Analysis of genetic inheritance in a family quartet by whole-genome sequencing

The hitchhiker's guide to single-locus species delimitation

Molecular approaches to species delimitation are increasingly used to ascertain the number of species in a sample prior to taxonomic, ecological or physiological studies. Although multilocus approaches are gaining fast in popularity, single-gene methods still predominate in the literature. However, available simulation benchmarks of these methods focus exclusively on species-poor samples and/or tree-based approaches: as a result, travellers in the land of single-locus species delimitation lack a comprehensive "hitchhiker's guide" highlighting the sweet spots and dangers on their road. To fill this gap, we compared the performances of distance-based (ABGD, "automatic barcode gap discovery"), allele sharing-based (haplowebs) and tree-based approaches (GMYC, "generalized mixed Yule-coalescent" and PTP, "Poisson tree processes") to detect interspecific boundaries in samples of 6, 60 and 120 simulated species with various speciation rates, effective population sizes, mutation rates and sampling patterns. We found that all approaches performed poorly when population sizes and speciation rates were large, with haplowebs yielding best results followed by ABGD then tree-based approaches. The latter's error type was mostly oversplitting, whereas ABGD chiefly overlumped and haplowebs leaned either way depending on simulation parameters: such widely divergent error patterns suggest that, if all three types of methods agree, then the resulting delimitation is probably correct. Perfect congruence being quite rare, travellers in search of a one-size-fit-all approach to single-locus species delimitation should forget it; however, our hitchhiker's guide raises hope that such species delimitation's Holy Grail may be found in the relatively uncharted nearby land of multilocus species delimitation.

HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads

Background

Haplotype assembly is the process of assigning the different alleles of the variants covered by mapped sequencing reads to the two haplotypes of the genome of a human individual. Long reads, which are nowadays cheaper to produce and more widely available than ever before, have been used to reduce the fragmentation of the assembled haplotypes since their ability to span several variants along the genome. These long reads are also characterized by a high error rate, an issue which may be mitigated, however, with larger sets of reads, when this error rate is uniform across genome positions. Unfortunately, current state-of-the-art dynamic programming approaches designed for long reads deal only with limited coverages.

Results

Here, we propose a new method for assembling haplotypes which combines and extends the features of previous approaches to deal with long reads and higher coverages. In particular, our algorithm is able to dynamically adapt the estimated number of errors at each variant site, while minimizing the total number of error corrections necessary for finding a feasible solution. This allows our method to significantly reduce the required computational resources, allowing to consider datasets composed of higher coverages. The algorithm has been implemented in a freely available tool, HapCHAT: Haplotype Assembly Coverage Handling by Adapting Thresholds. An experimental analysis on sequencing reads with up to 60 × coverage reveals improvements in accuracy and recall achieved by considering a higher coverage with lower runtimes.

Conclusions

Our method leverages the long-range information of sequencing reads that allows to obtain assembled haplotypes fragmented in a lower number of unphased haplotype blocks. At the same time, our method is also able to deal with higher coverages to better correct the errors in the original reads and to obtain more accurate haplotypes as a result.

Availability

HapCHAT is available at http://hapchat.algolab.euunder the GNU Public License (GPL).

Haplotype phasing in single-cell DNA-sequencing data

Motivation

Current technologies for single-cell DNA sequencing require whole-genome amplification (WGA), as a single cell contains too little DNA for direct sequencing. Unfortunately, WGA introduces biases in the resulting sequencing data, including non-uniformity in genome coverage and high rates of allele dropout. These biases complicate many downstream analyses, including the detection of genomic variants.

Results

We show that amplification biases have a potential upside: long-range correlations in rates of allele dropout provide a signal for phasing haplotypes at the lengths of amplicons from WGA, lengths which are generally longer than than individual sequence reads. We describe a statistical test to measure concurrent allele dropout between single-nucleotide polymorphisms (SNPs) across multiple sequenced single cells. We use results of this test to perform haplotype assembly across a collection of single cells. We demonstrate that the algorithm predicts phasing between pairs of SNPs with higher accuracy than phasing from reads alone. Using whole-genome sequencing data from only seven neural cells, we obtain haplotype blocks that are orders of magnitude longer than with sequence reads alone (median length 10.2 kb versus 312 bp), with error rates <2%. We demonstrate similar advantages on whole-exome data from 16 cells, where we obtain haplotype blocks with median length 9.2 kb-comparable to typical gene lengths-compared with median lengths of 41 bp with sequence reads alone, with error rates <4%. Our algorithm will be useful for haplotyping of rare alleles and studies of allele-specific somatic aberrations.

Availability and implementation

Source code is available at https://www.github.com/raphael-group.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Statistical Framework for Mapping Risk Genes from De Novo Mutations in Whole-Genome-Sequencing Studies

Analysis of de novo mutations (DNMs) from sequencing data of nuclear families has identified risk genes for many complex diseases, including multiple neurodevelopmental and psychiatric disorders. Most of these efforts have focused on mutations in protein-coding sequences. Evidence from genome-wide association studies (GWASs) strongly suggests that variants important to human diseases often lie in non-coding regions. Extending DNM-based approaches to non-coding sequences is challenging, however, because the functional significance of non-coding mutations is difficult to predict. We propose a statistical framework for analyzing DNMs from whole-genome sequencing (WGS) data. This method, TADA-Annotations (TADA-A), is a major advance of the TADA method we developed earlier for DNM analysis in coding regions. TADA-A is able to incorporate many functional annotations such as conservation and enhancer marks, to learn from data which annotations are informative of pathogenic mutations, and to combine both coding and non-coding mutations at the gene level to detect risk genes. It also supports meta-analysis of multiple DNM studies, while adjusting for study-specific technical effects. We applied TADA-A to WGS data of ∼300 autism-affected family trios across five studies and discovered several autism risk genes. The software is freely available for all research uses.

