@article {847, title = {Chromosome-scale, haplotype-resolved assembly of human genomes.}, journal = {Nat Biotechnol}, volume = {39}, year = {2021}, month = {2021 03}, pages = {309-312}, abstract = {

Haplotype-resolved or phased genome assembly provides a complete picture of genomes and their complex genetic variations. However, current algorithms for phased assembly either do not generate chromosome-scale phasing or require pedigree information, which limits their application. We present a method named diploid assembly (DipAsm) that uses long, accurate reads and long-range conformation data for single individuals to generate a chromosome-scale phased assembly within 1 day. Applied to four public human genomes, PGP1, HG002, NA12878 and HG00733, DipAsm produced haplotype-resolved assemblies with minimum contig length needed to cover 50\% of the known genome (NG50) up to 25 Mb and phased ~99.5\% of heterozygous sites at 98-99\% accuracy, outperforming other approaches in terms of both contiguity and phasing completeness. We demonstrate the importance of chromosome-scale phased assemblies for the discovery of structural variants (SVs), including thousands of new transposon insertions, and of highly polymorphic and medically important regions such as the human leukocyte antigen (HLA) and killer cell immunoglobulin-like receptor (KIR) regions. DipAsm will facilitate high-quality precision medicine and studies of individual haplotype variation and population diversity.

}, keywords = {Algorithms, Chromosomes, Human, Genome, Human, Haplotypes, Heterozygote, Humans, Polymorphism, Single Nucleotide}, issn = {1546-1696}, doi = {10.1038/s41587-020-0711-0}, author = {Garg, Shilpa and Fungtammasan, Arkarachai and Carroll, Andrew and Chou, Mike and Schmitt, Anthony and Zhou, Xiang and Mac, Stephen and Peluso, Paul and Hatas, Emily and Ghurye, Jay and Maguire, Jared and Mahmoud, Medhat and Cheng, Haoyu and Heller, David and Zook, Justin M and Moemke, Tobias and Marschall, Tobias and Sedlazeck, Fritz J and Aach, John and Chin, Chen-Shan and Church, George M and Li, Heng} } @article {872, title = {Rapid, Paralog-Sensitive CNV Analysis of 2457 Human Genomes Using QuicK-mer2.}, journal = {Genes (Basel)}, volume = {11}, year = {2020}, month = {2020 01 29}, abstract = {

Gene duplication is a major mechanism for the evolution of gene novelty, and copy-number variation makes a major contribution to inter-individual genetic diversity. However, most approaches for studying copy-number variation rely upon uniquely mapping reads to a genome reference and are unable to distinguish among duplicated sequences. Specialized approaches to interrogate specific paralogs are comparatively slow and have a high degree of computational complexity, limiting their effective application to emerging population-scale data sets. We present QuicK-mer2, a self-contained, mapping-free approach that enables the rapid construction of paralog-specific copy-number maps from short-read sequence data. This approach is based on the tabulation of unique k-mer sequences from short-read data sets, and is able to analyze a 20X coverage human genome in approximately 20 min. We applied our approach to newly released sequence data from the 1000 Genomes Project, constructed paralog-specific copy-number maps from 2457 unrelated individuals, and uncovered copy-number variation of paralogous genes. We identify nine genes where none of the analyzed samples have a copy number of two, 92 genes where the majority of samples have a copy number other than two, and describe rare copy number variation effecting multiple genes at the APOBEC3 locus.

}, keywords = {Algorithms, Computational Biology, DNA Copy Number Variations, Evolution, Molecular, Gene Duplication, Genome, Human, Humans, Sequence Analysis, DNA}, issn = {2073-4425}, doi = {10.3390/genes11020141}, author = {Shen, Feichen and Kidd, Jeffrey M} } @article {551, title = {Ximmer: a system for improving accuracy and consistency of CNV calling from exome data.}, journal = {Gigascience}, volume = {7}, year = {2018}, month = {2018 10 01}, abstract = {

Background: While exome and targeted next-generation DNA sequencing are primarily used for detecting single nucleotide changes and small indels, detection of copy number variants (CNVs) can provide highly valuable additional information from the data. Although there are dozens of exome CNV detection methods available, these are often difficult to use, and accuracy varies unpredictably between and within datasets.

