Distinct error rates for reference and nonreference genotypes estimated by pedigree analysis

Errors in genotype calling can have perverse effects on genetic analyses, confounding association studies, and obscuring rare variants. Analyses now routinely incorporate error rates to control for spurious findings. However, reliable estimates of the error rate can be difficult to obtain because of their variance between studies. Most studies also report only a single estimate of the error rate even though genotypes can be miscalled in more than one way. Here, we report a method for estimating the rates at which different types of genotyping errors occur at biallelic loci using pedigree information. Our method identifies potential genotyping errors by exploiting instances where the haplotypic phase has not been faithfully transmitted. The expected frequency of inconsistent phase depends on the combination of genotypes in a pedigree and the probability of miscalling each genotype. We develop a model that uses the differences in these frequencies to estimate rates for different types of genotype error. Simulations show that our method accurately estimates these error rates in a variety of scenarios. We apply this method to a dataset from the whole-genome sequencing of owl monkeys (Aotus nancymaae) in three-generation pedigrees. We find significant differences between estimates for different types of genotyping error, with the most common being homozygous reference sites miscalled as heterozygous and vice versa. The approach we describe is applicable to any set of genotypes where haplotypic phase can reliably be called and should prove useful in helping to control for false discoveries.     

The nature of intraspecific and interspecific genome size variation in taxonomically complex eyebrights

Background and aimsGenome size varies considerably across the diversity of plant life. Although genome size is, by definition, affected by genetic presence/absence variants, which are ubiquitous in population sequencing studies, genome size is often treated as an intrinsic property of a species. Here, we studied intra- and interspecific genome size variation in taxonomically complex British eyebrights (Euphrasia, Orobanchaceae). Our aim is to document genome size diversity and investigate underlying evolutionary processes shaping variation between individuals, populations and species.MethodsWe generated genome size data for 192 individuals of diploid and tetraploid Euphrasia and analysed genome size variation in relation to ploidy, taxonomy, population affiliation and geography. We further compared the genomic repeat content of 30 samples.Key resultsWe found considerable intraspecific genome size variation, and observed isolation-by-distance for genome size in outcrossing diploids. Tetraploid Euphrasia showed contrasting patterns, with genome size increasing with latitude in outcrossing Euphrasia arctica, but with little genome size variation in the highly selfing Euphrasia micrantha. Interspecific differences in genome size and the genomic proportions of repeat sequences were small.ConclusionsWe show the utility of treating genome size as the outcome of polygenic variation. Like other types of genetic variation, such as single nucleotide polymorphisms, genome size variation may be affected by ongoing hybridization and the extent of population subdivision. In addition to selection on associated traits, genome size is predicted to be affected indirectly by selection due to pleiotropy of the underlying presence/absence variants.     

Interlocus gene conversion explains at least 2.7?% of single nucleotide variants in human segmental duplications

Background

Interlocus gene conversion (IGC) is a recombination-based mechanism that results in the unidirectional transfer of short stretches of sequence between paralogous loci. Although IGC is a well-established mechanism of human disease, the extent to which this mutagenic process has shaped overall patterns of segregating variation in multi-copy regions of the human genome remains unknown. One expected manifestation of IGC in population genomic data is the presence of one-to-one paralogous SNPs that segregate identical alleles.

Results

Here, I use SNP genotype calls from the low-coverage phase 3 release of the 1000 Genomes Project to identify 15,790 parallel, shared SNPs in duplicated regions of the human genome. My approach for identifying these sites accounts for the potential redundancy of short read mapping in multi-copy genomic regions, thereby effectively eliminating false positive SNP calls arising from paralogous sequence variation. I demonstrate that independent mutation events to identical nucleotides at paralogous sites are not a significant source of shared polymorphisms in the human genome, consistent with the interpretation that these sites are the outcome of historical IGC events. These putative signals of IGC are enriched in genomic contexts previously associated with non-allelic homologous recombination, including clear signals in gene families that form tandem intra-chromosomal clusters.

Conclusions

Taken together, my analyses implicate IGC, not point mutation, as the mechanism generating at least 2.7 % of single nucleotide variants in duplicated regions of the human genome.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1681-3) contains supplementary material, which is available to authorized users.     

An Algorithm for the DNA Sequence Generation from k-Tuple Word Contents of the Minimal Number of Random Fragments

Abstract

An algorithm is described for generation of the long sequence written in a four letter alphabet from the constituent k-tuple words in the minimal number of separate, randomly defined fragments of the starting sequence. It is primarily intended for use in sequencing by hybridization (SBH) process- a potential method for sequencing human genome DNA (Drmanac et al., Genomics 4, pp. 114–128, 1989). The algorithm is based on the formerly defined rules and informative entities of the linear sequence.

The algorithm requires neither knowledge on the number of appearances of a given k-tuple in sequence fragments, nor the information on which k-tuple words are on the ends of a fragment. It operates with the mixed content of k-tuples of the various lengths. The concept of the algorithm enables operations with the k-tuple sets containing false positive and false negative k-tuples. The content of the false k-tuples primarily affects the completeness of the generated sequence, and its correctness in the specific cases only. The algorithm can be used for the optimization of SBH parameters in the simulation experiments, as well as for the sequence generation in the real SBH experiments on the genomic DNA.     