Long-term health and germline transmission in transgenic cattle following transposon-mediated gene transfer

Background

Transposon-mediated, non-viral gene delivery is a powerful tool for generating stable cell lines and transgenic animals. However, as multi-copy insertion is the preferred integration pattern, there is the potential for uncontrolled changes in endogenous gene expression and detrimental effects in cells or animals. Our group has previously reported on the generation of several transgenic cattle by using microinjection of the Sleeping Beauty (SB) and PiggyBac (PB) transposons and seeks to explore the long-term effects of this technology on cattle.

Results

Transgenic cattle, one female (SNU-SB-1) and one male (SNU-PB-1), reached over 36 months of age with no significant health issues and normal blood parameters. The detection of transgene integration and fluorescent signal in oocytes and sperm suggested the capacity for germline transmission in both of the founder animals. After natural breeding, the founder transgenic cow delivered a male calf and secreted milk containing fluorescent transgenic proteins. The calf expressed green fluorescent protein in primary cells from ear skin, with no significant change in overall genomic stability and blood parameters. Three sites of transgene integration were identified by next-generation sequencing of the calf’s genome.

Conclusions

Overall, these data demonstrate that transposon-mediated transgenesis can be applied to cattle without being detrimental to their long-term genomic stability or general health. We further suggest that this technology may be usefully applied in other fields, such as the generation of transgenic animal models.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4760-4) contains supplementary material, which is available to authorized users.

The molecular characteristics of colorectal cancer: Implications for diagnosis and therapy

Colorectal cancer (CRC) results from the progressive accumulation of multiple genetic and epigenetic aberrations within cells. The progression from colorectal adenoma to carcinoma is caused by three major pathways: Microsatellite instability, chromosomal instability and CpG island methylator phenotype. A growing body of scientific evidences suggests that CRC is a heterogeneous disease, and genetic characteristics of the tumors determine their prognostic outcome and response to targeted therapies. Early diagnosis and effective targeted therapies based on a current knowledge of the molecular characteristics of CRC are essential to the successful treatment of CRC. Therefore, the present review summarized the current understanding of the molecular characteristics of CRC, and discussed its implications for diagnosis and targeted therapy.

Muver, a computational framework for accurately calling accumulated mutations

BACKGROUND

Identification of mutations from next-generation sequencing data typically requires a balance between sensitivity and accuracy. This is particularly true of DNA insertions and deletions (indels), that can impart significant phenotypic consequences on cells but are harder to call than substitution mutations from whole genome mutation accumulation experiments. To overcome these difficulties, we present muver, a computational framework that integrates established bioinformatics tools with novel analytical methods to generate mutation calls with the extremely low false positive rates and high sensitivity required for accurate mutation rate determination and comparison.

RESULTS

Muver uses statistical comparison of ancestral and descendant allelic frequencies to identify variant loci and assigns genotypes with models that include per-sample assessments of sequencing errors by mutation type and repeat context. Muver identifies maximally parsimonious mutation pathways that connect these genotypes, differentiating potential allelic conversion events and delineating ambiguities in mutation location, type, and size. Benchmarking with a human gold standard father-son pair demonstrates muver's sensitivity and low false positive rates. In DNA mismatch repair (MMR) deficient Saccharomyces cerevisiae, muver detects multi-base deletions in homopolymers longer than the replicative polymerase footprint at rates greater than predicted for sequential single-base deletions, implying a novel multi-repeat-unit slippage mechanism.

CONCLUSIONS

Benchmarking results demonstrate the high accuracy and sensitivity achieved with muver, particularly for indels, relative to available tools. Applied to an MMR-deficient Saccharomyces cerevisiae system, muver mutation calls facilitate mechanistic insights into DNA replication fidelity.

Enhancing the accuracy of next-generation sequencing for detecting rare and subclonal mutations

Mutations, the fuel of evolution, are first manifested as rare DNA changes within a population of cells. Although next-generation sequencing (NGS) technologies have revolutionized the study of genomic variation between species and individual organisms, most have limited ability to accurately detect and quantify rare variants among the different genome copies in heterogeneous mixtures of cells or molecules. We describe the technical challenges in characterizing subclonal variants using conventional NGS protocols and the recent development of error correction strategies, both computational and experimental, including consensus sequencing of single DNA molecules. We also highlight major applications for low-frequency mutation detection in science and medicine, describe emerging methodologies and provide our vision for the future of DNA sequencing.

Multiomics tools for the diagnosis and treatment of rare neurological disease

Conventional workup of rare neurological disease is frequently hampered by diagnostic delay or lack of diagnosis. While biomarkers have been established for many neurometabolic disorders, improved methods are required for diagnosis of previously unidentified or underreported causes of rare neurological disease. This would result in a higher diagnostic yield and increased patient numbers required for interventional studies. Recent studies using next-generation sequencing and metabolomics have led to identification of novel disease-causing genes and biomarkers. This combined approach can assist in overcoming challenges associated with analyzing and interpreting the large amount of data obtained from each technique. In particular, metabolomics can support the pathogenicity of sequence variants in genes encoding enzymes or transporters involved in metabolic pathways. Moreover, metabolomics can show the broader perturbation caused by inborn errors of metabolism and identify a metabolic fingerprint of metabolic disorders. As such, using "omics" has great potential to meet the current needs for improved diagnosis and elucidation of rare neurological disease.

