@article {885, title = {Evaluation of the VISAGE Basic Tool for Appearance and Ancestry Prediction Using PowerSeq Chemistry on the MiSeq FGx System.}, journal = {Genes (Basel)}, volume = {11}, year = {2020}, month = {2020 06 26}, abstract = {

The study of DNA to predict externally visible characteristics (EVCs) and the biogeographical ancestry (BGA) from unknown samples is gaining relevance in forensic genetics. Technical developments in Massively Parallel Sequencing (MPS) enable the simultaneous analysis of hundreds of DNA markers, which improves successful Forensic DNA Phenotyping (FDP). The EU-funded VISAGE (VISible Attributes through GEnomics) Consortium has developed various targeted MPS-based lab tools to apply FDP in routine forensic analyses. Here, we present an evaluation of the VISAGE Basic tool for appearance and ancestry prediction based on PowerSeq chemistry (Promega) on a MiSeq FGx System (Illumina). The panel consists of 153 single nucleotide polymorphisms (SNPs) that provide information about EVCs (41 SNPs for eye, hair and skin color from HIrisPlex-S) and continental BGA (115 SNPs; three overlap with the EVCs SNP set). The assay was evaluated for sensitivity, repeatability and genotyping concordance, as well as its performance with casework-type samples. This targeted MPS assay provided complete genotypes at all 153 SNPs down to 125 pg of input DNA and 99.67\% correct genotypes at 50 pg. It was robust in terms of repeatability and concordance and provided useful results with casework-type samples. The results suggest that this MPS assay is a useful tool for basic appearance and ancestry prediction in forensic genetics for users interested in applying PowerSeq chemistry and MiSeq for this purpose.

}, keywords = {DNA Fingerprinting, Eye Color, Forensic Genetics, Genetic Markers, Genotype, Hair Color, High-Throughput Nucleotide Sequencing, Humans, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Skin Pigmentation, Software}, issn = {2073-4425}, doi = {10.3390/genes11060708}, author = {Palencia-Madrid, Leire and Xavier, Catarina and de la Puente, Maria and Hohoff, Carsten and Phillips, Christopher and Kayser, Manfred and Parson, Walther} } @article {839, title = {Mapping and characterization of structural variation in 17,795 human genomes.}, journal = {Nature}, volume = {583}, year = {2020}, month = {2020 07}, pages = {83-89}, abstract = {

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2\% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2\% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90\% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2\% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.

}, keywords = {Alleles, Case-Control Studies, Continental Population Groups, Epigenesis, Genetic, Female, Gene Dosage, Genetic Variation, Genetics, Population, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Male, Molecular Sequence Annotation, Quantitative Trait Loci, Software, Whole Genome Sequencing}, issn = {1476-4687}, doi = {10.1038/s41586-020-2371-0}, author = {Abel, Haley J and Larson, David E and Regier, Allison A and Chiang, Colby and Das, Indraniel and Kanchi, Krishna L and Layer, Ryan M and Neale, Benjamin M and Salerno, William J and Reeves, Catherine and Buyske, Steven and Matise, Tara C and Muzny, Donna M and Zody, Michael C and Lander, Eric S and Dutcher, Susan K and Stitziel, Nathan O and Hall, Ira M} } @article {525, title = {MendelProb: probability and sample size calculations for Mendelian studies of exome and whole genome sequence data.}, journal = {Bioinformatics}, volume = {35}, year = {2019}, month = {2019 02 01}, pages = {529-531}, abstract = {

Motivation: For the design of genetic studies, it is necessary to perform power calculations. Although for Mendelian traits the power of detecting linkage for pedigree(s) can be determined, it is also of great interest to determine the probability of identifying multiple pedigrees or unrelated cases with variants in the same gene. For many diseases, due to extreme locus heterogeneity this probability can be small. If only one family is observed segregating a variant classified as likely pathogenic or of unknown significance, the gene cannot be implicated in disease etiology. The probability of identifying several disease families or cases is dependent on the gene-specific disease prevalence and the sample size. The observation of multiple disease families or cases with variants in the same gene as well as evidence of pathogenicity from other sources, e.g. in silico prediction, expression and functional studies, can aid in implicating a gene in disease etiology. MendelProb can determine the probability of detecting a minimum number of families or cases with variants in the same gene. It can also calculate the probability of detecting genes with variants in different data types, e.g. identifying a variant in at least one family that can establish linkage and more the two additional families regardless of their size. Additionally, for a specified probability MendelProb can determine the number of probands which need to be screened to detect a minimum number of individuals with variants within the same gene.