SVM2: an improved paired-end-based tool for the detection of small genomic structural variations using high-throughput single-genome resequencing data

Several bioinformatics methods have been proposed for the detection and characterization of genomic structural variation (SV) from ultra high-throughput genome resequencing data. Recent surveys show that comprehensive detection of SV events of different types between an individual resequenced genome and a reference sequence is best achieved through the combination of methods based on different principles (split mapping, reassembly, read depth, insert size, etc.). The improvement of individual predictors is thus an important objective. In this study, we propose a new method that combines deviations from expected library insert sizes and additional information from local patterns of read mapping and uses supervised learning to predict the position and nature of structural variants. We show that our approach provides greatly increased sensitivity with respect to other tools based on paired end read mapping at no cost in specificity, and it makes reliable predictions of very short insertions and deletions in repetitive and low-complexity genomic contexts that can confound tools based on split mapping of reads.     

Elusive Copy Number Variation in the Mouse Genome

Background

Array comparative genomic hybridization (aCGH) to detect copy number variants (CNVs) in mammalian genomes has led to a growing awareness of the potential importance of this category of sequence variation as a cause of phenotypic variation. Yet there are large discrepancies between studies, so that the extent of the genome affected by CNVs is unknown. We combined molecular and aCGH analyses of CNVs in inbred mouse strains to investigate this question.

Principal Findings

Using a 2.1 million probe array we identified 1,477 deletions and 499 gains in 7 inbred mouse strains. Molecular characterization indicated that approximately one third of the CNVs detected by the array were false positives and we estimate the false negative rate to be more than 50%. We show that low concordance between studies is largely due to the molecular nature of CNVs, many of which consist of a series of smaller deletions and gains interspersed by regions where the DNA copy number is normal.

Conclusions

Our results indicate that CNVs detected by arrays may be the coincidental co-localization of smaller CNVs, whose presence is more likely to perturb an aCGH hybridization profile than the effect of an isolated, small, copy number alteration. Our findings help explain the hitherto unexplored discrepancies between array-based studies of copy number variation in the mouse genome.     

Evidence for widespread selection in shaping the genomic landscape during speciation of Populus

Increasing our understanding of how evolutionary processes drive the genomic landscape of variation is fundamental to a better understanding of the genomic consequences of speciation. However, genome‐wide patterns of within‐ and between‐ species variation have not been fully investigated in most forest tree species despite their global ecological and economic importance. Here, we use whole‐genome resequencing data from four Populus species spanning the speciation continuum to reconstruct their demographic histories and investigate patterns of diversity and divergence within and between species. Using Populus trichocarpa as an outgroup species, we further infer the genealogical relationships and estimate the extent of ancient introgression among the three aspen species (Populus tremula, Populus davidiana and Populus tremuloides) throughout the genome. Our results show substantial variation in these patterns along the genomes with this variation being strongly predicted by local recombination rates and the density of functional elements. This implies that the interaction between recurrent selection and intrinsic genomic features has dramatically sculpted the genomic landscape over long periods of time. In addition, our findings provide evidence that, apart from background selection, recent positive selection and long‐term balancing selection have also been crucial components in shaping patterns of genome‐wide variation during the speciation process.     

A Streamlined Method for Detecting Structural Variants in Cancer Genomes by Short Read Paired-End Sequencing

Defining the architecture of a specific cancer genome, including its structural variants, is essential for understanding tumor biology, mechanisms of oncogenesis, and for designing effective personalized therapies. Short read paired-end sequencing is currently the most sensitive method for detecting somatic mutations that arise during tumor development. However, mapping structural variants using this method leads to a large number of false positive calls, mostly due to the repetitive nature of the genome and the difficulty of assigning correct mapping positions to short reads. This study describes a method to efficiently identify large tumor-specific deletions, inversions, duplications and translocations from low coverage data using SVDetect or BreakDancer software and a set of novel filtering procedures designed to reduce false positive calls. Applying our method to a spontaneous T cell lymphoma arising in a core RAG2/p53-deficient mouse, we identified 40 validated tumor-specific structural rearrangements supported by as few as 2 independent read pairs.     

Identification of rare DNA variants in mitochondrial disorders with improved array-based sequencing

A common goal in the discovery of rare functional DNA variants via medical resequencing is to incur a relatively lower proportion of false positive base-calls. We developed a novel statistical method for resequencing arrays (SRMA, sequence robust multi-array analysis) to increase the accuracy of detecting rare variants and reduce the costs in subsequent sequence verifications required in medical applications. SRMA includes single and multi-array analysis and accounts for technical variables as well as the possibility of both low- and high-frequency genomic variation. The confidence of each base-call was ranked using two quality measures. In comparison to Sanger capillary sequencing, we achieved a false discovery rate of 2% (false positive rate 1.2 × 10⁻⁵, false negative rate 5%), which is similar to automated second-generation sequencing technologies. Applied to the analysis of 39 nuclear candidate genes in disorders of mitochondrial DNA (mtDNA) maintenance, we confirmed mutations in the DNA polymerase gamma POLG in positive control cases, and identified novel rare variants in previously undiagnosed cases in the mitochondrial topoisomerase TOP1MT, the mismatch repair enzyme MUTYH, and the apurinic-apyrimidinic endonuclease APEX2. Some patients carried rare heterozygous variants in several functionally interacting genes, which could indicate synergistic genetic effects in these clinically similar disorders.