High-depth whole genome sequencing of an Ashkenazi Jewish reference panel: enhancing sensitivity, accuracy, and imputation

While increasingly large reference panels for genome-wide imputation have been recently made available, the degree to which imputation accuracy can be enhanced by population-specific reference panels remains an open question. Here, we sequenced at full-depth (≥ 30×), across two platforms (Illumina X Ten and Complete Genomics, Inc.), a moderately large (n = 738) cohort of samples drawn from the Ashkenazi Jewish population. We developed a series of quality control steps to optimize sensitivity, specificity, and comprehensiveness of variant calls in the reference panel, and then tested the accuracy of imputation against target cohorts drawn from the same population. Quality control (QC) thresholds for the Illumina X Ten platform were identified that permitted highly accurate calling of single nucleotide variants across 94% of the genome. QC procedures also identified numerous regions that are poorly mapped using current reference or alternate assemblies. After stringent QC, the population-specific reference panel produced more accurate and comprehensive imputation results relative to publicly available, large cosmopolitan reference panels, especially in the range of rare variants that may be most critical to further progress in mapping of complex phenotypes. The population-specific reference panel also permitted enhanced filtering of clinically irrelevant variants from personal genomes.

Discovery of Lineage-Specific Genome Change in Rice Through Analysis of Resequencing Data

New mutations are rare, which makes their discovery laborious and time-consuming. Arthur and Bennetzen describe an approach for enriching recent mutations that relies only on a reference genome sequence and resequencing data for other...

Genome comparisons provide information on the nature of genetic change, but such comparisons are challenged to differentiate the importance of the actual sequence change processes relative to the role of selection. This problem can be overcome by identifying changes that have not yet had the time to undergo millions of years of natural selection. We describe a strategy to discover accession-specific changes in the rice genome using an abundant resource routinely provided for many genome analyses, resequencing data. The sequence of the fully sequenced rice genome from variety Nipponbare was compared to the pooled (∼114×) resequencing data from 126 japonica rice accessions to discover “Nipponbare-specific” sequences. Analyzing nonrepetitive sequences, 8504 “candidate” Nipponbare-specific changes were detected, of which around two-thirds are true novel sequence changes and the rest are predicted genome sequencing errors. Base substitutions outnumbered indels in this data set by > 28:1, with ∼8:5 bias toward transversions over transitions, and no transposable element insertions or excisions were observed. These results indicate that the strategy employed is effective for finding recent sequence changes, sequencing errors, and rare alleles in any organism that has both a reference genome sequence and a wealth of resequencing data.

Estimation of the Genetic Substitution Rate of Hanwoo and Holstein Cattle Using Whole Genome Sequencing Data

Despite the importance of mutation rate, some difficulties exist in estimating it. Next-generation sequencing (NGS) data yields large numbers of single-nucleotide polymorphisms, which can make it feasible to estimate substitution rates. The genetic substitution rates of Hanwoo and Holstein cattle were estimated using NGS data. Our main findings was to calculate the gene's substitution rates. Through estimation of genetic substitution rates, we found: diving region of altered substitution density exists. This region may indicate a boundary between protected and unprotected genes. The protected region is mainly associated with the gene ontology terms of regulatory genes. The genes that distinguish Hanwoo from Holstein in terms of substitution rate predominantly have gene ontology terms related to blood and circulatory system. This might imply that Hanwoo and Holstein evolved with dissimilar mutation rates and processes after domestication. The difference in meat quality between Hanwoo and Holstein could originate from differential evolution of the genes related to these blood and circulatory system ontology terms.

Rare ABCA7 variants in 2 German families with Alzheimer disease

Objective

The aim of this study was to identify variants associated with familial late-onset Alzheimer disease (AD) using whole-genome sequencing.

Methods

Several families with an autosomal dominant inheritance pattern of AD were analyzed by whole-genome sequencing. Variants were prioritized for rare, likely pathogenic variants in genes already known to be associated with AD and confirmed by Sanger sequencing using standard protocols.

Results

We identified 2 rare ABCA7 variants (rs143718918 and rs538591288) with varying penetrance in 2 independent German AD families, respectively. The single nucleotide variant (SNV) rs143718918 causes a missense mutation, and the deletion rs538591288 causes a frameshift mutation of ABCA7. Both variants have previously been reported in larger cohorts but with incomplete segregation information. ABCA7 is one of more than 20 AD risk loci that have so far been identified by genome-wide association studies, and both common and rare variants of ABCA7 have previously been described in different populations with higher frequencies in AD cases than in controls and varying penetrance. Furthermore, ABCA7 is known to be involved in several AD-relevant pathways.

Conclusions

We conclude that both SNVs might contribute to the development of AD in the examined family members. Together with previous findings, our data confirm ABCA7 as one of the most relevant AD risk genes.

Whole genome sequencing of Caribbean Hispanic families with late-onset Alzheimer's disease

OBJECTIVE

To identify rare causal variants underlying known loci that segregate with late-onset Alzheimer's disease (LOAD) in multiplex families.

METHODS

We analyzed whole genome sequences (WGS) from 351 members of 67 Caribbean Hispanic (CH) families from Dominican Republic and New York multiply affected by LOAD. Members of 67 CH and additional 47 Caucasian families underwent WGS as a part of the Alzheimer's Disease Sequencing Project (ADSP). All members of 67 CH families, an additional 48 CH families and an independent CH case-control cohort were subsequently genotyped for validation. Patients met criteria for LOAD, and controls were determined to be dementia free. We investigated rare variants segregating within families and gene-based associations with disease within LOAD GWAS loci.

RESULTS

A variant in AKAP9, p.R434W, segregated significantly with LOAD in two large families (OR = 5.77, 95% CI: 1.07-30.9, P = 0.041). In addition, missense mutations in MYRF and ASRGL1 under previously reported linkage peaks at 7q14.3 and 11q12.3 segregated completely in one family and in follow-up genotyping both were nominally significant (P < 0.05). We also identified rare variants in a number of genes associated with LOAD in prior genome wide association studies, including CR1 (P = 0.049), BIN1 (P = 0.0098) and SLC24A4 (P = 0.040).

CONCLUSIONS AND RELEVANCE

Rare variants in multiple genes influence the risk of LOAD disease in multiplex families. These results suggest that rare variants may underlie loci identified in genome wide association studies.