Results: A single Mendelian disease family is not sufficient to implicate a gene in disease etiology. It is necessary to observe multiple families or cases with potentially pathogenic variants in the same gene. MendelProb, an R library, was developed to determine the probability of observing multiple families and cases with variants within a gene and to also establish the numbers of probands to screen to detect multiple observations of variants within a gene.

Availability and implementation: https://github.com/statgenetics/mendelprob.

}, keywords = {Exome, Genetic Linkage, Genomics, Humans, Pedigree, Probability, Sample Size, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bty542}, author = {He, Zongxiao and Wang, Lu and DeWan, Andrew T and Leal, Suzanne M} } @article {501, title = {STRetch: detecting and discovering pathogenic short tandem repeat expansions.}, journal = {Genome Biol}, volume = {19}, year = {2018}, month = {2018 08 21}, pages = {121}, abstract = {

Short tandem repeat (STR) expansions have been identified as the causal DNA mutation in dozens of Mendelian diseases. Most existing tools for detecting STR variation with short reads do so within the read length and so are unable to detect the majority of pathogenic expansions. Here we present STRetch, a new genome-wide method to scan for STR expansions at all loci across the human genome. We demonstrate the use of STRetch for detecting STR expansions using short-read whole-genome sequencing data at known pathogenic loci as well as novel STR loci. STRetch is open source software, available from github.com/Oshlack/STRetch .

}, keywords = {Alleles, Chromosomes, Human, DNA Repeat Expansion, Genetic Loci, Genome, Human, Humans, Microsatellite Repeats, Polymerase Chain Reaction, Software}, issn = {1474-760X}, doi = {10.1186/s13059-018-1505-2}, author = {Dashnow, Harriet and Lek, Monkol and Phipson, Belinda and Halman, Andreas and Sadedin, Simon and Lonsdale, Andrew and Davis, Mark and Lamont, Phillipa and Clayton, Joshua S and Laing, Nigel G and MacArthur, Daniel G and Oshlack, Alicia} } @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 {440, title = {Matchmaker Exchange.}, journal = {Curr Protoc Hum Genet}, volume = {95}, year = {2017}, month = {2017 10 18}, pages = {9.31.1-9.31.15}, abstract = {

In well over half of the individuals with rare disease who undergo clinical or research next-generation sequencing, the responsible gene cannot be determined. Some reasons for this relatively low yield include unappreciated phenotypic heterogeneity; locus heterogeneity; somatic and germline mosaicism; variants of uncertain functional significance; technically inaccessible areas of the genome; incorrect mode of inheritance investigated; and inadequate communication between clinicians and basic scientists with knowledge of particular genes, proteins, or biological systems. To facilitate such communication and improve the search for patients or model organisms with similar phenotypes and variants in specific candidate genes, we have developed the Matchmaker Exchange (MME). MME was created to establish a federated network connecting databases of genomic and phenotypic data using a common application programming interface (API). To date, seven databases can exchange data using the API (GeneMatcher, PhenomeCentral, DECIPHER, MyGene2, matchbox, Australian Genomics Health Alliance Patient Archive, and Monarch Initiative; the latter included for model organism matching). This article guides usage of the MME for rare disease gene discovery. {\textcopyright} 2017 by John Wiley \& Sons, Inc.

}, keywords = {Animals, Computational Biology, Databases, Genetic, Genetic Association Studies, Genetic Predisposition to Disease, Genomics, Humans, Rare Diseases, Software, Web Browser}, issn = {1934-8258}, doi = {10.1002/cphg.50}, author = {Sobreira, Nara L M and Arachchi, Harindra and Buske, Orion J and Chong, Jessica X and Hutton, Ben and Foreman, Julia and Schiettecatte, Fran{\c c}ois and Groza, Tudor and Jacobsen, Julius O B and Haendel, Melissa A and Boycott, Kym M and Hamosh, Ada and Rehm, Heidi L} } @article {68, title = {Identifying Allosteric Hotspots with Dynamics: Application to Inter- and Intra-species Conservation.}, journal = {Structure}, volume = {24}, year = {2016}, month = {2016 05 03}, pages = {826-837}, abstract = {

The rapidly growing volume of data being produced by next-generation sequencing initiatives is enabling more in-depth analyses of conservation than previously possible. Deep sequencing is uncovering disease loci and regions under selective constraint, despite the fact that intuitive biophysical reasons for such constraint are sometimes absent. Allostery may often provide the missing explanatory link. We use models of protein conformational change to identify allosteric residues by finding essential surface pockets and information-flow bottlenecks, and we develop a software tool that enables users to perform this analysis on their own proteins of interest. Though fundamentally 3D-structural in nature, our analysis is computationally fast, thereby allowing us to run it across the PDB and to evaluate general properties of predicted allosteric residues. We find that these tend to be conserved over diverse evolutionary time scales. Finally, we highlight examples of allosteric residues that help explain poorly understood disease-associated variants.

