copy-number variants, and large structural variants. Patient phenotypes are deposited using Human Phenotype Ontology (HPO) terms, supplemented by quantitative data, which is aggregated to derive gene-specific phenotypic summaries. It hosts data from >250 projects from ~40 countries, openly sharing >40,000 patient records containing >51,000 variants and >172,000 phenotype terms. The rich phenotype-linked variant data in DECIPHER drives rare disease research and diagnosis by enabling patient matching within DECIPHER and with other resources, and has been cited in >2,600 publications. In this study, we describe the types of data deposited to DECIPHER, the variant interpretation tools, and patient matching interfaces which make DECIPHER an invaluable rare disease resource

The contribution of X-linked coding variation to severe developmental disorders

Over 130 X-linked genes have been robustly associated with developmental disorders, and X-linked causes have been hypothesised to underlie the higher developmental disorder rates in males. Here, we evaluate the burden of X-linked coding variation in 11,044 developmental disorder patients, and find a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We develop an improved strategy to detect X-linked developmental disorders and identify 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known developmental disorder-associated genes. Importantly, we estimate that, in male probands, only 13% of inherited rare missense variants in known developmental disorder-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders. Developmental disorders (DDs) are more prevalent in males, thought to be due to X-linked genetic variation. Here, the authors investigate the burden of X-linked coding variants in 11,044 DD patients, showing that this contributes to similar to 6% of both male and female cases and therefore does not solely explain male bias in DDs.Peer reviewe

Evaluating variants classified as pathogenic in ClinVar in the DDD Study.

PURPOSE: Automated variant filtering is an essential part of diagnostic genome-wide sequencing but may generate false negative results. We sought to investigate whether some previously identified pathogenic variants may be being routinely excluded by standard variant filtering pipelines. METHODS: We evaluated variants that were previously classified as pathogenic or likely pathogenic in ClinVar in known developmental disorder genes using exome sequence data from the Deciphering Developmental Disorders (DDD) study. RESULTS: Of these ClinVar pathogenic variants, 3.6% were identified among 13,462 DDD probands, and 1134/1352 (83.9%) had already been independently communicated to clinicians using DDD variant filtering pipelines as plausibly pathogenic. The remaining 218 variants failed consequence, inheritance, or other automated variant filters. Following clinical review of these additional variants, we were able to identify 112 variants in 107 (0.8%) DDD probands as potential diagnoses. CONCLUSION: Lower minor allele frequency (1 star) are good predictors of a previously identified variant being plausibly diagnostic for developmental disorders. However, around half of previously identified pathogenic variants excluded by automated variant filtering did not appear to be disease-causing, underlining the continued need for clinical evaluation of candidate variants as part of the diagnostic process