Allele-specific expression in a family quartet with autism reveals mono-to-biallelic switch and novel transcriptional processes of autism susceptibility genes

Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder, and the exact causal mechanism is unknown. Dysregulated allele-specific expression (ASE) has been identified in persons with ASD; however, a comprehensive analysis of ASE has not been conducted in a family quartet with ASD. To fill this gap, we analyzed ASE using genomic DNA from parent and offspring and RNA from offspring's postmortem prefrontal cortex (PFC); one of the two offspring had been diagnosed with ASD. DNA- and RNA-sequencing revealed distinct ASE patterns from the PFC of both offspring. However, only the PFC of the offspring with ASD exhibited a mono-to-biallelic switch for LRP2BP and ZNF407. We also identified a novel site of RNA-editing in KMT2C in addition to new monoallelically-expressed genes and miRNAs. Our results demonstrate the prevalence of ASE in human PFC and ASE abnormalities in the PFC of a person with ASD. Taken together, these findings may provide mechanistic insights into the pathogenesis of ASD.

A "late-but-fitter revertant cell" explains the high frequency of revertant mosaicism in epidermolysis bullosa

Revertant mosaicism, or "natural gene therapy", is the phenomenon in which germline mutations are corrected by somatic events. In recent years, revertant mosaicism has been identified in all major types of epidermolysis bullosa, the group of heritable blistering disorders caused by mutations in the genes encoding epidermal adhesion proteins. Moreover, revertant mosaicism appears to be present in all patients with a specific subtype of recessive epidermolysis bullosa. We therefore hypothesized that revertant mosaicism should be expected at least in all patients with recessive forms of epidermolysis bullosa. Naturally corrected, patient-own cells are of extreme interest for their promising therapeutic potential, and their presence in all patients would open exciting, new treatment perspectives to those patients. To test our hypothesis, we determined the probability that single nucleotide reversions occur in patients' skin using a mathematical developmental model. According to our model, reverse mutations are expected to occur frequently (estimated 216x) in each patient's skin. Reverse mutations should, however, occur early in embryogenesis to be able to drive the emergence of recognizable revertant patches, which is expected to occur in only one per ~10,000 patients. This underestimate, compared to our clinical observations, can be explained by the "late-but-fitter revertant cell" hypothesis: reverse mutations arise at later stages of development, but provide revertant cells with a selective growth advantage in vivo that drives the development of recognizable healthy skin patches. Our results can be extrapolated to any other organ with stem cell division numbers comparable to skin, which may offer novel future therapeutic options for other genetic conditions if these revertant cells can be identified and isolated.

The rate and potential relevance of new mutations in a colonizing plant lineage

By following the evolution of populations that are initially genetically homogeneous, much can be learned about core biological principles. For example, it allows for detailed studies of the rate of emergence of de novo mutations and their change in frequency due to drift and selection. Unfortunately, in multicellular organisms with generation times of months or years, it is difficult to set up and carry out such experiments over many generations. An alternative is provided by "natural evolution experiments" that started from colonizations or invasions of new habitats by selfing lineages. With limited or missing gene flow from other lineages, new mutations and their effects can be easily detected. North America has been colonized in historic times by the plant Arabidopsis thaliana, and although multiple intercrossing lineages are found today, many of the individuals belong to a single lineage, HPG1. To determine in this lineage the rate of substitutions-the subset of mutations that survived natural selection and drift-, we have sequenced genomes from plants collected between 1863 and 2006. We identified 73 modern and 27 herbarium specimens that belonged to HPG1. Using the estimated substitution rate, we infer that the last common HPG1 ancestor lived in the early 17th century, when it was most likely introduced by chance from Europe. Mutations in coding regions are depleted in frequency compared to those in other portions of the genome, consistent with purifying selection. Nevertheless, a handful of mutations is found at high frequency in present-day populations. We link these to detectable phenotypic variance in traits of known ecological importance, life history and growth, which could reflect their adaptive value. Our work showcases how, by applying genomics methods to a combination of modern and historic samples from colonizing lineages, we can directly study new mutations and their potential evolutionary relevance.

Using Genome Sequence to Enable the Design of Medicines and Chemical Probes

Rapid progress in genome sequencing technology has put us firmly into a postgenomic era. A key challenge in biomedical research is harnessing genome sequence to fulfill the promise of personalized medicine. This Review describes how genome sequencing has enabled the identification of disease-causing biomolecules and how these data have been converted into chemical probes of function, preclinical lead modalities, and ultimately U.S. Food and Drug Administration (FDA)-approved drugs. In particular, we focus on the use of oligonucleotide-based modalities to target disease-causing RNAs; small molecules that target DNA, RNA, or protein; the rational repurposing of known therapeutic modalities; and the advantages of pharmacogenetics. Lastly, we discuss the remaining challenges and opportunities in the direct utilization of genome sequence to enable design of medicines.

Promise and peril in nanomedicine: the challenges and needs for integrated systems biology approaches to define health risk

In the 1966s visionary film 'Fantastic Voyage' a submarine crew was shrunk to 100 nm in size and injected into the body of an injured scientist to repair his damaged brain. The movie (written by Harry Kleiner; directed by Richard Fleischer; novel by Isaac Asimov) drew attention to the potential power of engineered nanoscale structures and devices to construct, monitor, control, treat, and repair individual cells. Even more interesting was the fact that the film elegantly noted that the structure had to be miniaturized to a size that is not detected by the body's immune surveillance system, and highlighted the many physiological barriers that are encountered on the submarine's long journey to the target. Although the concept of miniaturizing humans remains an element of science fiction, targeted drug delivery through nanobots to treat diseases such as cancer is now a reality. The ability of nanobots to evade immune surveillance is one of the most attractive features of nanoscale materials that are exploited in the field of medicine for molecular diagnostics, targeted drug delivery, and therapy of diseases. This article will provide a concise opinion on the state-of-the-art, the challenges, and the use of systems biology-another equally revolutionary field of science-to assess the unique health hazards of nanomaterial exposures. WIREs Nanomed Nanobiotechnol 2018, 10:e1465. doi: 10.1002/wnan.1465 This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials.