Findings: We present Ximmer, a tool that supports an end-to-end process for evaluating, tuning, and running analysis methods for detection of CNVs in germline samples. Ximmer includes a simulation framework, implementations of several commonly used CNV detection methods, and a visualization and curation tool that together enable interactive exploration and quality control of CNV results. Using Ximmer, we comprehensively evaluate CNV detection on four datasets using five different detection methods. We show that application of Ximmer can improve accuracy and aid in quality control of CNV detection results. In addition, Ximmer can be used to run analyses and explore CNV results in exome data.

Conclusions: Ximmer offers a comprehensive tool and method for applying and improving accuracy of CNV detection methods for exome data.

}, keywords = {Algorithms, Computational Biology, Computer Simulation, DNA Copy Number Variations, Exome, High-Throughput Nucleotide Sequencing, Humans, Reproducibility of Results, ROC Curve, Sensitivity and Specificity, Software}, issn = {2047-217X}, doi = {10.1093/gigascience/giy112}, author = {Sadedin, Simon P and Ellis, Justine A and Masters, Seth L and Oshlack, Alicia} } @article {372, title = {Homozygous and hemizygous CNV detection from exome sequencing data in a Mendelian disease cohort.}, journal = {Nucleic Acids Res}, volume = {45}, year = {2017}, month = {2017 02 28}, pages = {1633-1648}, abstract = {

We developed an algorithm, HMZDelFinder, that uses whole exome sequencing (WES) data to identify rare and intragenic homozygous and hemizygous (HMZ) deletions that may represent complete loss-of-function of the indicated gene. HMZDelFinder was applied to 4866 samples in the Baylor-Hopkins Center for Mendelian Genomics (BHCMG) cohort and detected 773 HMZ deletion calls (567 homozygous or 206 hemizygous) with an estimated sensitivity of 86.5\% (82\% for single-exonic and 88\% for multi-exonic calls) and precision of 78\% (53\% single-exonic and 96\% for multi-exonic calls). Out of 773 HMZDelFinder-detected deletion calls, 82 were subjected to array comparative genomic hybridization (aCGH) and/or breakpoint PCR and 64 were confirmed. These include 18 single-exon deletions out of which 8 were exclusively detected by HMZDelFinder and not by any of seven other CNV detection tools examined. Further investigation of the 64 validated deletion calls revealed at least 15 pathogenic HMZ deletions. Of those, 7 accounted for 17-50\% of pathogenic CNVs in different disease cohorts where 7.1-11\% of the molecular diagnosis solved rate was attributed to CNVs. In summary, we present an algorithm to detect rare, intragenic, single-exon deletion CNVs using WES data; this tool can be useful for disease gene discovery efforts and clinical WES analyses.

}, keywords = {Algorithms, Alternative Splicing, Cohort Studies, Computational Biology, Consanguinity, Datasets as Topic, DNA Copy Number Variations, Exome, Genetic Diseases, Inborn, Hemizygote, High-Throughput Nucleotide Sequencing, Homozygote, Humans, Inheritance Patterns, Models, Genetic, Pedigree, Reproducibility of Results, Sequence Deletion, Workflow}, issn = {1362-4962}, doi = {10.1093/nar/gkw1237}, author = {Gambin, Tomasz and Akdemir, Zeynep C and Yuan, Bo and Gu, Shen and Chiang, Theodore and Carvalho, Claudia M B and Shaw, Chad and Jhangiani, Shalini and Boone, Philip M and Eldomery, Mohammad K and Karaca, Ender and Bayram, Yavuz and Stray-Pedersen, Asbj{\o}rg and Muzny, Donna and Charng, Wu-Lin and Bahrambeigi, Vahid and Belmont, John W and Boerwinkle, Eric and Beaudet, Arthur L and Gibbs, Richard A and Lupski, James R} } @article {43, title = {PADRE: Pedigree-Aware Distant-Relationship Estimation.}, journal = {Am J Hum Genet}, volume = {99}, year = {2016}, month = {2016 Jul 07}, pages = {154-62}, abstract = {