}, keywords = {Allosteric Site, Animals, Conserved Sequence, Humans, Proteome, Sequence Analysis, Protein, Software}, issn = {1878-4186}, doi = {10.1016/j.str.2016.03.008}, author = {Clarke, Declan and Sethi, Anurag and Li, Shantao and Kumar, Sushant and Chang, Richard W F and Chen, Jieming and Gerstein, Mark} } @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 {88, title = {A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics.}, journal = {Genome Med}, volume = {8}, year = {2016}, month = {2016 Feb 02}, pages = {13}, abstract = {

BACKGROUND: Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives.

METHODS: Our tool, "OMIM Explorer" ( http://www.omimexplorer.com ), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries--in essence, generating a computationally assisted differential diagnosis informed by the individual{\textquoteright}s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes.

RESULTS: We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 \% of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants.

CONCLUSIONS: Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge.

}, keywords = {Computational Biology, Diagnosis, Differential, Disease, Genetic Variation, Genome-Wide Association Study, Humans, Phenotype, Pilot Projects, Retrospective Studies, Software}, issn = {1756-994X}, doi = {10.1186/s13073-016-0261-8}, author = {James, Regis A and Campbell, Ian M and Chen, Edward S and Boone, Philip M and Rao, Mitchell A and Bainbridge, Matthew N and Lupski, James R and Yang, Yaping and Eng, Christine M and Posey, Jennifer E and Shaw, Chad A} } @article {177, title = {Assessing structural variation in a personal genome-towards a human reference diploid genome.}, journal = {BMC Genomics}, volume = {16}, year = {2015}, month = {2015 Apr 11}, pages = {286}, abstract = {

BACKGROUND: Characterizing large genomic variants is essential to expanding the research and clinical applications of genome sequencing. While multiple data types and methods are available to detect these structural variants (SVs), they remain less characterized than smaller variants because of SV diversity, complexity, and size. These challenges are exacerbated by the experimental and computational demands of SV analysis. Here, we characterize the SV content of a personal genome with Parliament, a publicly available consensus SV-calling infrastructure that merges multiple data types and SV detection methods.

RESULTS: We demonstrate Parliament{\textquoteright}s efficacy via integrated analyses of data from whole-genome array comparative genomic hybridization, short-read next-generation sequencing, long-read (Pacific BioSciences RSII), long-insert (Illumina Nextera), and whole-genome architecture (BioNano Irys) data from the personal genome of a single subject (HS1011). From this genome, Parliament identified 31,007 genomic loci between 100 bp and 1 Mbp that are inconsistent with the hg19 reference assembly. Of these loci, 9,777 are supported as putative SVs by hybrid local assembly, long-read PacBio data, or multi-source heuristics. These SVs span 59 Mbp of the reference genome (1.8\%) and include 3,801 events identified only with long-read data. The HS1011 data and complete Parliament infrastructure, including a BAM-to-SV workflow, are available on the cloud-based service DNAnexus.

CONCLUSIONS: HS1011 SV analysis reveals the limits and advantages of multiple sequencing technologies, specifically the impact of long-read SV discovery. With the full Parliament infrastructure, the HS1011 data constitute a public resource for novel SV discovery, software calibration, and personal genome structural variation analysis.

}, keywords = {Computational Biology, Databases, Genetic, Diploidy, Genome, Human, Genomic Structural Variation, Humans, Sequence Analysis, DNA, Software}, issn = {1471-2164}, doi = {10.1186/s12864-015-1479-3}, author = {English, Adam C and Salerno, William J and Hampton, Oliver A and Gonzaga-Jauregui, Claudia and Ambreth, Shruthi and Ritter, Deborah I and Beck, Christine R and Davis, Caleb F and Dahdouli, Mahmoud and Ma, Singer and Carroll, Andrew and Veeraraghavan, Narayanan and Bruestle, Jeremy and Drees, Becky and Hastie, Alex and Lam, Ernest T and White, Simon and Mishra, Pamela and Wang, Min and Han, Yi and Zhang, Feng and Stankiewicz, Pawel and Wheeler, David A and Reid, Jeffrey G and Muzny, Donna M and Rogers, Jeffrey and Sabo, Aniko and Worley, Kim C and Lupski, James R and Boerwinkle, Eric and Gibbs, Richard A} } @article {178, title = {Collapsed haplotype pattern method for linkage analysis of next-generation sequence data.}, journal = {Eur J Hum Genet}, volume = {23}, year = {2015}, month = {2015 Dec}, pages = {1739-43}, abstract = {