Applied Human Genetic Epidemiology

This chapter covers the study of human epidemiology, including family studies in genetic epidemiology, linkage analysis, genetic Mapping in human diseases, human genetic influences on diseases, genetic relationships in familial aggregation, and derivation of familial risk.An Illustration is provided of a research project in genetic epidemiology research which included (1) Heritability Analysis (2) Molecular Variation Study Methods (3) Genomics for Human Genetic Epidemiology Complex Traits and Mendelian Inheritance Mendel's Laws Hardy-Weinberg Principle Gene Structure and Genetic Code Genetic Linkage and Linkage Disequilibrium Study Designs for of Rare Genetic Variations Spectrum of Variation Familial Factors in Human Genetic Epidemiology *Human Genetic Association Genetic Epidemiology Owing to Population Stratification Environmental Effects on Genetic Epidemiology Genetic Epidemiology and Public Health.

Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship

Genomic prediction is emerging in a wide range of fields including animal and plant breeding, risk prediction in human precision medicine and forensic. It is desirable to establish a theoretical framework for genomic prediction accuracy when the reference data consists of information sources with varying degrees of relationship to the target individuals. A reference set can contain both close and distant relatives as well as ‘unrelated’ individuals from the wider population in the genomic prediction. The various sources of information were modeled as different populations with different effective population sizes (N_e). Both the effective number of chromosome segments (M_e) and N_e are considered to be a function of the data used for prediction. We validate our theory with analyses of simulated as well as real data, and illustrate that the variation in genomic relationships with the target is a predictor of the information content of the reference set. With a similar amount of data available for each source, we show that close relatives can have a substantially larger effect on genomic prediction accuracy than lesser related individuals. We also illustrate that when prediction relies on closer relatives, there is less improvement in prediction accuracy with an increase in training data or marker panel density. We release software that can estimate the expected prediction accuracy and power when combining different reference sources with various degrees of relationship to the target, which is useful when planning genomic prediction (before or after collecting data) in animal, plant and human genetics.

An Upper Limit on the Functional Fraction of the Human Genome

For the human population to maintain a constant size from generation to generation, an increase in fertility must compensate for the reduction in the mean fitness of the population caused, among others, by deleterious mutations. The required increase in fertility due to this mutational load depends on the number of sites in the genome that are functional, the mutation rate, and the fraction of deleterious mutations among all mutations in functional regions. These dependencies and the fact that there exists a maximum tolerable replacement level fertility can be used to put an upper limit on the fraction of the human genome that can be functional. Mutational load considerations lead to the conclusion that the functional fraction within the human genome cannot exceed 15%.

The impact of recombination on human mutation load and disease

Recombination promotes genomic integrity among cells and tissues through double-strand break repair, and is critical for gamete formation and fertility through a strict regulation of the molecular mechanisms associated with proper chromosomal disjunction. In humans, congenital defects and recurrent structural abnormalities can be attributed to aberrant meiotic recombination. Moreover, mutations affecting genes involved in recombination pathways are directly linked to pathologies including infertility and cancer. Recombination is among the most prominent mechanism shaping genome variation, and is associated with not only the structuring of genomic variability, but is also tightly linked with the purging of deleterious mutations from populations. Together, these observations highlight the multiple roles of recombination in human genetics: its ability to act as a major force of evolution, its molecular potential to maintain genome repair and integrity in cell division and its mutagenic cost impacting disease evolution.This article is part of the themed issue 'Evolutionary causes and consequences of recombination rate variation in sexual organisms'.

Identification of de novo germline mutations and causal genes for sporadic diseases using trio-based whole-exome/genome sequencing

Whole-genome or whole-exome sequencing (WGS/WES) of the affected proband together with normal parents (trio) is commonly adopted to identify de novo germline mutations (DNMs) underlying sporadic cases of various genetic disorders. However, our current knowledge of the occurrence and functional effects of DNMs remains limited and accurately identifying the disease-causing DNM from a group of irrelevant DNMs is complicated. Herein, we provide a general-purpose discussion of important issues related to pathogenic gene identification based on trio-based WGS/WES data. Specifically, the relevance of DNMs to human sporadic diseases, current knowledge of DNM biogenesis mechanisms, and common strategies or software tools used for DNM detection are reviewed, followed by a discussion of pathogenic gene prioritization. In addition, several key factors that may affect DNM identification accuracy and causal gene prioritization are reviewed. Based on recent major advances, this review both sheds light on how trio-based WGS/WES technologies can play a significant role in the identification of DNMs and causal genes for sporadic diseases, and also discusses existing challenges.

Direct estimate of the spontaneous germ line mutation rate in African green monkeys

Here, I provide the first direct estimate of the spontaneous mutation rate in an Old World monkey, using a seven individual, three-generation pedigree of African green monkeys. Eight de novo mutations were identified within ∼1.5 Gbp of accessible genome, corresponding to an estimated point mutation rate of 0.94 × 10^-8 per site per generation, suggesting an effective population size of ∼12000 for the species. This estimation represents a significant improvement in our knowledge of the population genetics of the African green monkey, one of the most important nonhuman primate models in biomedical research. Furthermore, by comparing mutation rates in Old World monkeys with the only other direct estimates in primates to date-humans and chimpanzees-it is possible to uniquely address how mutation rates have evolved over longer time scales. While the estimated spontaneous mutation rate for African green monkeys is slightly lower than the rate of 1.2 × 10^-8 per base pair per generation reported in chimpanzees, it is similar to the lower range of rates of 0.96 × 10^-8 -1.28 × 10^-8 per base pair per generation recently estimated from whole genome pedigrees in humans. This result suggests a long-term constraint on mutation rate that is quite different from similar evidence pertaining to recombination rate evolution in primates.