Accurate estimation of shared ancestry is an important component of many genetic studies; current prediction tools accurately estimate pairwise genetic relationships up to the ninth degree. Pedigree-aware distant-relationship estimation (PADRE) combines relationship likelihoods generated by estimation of recent shared ancestry (ERSA) with likelihoods from family networks reconstructed by pedigree reconstruction and identification of a maximum unrelated set (PRIMUS), improving the power to detect distant relationships between pedigrees. Using PADRE, we estimated relationships from simulated pedigrees and three extended pedigrees, correctly predicting 20\% more fourth- through ninth-degree simulated relationships than when using ERSA alone. By leveraging pedigree information, PADRE can even identify genealogical relationships between individuals who are genetically unrelated. For example, although 95\% of 13(th)-degree relatives are genetically unrelated, in simulations, PADRE correctly predicted 50\% of 13(th)-degree relationships to within one degree of relatedness. The improvement in prediction accuracy was consistent between simulated and actual pedigrees. We also applied PADRE to the HapMap3 CEU samples and report new cryptic relationships and validation of previously described relationships between families. PADRE greatly expands the range of relationships that can be estimated by using genetic data in pedigrees.

}, keywords = {Algorithms, Female, Haplotypes, Humans, Male, Models, Genetic, Pedigree, Reproducibility of Results}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2016.05.020}, author = {Staples, Jeffrey and Witherspoon, David J and Jorde, Lynn B and Nickerson, Deborah A and Below, Jennifer E and Huff, Chad D} } @article {123, title = {PRIMUS: improving pedigree reconstruction using mitochondrial and Y haplotypes.}, journal = {Bioinformatics}, volume = {32}, year = {2016}, month = {2016 Feb 15}, pages = {596-8}, abstract = {

UNLABELLED: PRIMUS is a pedigree reconstruction algorithm that uses estimates of genome-wide identity by descent to reconstruct pedigrees consistent with observed genetic data. However, when genetic data for individuals within a pedigree are missing, often multiple pedigrees can be reconstructed that fit the data. We report a major expansion of PRIMUS that uses mitochondrial (mtDNA) and non-recombining Y chromosome (NRY) haplotypes to eliminate many pedigree structures that are inconsistent with the genetic data. We demonstrate that discordances in mtDNA and NRY haplotypes substantially reduce the number of potential pedigrees, and often lead to the identification of the correct pedigree.

AVAILABILITY AND IMPLEMENTATION: We have implemented PRIMUS updates in PERL and it is available at primus.gs.washington.edu.

}, keywords = {Algorithms, Chromosomes, Human, Y, Computer Simulation, DNA, Mitochondrial, Genetics, Population, Haplotypes, Humans, Linkage Disequilibrium, Pedigree, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btv618}, author = {Staples, Jeffrey and Ekunwe, Lynette and Lange, Ethan and Wilson, James G and Nickerson, Deborah A and Below, Jennifer E} } @article {182, title = {Genetic linkage analysis in the age of whole-genome sequencing.}, journal = {Nat Rev Genet}, volume = {16}, year = {2015}, month = {2015 May}, pages = {275-84}, abstract = {

For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches. Here, we review the principles of linkage analysis and provide practical guidelines for carrying out linkage studies using WGS data.

}, keywords = {Algorithms, Female, Genetic Linkage, Genome, Human, Genome-Wide Association Study, Genotyping Techniques, Humans, Lod Score, Male, Pedigree, Penetrance, Phenotype, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Software}, issn = {1471-0064}, doi = {10.1038/nrg3908}, author = {Ott, Jurg and Wang, Jing and Leal, Suzanne M} } @article {136, title = {The Matchmaker Exchange API: automating patient matching through the exchange of structured phenotypic and genotypic profiles.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Oct}, pages = {922-7}, abstract = {