Direct estimation of de novo mutation rates in a chimpanzee parent-offspring trio by ultra-deep whole genome sequencing

Mutations generate genetic variation and are a major driving force of evolution. Therefore, examining mutation rates and modes are essential for understanding the genetic basis of the physiology and evolution of organisms. Here, we aim to identify germline de novo mutations through the whole-genome surveyance of Mendelian inheritance error sites (MIEs), those not inherited through the Mendelian inheritance manner from either of the parents, using ultra-deep whole genome sequences (>150-fold) from a chimpanzee parent-offspring trio. We identified such 889 MIEs and classified them into four categories based on the pattern of inheritance and the sequence read depth: [i] de novo single nucleotide variants (SNVs), [ii] copy number neutral inherited variants, [iii] hemizygous deletion inherited variants, and [iv] de novo copy number variants (CNVs). From de novo SNV candidates, we estimated a germline de novo SNV mutation rate as 1.48 × 10^-8 per site per generation or 0.62 × 10^-9 per site per year. In summary, this study demonstrates the significance of ultra-deep whole genome sequencing not only for the direct estimation of mutation rates but also for discerning various mutation modes including de novo allelic conversion and de novo CNVs by identifying MIEs through the transmission of genomes from parents to offspring.

40,000-Year-Old Individual from Asia Provides Insight into Early Population Structure in Eurasia

By at least 45,000 years before present, anatomically modern humans had spread across Eurasia [1-3], but it is not well known how diverse these early populations were and whether they contributed substantially to later people or represent early modern human expansions into Eurasia that left no surviving descendants today. Analyses of genome-wide data from several ancient individuals from Western Eurasia and Siberia have shown that some of these individuals have relationships to present-day Europeans [4, 5] while others did not contribute to present-day Eurasian populations [3, 6]. As contributions from Upper Paleolithic populations in Eastern Eurasia to present-day humans and their relationship to other early Eurasians is not clear, we generated genome-wide data from a 40,000-year-old individual from Tianyuan Cave, China, [1, 7] to study his relationship to ancient and present-day humans. We find that he is more related to present-day and ancient Asians than he is to Europeans, but he shares more alleles with a 35,000-year-old European individual than he shares with other ancient Europeans, indicating that the separation between early Europeans and early Asians was not a single population split. We also find that the Tianyuan individual shares more alleles with some Native American groups in South America than with Native Americans elsewhere, providing further support for population substructure in Asia [8] and suggesting that this persisted from 40,000 years ago until the colonization of the Americas. Our study of the Tianyuan individual highlights the complex migration and subdivision of early human populations in Eurasia.

Human Genome Sequencing at the Population Scale: A Primer on High-Throughput DNA Sequencing and Analysis

Most human diseases have underlying genetic causes. To better understand the impact of genes on disease and its implications for medicine and public health, researchers have pursued methods for determining the sequences of individual genes, then all genes, and now complete human genomes. Massively parallel high-throughput sequencing technology, where DNA is sheared into smaller pieces, sequenced, and then computationally reordered and analyzed, enables fast and affordable sequencing of full human genomes. As the price of sequencing continues to decline, more and more individuals are having their genomes sequenced. This may facilitate better population-level disease subtyping and characterization, as well as individual-level diagnosis and personalized treatment and prevention plans. In this review, we describe several massively parallel high-throughput DNA sequencing technologies and their associated strengths, limitations, and error modes, with a focus on applications in epidemiologic research and precision medicine. We detail the methods used to computationally process and interpret sequence data to inform medical or preventative action.

Cellular and population level processes influence the rate, accumulation and observed frequency of inherited and somatic mtDNA mutations

Mitochondria are found in all animals and have the unique feature of containing multiple copies of their own small, circular DNA genome (mtDNA). The rate and pattern of mutation accumulation in the mtDNA are influenced by molecular, cellular and population level processes. We distinguish between inherited and somatic mtDNA mutations and review evidence for the often-made assumption that mutations accumulate at a higher rate in mtDNA than in nuclear DNA (nDNA). We conclude that the whole genome mutation accumulation rate is higher for mtDNA than for nDNA but include the caveat that rates overlap considerably between the individual mtDNA- and nDNA-encoded genes. Next, we discuss the postulated causal mechanisms for the high rate of mtDNA mutation accumulation in both inheritance and in somatic cells. Perhaps unexpectedly, mtDNA is resilient to many mutagens of nDNA but is prone to errors of replication. We then consider the influence of maternal inheritance, recombination and selection on the observed accumulation pattern of inherited mtDNA mutations. Finally, we discuss environmental influences of temperature and diet on the observed frequency of inherited and somatic mtDNA mutations. We conclude that it is necessary to understand the cellular processes to fully interpret the pattern of mutations and how they influence our interpretations of evolution and disease.

Statistical analysis of mutant allele frequency level of circulating cell-free DNA and blood cells in healthy individuals

Cell-free DNA (cfDNA) in plasma has emerged as a potential important biomarker in clinical diagnostics, particularly in cancer. However, somatic mutations are also commonly found in healthy individuals, which interfere with the effectiveness for cancer diagnostics. This study examined the background somatic mutations in white blood cells (WBC) and cfDNA in healthy controls based on sequencing data from 821 non-cancer individuals and several cancer samples with the aim of understanding the patterns of mutations detected in cfDNA. We determined the mutation allele frequencies in both WBC and cfDNA using a panel of 50 cancer-associated genes that covers 20 K-nucleotide region and ultra-deep sequencing with average depth >40000-fold. Our results showed that most of the mutations in cfDNA were highly correlated to WBC. We also observed that the NPM1 gene was the most frequently mutated gene in both WBC and cfDNA. Our study highlighted the importance of sequencing both cfDNA and WBC to improve the sensitivity and accuracy for calling cancer-related mutations from circulating tumour DNA, and shedded light on developing a strategy for early cancer diagnosis by cfDNA sequencing.

Evaluation of chemical mutagenicity using next generation sequencing: A review

Mutations are heritable changes in the nucleotide sequence of DNA that can lead to many adverse effects. Genotoxicity assays have been used to identify chemical mutagenicity. Recently, next generation sequencing (NGS) has been used for this purpose. In this review, we present the progress in NGS application for assessing mutagenicity of chemicals, including the methods used for detecting the induced mutations, bioinformatics tools for analyzing the sequencing data, and chemicals whose mutagenicity has been evaluated using NGS. Available information suggests that NGS technology has unparalleled advantages for evaluating mutagenicity of chemicals can be applied for the next generation of mutagenicity tests.

From sperm to offspring: Assessing the heritable genetic consequences of paternal smoking and potential public health impacts

Individuals who smoke generally do so with the knowledge of potential consequences to their own health. What is rarely considered are the effects of smoking on their future children. The objective of this work was to review the scientific literature on the effects of paternal smoking on sperm and assess the consequences to offspring. A literature search identified over 200 studies with relevant data in humans and animal models. The available data were reviewed to assess the weight of evidence that tobacco smoke is a human germ cell mutagen and estimate effect sizes. These results were used to model the potential increase in genetic disease burden in offspring caused by paternal smoking, with specific focus on aneuploid syndromes and intellectual disability, and the socioeconomic impacts of such an effect. The review revealed strong evidence that tobacco smoking is associated with impaired male fertility, and increases in DNA damage, aneuploidies, and mutations in sperm. Studies support that these effects are heritable and adversely impact the offspring. Our model estimates that, with even a modest 25% increase in sperm mutation frequency caused by smoke-exposure, for each generation across the global population there will be millions of smoking-induced de novo mutations transmitted from fathers to offspring. Furthermore, paternal smoking is estimated to contribute to 1.3 million extra cases of aneuploid pregnancies per generation. Thus, the available evidence makes a compelling case that tobacco smoke is a human germ cell mutagen with serious public health and socio-economic implications. Increased public education should be encouraged to promote abstinence from smoking, well in advance of reproduction, to minimize the transmission of harmful mutations to the next-generation.

Genetics and Genomics of Single-Gene Cardiovascular Diseases: Common Hereditary Cardiomyopathies as Prototypes of Single-Gene Disorders

This is the first of 2 review papers on genetics and genomics appearing as part of the series on "omics." Genomics pertains to all components of an organism's genes, whereas genetics involves analysis of a specific gene or genes in the context of heredity. The paper provides introductory comments, describes the basis of human genetic diversity, and addresses the phenotypic consequences of genetic variants. Rare variants with large effect sizes are responsible for single-gene disorders, whereas complex polygenic diseases are typically due to multiple genetic variants, each exerting a modest effect size. To illustrate the clinical implications of genetic variants with large effect sizes, 3 common forms of hereditary cardiomyopathies are discussed as prototypic examples of single-gene disorders, including their genetics, clinical manifestations, pathogenesis, and treatment. The genetic basis of complex traits is discussed in a separate paper.

Pilot study of large-scale production of mutant pigs by ENU mutagenesis

N-ethyl-N-nitrosourea (ENU) mutagenesis is a powerful tool to generate mutants on a large scale efficiently, and to discover genes with novel functions at the whole-genome level in Caenorhabditis elegans, flies, zebrafish and mice, but it has never been tried in large model animals. We describe a successful systematic three-generation ENU mutagenesis screening in pigs with the establishment of the Chinese Swine Mutagenesis Consortium. A total of 6,770 G1 and 6,800 G3 pigs were screened, 36 dominant and 91 recessive novel pig families with various phenotypes were established. The causative mutations in 10 mutant families were further mapped. As examples, the mutation of SOX10 (R109W) in pig causes inner ear malfunctions and mimics human Mondini dysplasia, and upregulated expression of FBXO32 is associated with congenital splay legs. This study demonstrates the feasibility of artificial random mutagenesis in pigs and opens an avenue for generating a reservoir of mutants for agricultural production and biomedical research.

DOI:http://dx.doi.org/10.7554/eLife.26248.001

Evolutionary dynamics and significance of multiple subclonal mutations in cancer

For the last 40 years the authors have collaborated on trying to understand the complexities of human cancer by formulating testable mathematical models that are based on mutation accumulation in human malignancies. We summarize the concepts encompassed by multiple mutations in human cancers in the context of source, accumulation during carcinogenesis and tumor progression, and therapeutic consequences. We conclude that the efficacious treatment of human cancer by targeted therapy will involve individualized, uniquely directed specific agents singly and in simultaneous combinations, and take into account the importance of targeting resistant subclonal mutations, particularly those subclones with alterations in DNA repair genes, DNA polymerase, and other genes required to maintain genetic stability.

Is detection of intraperitoneal exfoliated tumor cells after surgical resection of rectal cancer a prognostic factor of survival?

Background

The prognostic significance of free cancer cells detected in peritoneal fluid at the time of rectal surgery remains unclear. A substantial number of patients will develop metastatic disease even with successful local treatment. This prospective non-randomized study investigated the prognostic value of intraperitoneal free cancer cells harvested in peritoneal lavage after surgery for rectal cancer. Mutational hotspots in mitochondrial DNA were examined as potential molecular signatures to detect circulating intraperitoneal free cancer cells when present in primary tumor and in lavage.

Methods

Point mutations in mitochondrial DNA amplifications were determined in primary tumors and corresponding exfoliated intraperitoneal free cancer cells in lavage from 191 patients with locally advanced rectal cancer scheduled for radical treatment. Mitochondrial DNA target sequences were amplified by polymerase chain reaction and base substitutions were detected by denaturant, cycling temperature capillary electrophoresis. Detection of intraperitoneal free cancer cells was correlated to survival.