Despite the increasing prevalence of clinical sequencing, the difficulty of identifying additional affected families is a key obstacle to solving many rare diseases. There may only be a handful of similar patients worldwide, and their data may be stored in diverse clinical and research databases. Computational methods are necessary to enable finding similar patients across the growing number of patient repositories and registries. We present the Matchmaker Exchange Application Programming Interface (MME API), a protocol and data format for exchanging phenotype and genotype profiles to enable matchmaking among patient databases, facilitate the identification of additional cohorts, and increase the rate with which rare diseases can be researched and diagnosed. We designed the API to be straightforward and flexible in order to simplify its adoption on a large number of data types and workflows. We also provide a public test data set, curated from the literature, to facilitate implementation of the API and development of new matching algorithms. The initial version of the API has been successfully implemented by three members of the Matchmaker Exchange and was immediately able to reproduce previously identified matches and generate several new leads currently being validated. The API is available at https://github.com/ga4gh/mme-apis.

}, keywords = {Algorithms, Computational Biology, Databases, Genetic, Genetic Predisposition to Disease, Genotype, Humans, Information Dissemination, Phenotype, Rare Diseases, Web Browser}, issn = {1098-1004}, doi = {10.1002/humu.22850}, author = {Buske, Orion J and Schiettecatte, Fran{\c c}ois and Hutton, Benjamin and Dumitriu, Sergiu and Misyura, Andriy and Huang, Lijia and Hartley, Taila and Girdea, Marta and Sobreira, Nara and Mungall, Chris and Brudno, Michael} } @article {277, title = {DIAMUND: direct comparison of genomes to detect mutations.}, journal = {Hum Mutat}, volume = {35}, year = {2014}, month = {2014 Mar}, pages = {283-8}, abstract = {

DNA sequencing has become a powerful method to discover the genetic basis of disease. Standard, widely used protocols for analysis usually begin by comparing each individual to the human reference genome. When applied to a set of related individuals, this approach reveals millions of differences, most of which are shared among the individuals and unrelated to the disease being investigated. We have developed a novel algorithm for variant detection, one that compares DNA sequences directly to one another, without aligning them to the reference genome. When used to find de novo mutations in exome sequences from family trios, or to compare normal and diseased samples from the same individual, the new method, direct alignment for mutation discovery (DIAMUND), produces a dramatically smaller list of candidate mutations than previous methods, without losing sensitivity to detect the true cause of a genetic disease. We demonstrate our results on several example cases, including two family trios in which it correctly found the disease-causing variant while excluding thousands of harmless variants that standard methods had identified.

}, keywords = {Algorithms, Computational Biology, Exome, Genetic Loci, Genetic Variation, Genome, Human, Humans, Mutation, Sequence Alignment, Sequence Analysis, DNA, Software}, issn = {1098-1004}, doi = {10.1002/humu.22503}, author = {Salzberg, Steven L and Pertea, Mihaela and Fahrner, Jill A and Sobreira, Nara} } @article {320, title = {Utilizing graph theory to select the largest set of unrelated individuals for genetic analysis.}, journal = {Genet Epidemiol}, volume = {37}, year = {2013}, month = {2013 Feb}, pages = {136-41}, abstract = {

Many statistical analyses of genetic data rely on the assumption of independence among samples. Consequently, relatedness is either modeled in the analysis or samples are removed to "clean" the data of any pairwise relatedness above a tolerated threshold. Current methods do not maximize the number of unrelated individuals retained for further analysis, and this is a needless loss of resources. We report a novel application of graph theory that identifies the maximum set of unrelated samples in any dataset given a user-defined threshold of relatedness as well as all networks of related samples. We have implemented this method into an open source program called Pedigree Reconstruction and Identification of a Maximum Unrelated Set, PRIMUS. We show that PRIMUS outperforms the three existing methods, allowing researchers to retain up to 50\% more unrelated samples. A unique strength of PRIMUS is its ability to weight the maximum clique selection using additional criteria (e.g. affected status and data missingness). PRIMUS is a permanent solution to identifying the maximum number of unrelated samples for a genetic analysis.

}, keywords = {Algorithms, Genome-Wide Association Study, HapMap Project, Humans, Models, Theoretical}, issn = {1098-2272}, doi = {10.1002/gepi.21684}, author = {Staples, Jeffrey and Nickerson, Deborah A and Below, Jennifer E} }