Results

Of 191patients analyzed, 138 (72%) were identified with somatic mitochondrial point mutations in rectal cancer tumors. From this fraction, 45 patients (33%) had positive lavage fluid with corresponding somatic mtDNA point mutations in lavage representing circulating intraperitoneal free cancer cells. There was no significant survival difference between patients identified with or without somatic mitochondrial DNA point mutations in the corresponding lavage.

Conclusion

Somatic mitochondrial DNA point mutations identified in primary rectal tumors enable detection of circulating intraperitoneal free cancer cells in lavage fluid.

Intraperitoneal free cancer cells harvested from lavage immediately after surgery for rectal cancer does not represent an independent prognostic factor on survival.

Direct estimation of the spontaneous mutation rate by short-term mutation accumulation lines in Chironomus riparius

Mutations are the ultimate basis of evolution, yet their occurrence rate is known only for few species. We directly estimated the spontaneous mutation rate and the mutational spectrum in the nonbiting midge C. riparius with a new approach. Individuals from ten mutation accumulation lines over five generations were deep genome sequenced to count de novo mutations that were not present in a pool of F1 individuals, representing parental genotypes. We identified 51 new single site mutations of which 25 were insertions or deletions and 26 single nucleotide mutations. This shift in the mutational spectrum compared to other organisms was explained by the high A/T content of the species. We estimated a haploid mutation rate of 2.1 × 10-9 (95% confidence interval: 1.4 × 10-9 - 3.1 × 10-9) that is in the range of recent estimates for other insects and supports the drift barrier hypothesis. We show that accurate mutation rate estimation from a high number of observed mutations is feasible with moderate effort even for nonmodel species.

The Hiroshima/Nagasaki Survivor Studies: Discrepancies Between Results and General Perception

The explosion of atom bombs over the cities of Hiroshima and Nagasaki in August 1945 resulted in very high casualties, both immediate and delayed but also left a large number of survivors who had been exposed to radiation, at levels that could be fairly precisely ascertained. Extensive follow-up of a large cohort of survivors (120,000) and of their offspring (77,000) was initiated in 1947 and continues to this day. In essence, survivors having received 1 Gy irradiation (∼1000 mSV) have a significantly elevated rate of cancer (42% increase) but a limited decrease of longevity (∼1 year), while their offspring show no increased frequency of abnormalities and, so far, no detectable elevation of the mutation rate. Current acceptable exposure levels for the general population and for workers in the nuclear industry have largely been derived from these studies, which have been reported in more than 100 publications. Yet the general public, and indeed most scientists, are unaware of these data: it is widely believed that irradiated survivors suffered a very high cancer burden and dramatically shortened life span, and that their progeny were affected by elevated mutation rates and frequent abnormalities. In this article, I summarize the results and discuss possible reasons for this very striking discrepancy between the facts and general beliefs about this situation.

Whole-Genome Sequencing of a Family with Hereditary Pulmonary Alveolar Proteinosis Identifies a Rare Structural Variant Involving CSF2RA/CRLF2/IL3RA Gene Disruption

Pulmonary alveolar proteinosis (PAP) is a rare pulmonary disease in which the abnormalities in alveolar surfactant accumulation are caused by impairments of GM-CSF pathway attributing to defects in a variety of genes. However, hereditary PAP is extremely uncommon and a detailed understanding in the genetic inheritance of PAP in a family may provide timely diagnosis, treatment and proper intervention including genetic consultation. Here, we described a comprehensive analysis of genome and gene expression for a family containing one affected child with a diagnosis of PAP and two other healthy siblings. Family-based whole-genome analysis revealed a homozygous deletion that disrupts CSF2RA, CRLF2, and IL3RA gene in the pseudoautosomal region of the X chromosome in the affected child and one of asymptomatic siblings. Further functional pathway analysis of differentially expressed genes in IL-1β-treated peripheral blood mononuclear cells highlighted the insufficiency of immune response in the child with PAP, especially the protection against bacterial infection. Collectively, our results reveal a novel allele as the genetic determinant of a family with PAP and provide insights into variable expressivity and incomplete penetrance of this rare disease, which will be helpful for proper genetic consultation and prompt treatment to avoid mortality and morbidity.

Using information of relatives in genomic prediction to apply effective stratified medicine

Genomic prediction shows promise for personalised medicine in which diagnosis and treatment are tailored to individuals based on their genetic profiles for complex diseases. We present a theoretical framework to demonstrate that prediction accuracy can be improved by targeting more informative individuals in the data set used to generate the predictors ("discovery sample") to include those with genetically close relationships with the subjects put forward for risk prediction. Increase of prediction accuracy from closer relationships is achieved under an additive model and does not rely on any family or interaction effects. Using theory, simulations and real data analyses, we show that the predictive accuracy or the area under the receiver operating characteristic curve (AUC) increased exponentially with decreasing effective size (Ne), i.e. when individuals are closely related. For example, with the sample size of discovery set N = 3000, heritability h2 = 0.5 and population prevalence K = 0.1, AUC value approached to 0.9 and the top percentile of the estimated genetic profile scores had 23 times higher proportion of cases than the general population. This suggests that there is considerable room to increase prediction accuracy by using a design that does not exclude closer relationships.

Non-Coding Loss-of-Function Variation in Human Genomes

Whole-genome and exome sequencing in human populations has revealed the tolerance of each gene for loss-of-function variation. By understanding this tolerance, it has become increasingly possible to identify genes that would make safe therapeutic targets and to identify rare genetic risk factors and phenotypes at the scale of individual genomes. To date, the vast majority of surveyed loss-of-function variants are in protein-coding regions of the genome mainly due to the focus on these regions by exome-based sequencing projects and their relative ease of interpretability. As whole-genome sequencing becomes more prevalent, new strategies will be required to uncover impactful variation in non-coding regions of the genome where the architecture of genome function is more complex. In this review, we investigate recent studies of loss-of-function variation and emerging approaches for interpreting whole-genome sequencing data to identify rare and impactful non-coding loss-of-function variants.