Recent advances in next-generation sequencing (NGS) make it possible to directly sequence genomes and exomes of individuals with Mendelian diseases and screen sequence data for causal variants. With the reduction in cost of NGS, DNA samples from entire families can be sequenced and linkage analysis can be performed directly using NGS data. Inspired by {\textquoteright}burden{\textquoteright} tests, which are used for complex trait rare variant association studies, we developed the collapsed haplotype pattern (CHP) method for linkage analysis. Using data from several deafness genes we demonstrate that the CHP method is substantially more powerful than analyzing individual variants. Unlike applying NGS data filtering approaches, the CHP method provides statistical evidence of a gene{\textquoteright}s involvement in disease etiology and is also less likely to exclude causal variants in the presence of phenocopies and/or reduced penetrance. The CHP method was implemented in the SEQLinkage software package, which can perform linkage analysis on NGS data or can generate data compatible with many linkage analysis programs, reviving them for use in NGS era.

}, keywords = {Chromosome Mapping, Genetic Linkage, Haplotypes, Hearing Loss, High-Throughput Nucleotide Sequencing, Humans, Sequence Analysis, DNA, Software}, issn = {1476-5438}, doi = {10.1038/ejhg.2015.64}, author = {Wang, Gao T and Zhang, Di and Li, Biao and Dai, Hang and Leal, Suzanne M} } @article {139, title = {GeneMatcher: a matching tool for connecting investigators with an interest in the same gene.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Oct}, pages = {928-30}, abstract = {

Here, we describe an overview and update on GeneMatcher (http://www.genematcher.org), a freely accessible Web-based tool developed as part of the Baylor-Hopkins Center for Mendelian Genomics. We created GeneMatcher with the goal of identifying additional individuals with rare phenotypes who had variants in the same candidate disease gene. We also wanted to facilitate connections to basic scientists working on orthologous genes in model systems with the goal of connecting their work to human Mendelian phenotypes. Meeting these goals will enhance the identification of novel Mendelian genes. Launched in September, 2013, GeneMatcher now has 2,178 candidate genes from 486 submitters spread across 38 countries entered in the database (June 1, 2015). GeneMatcher is also part of the Matchmaker Exchange (http://matchmakerexchange.org/) with an Application Programing Interface enabling submitters to query other databases of genetic variants and phenotypes without having to create accounts and data entries in multiple systems.

}, keywords = {Computational Biology, Databases, Genetic, Genetic Predisposition to Disease, Genetic Variation, Humans, Information Dissemination, Phenotype, Rare Diseases, Software, User-Computer Interface, Web Browser}, issn = {1098-1004}, doi = {10.1002/humu.22844}, author = {Sobreira, Nara and Schiettecatte, Fran{\c c}ois and Valle, David and Hamosh, Ada} } @article {146, title = {GeneMatcher aids in the identification of a new malformation syndrome with intellectual disability, unique facial dysmorphisms, and skeletal and connective tissue abnormalities caused by de novo variants in HNRNPK.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Oct}, pages = {1009-1014}, abstract = {

We report a new syndrome due to loss-of-function variants in the heterogeneous nuclear ribonucleoprotein K gene (HNRNPK). We describe two probands: one with a de novo frameshift (NM_002140.3: c.953+1dup), and the other with a de novo splice donor site variant (NM_002140.3: c.257G>A). Both probands have intellectual disability, a shared unique craniofacial phenotype, and connective tissue and skeletal abnormalities. The identification of this syndrome was made possible by a new online tool, GeneMatcher, which facilitates connections between clinicians and researchers based on shared interest in candidate genes. This report demonstrates that new Web-based approaches can be effective in helping investigators solve exome sequencing projects, and also highlights the newer paradigm of "reverse phenotyping," where characterization of syndromic features follows the identification of genetic variants.

}, keywords = {Abnormalities, Multiple, Adolescent, Child, Craniofacial Abnormalities, Databases, Genetic, Genetic Predisposition to Disease, Heterogeneous-Nuclear Ribonucleoprotein K, Humans, Information Dissemination, Intellectual Disability, Male, Muscular Atrophy, Phenotype, Polymorphism, Single Nucleotide, Ribonucleoproteins, Software, Web Browser}, issn = {1098-1004}, doi = {10.1002/humu.22837}, author = {Au, P Y Billie and You, Jing and Caluseriu, Oana and Schwartzentruber, Jeremy and Majewski, Jacek and Bernier, Francois P and Ferguson, Marcia and Valle, David and Parboosingh, Jillian S and Sobreira, Nara and Innes, A Micheil and Kline, Antonie D} } @article {145, title = {Generation of sequence-based data for pedigree-segregating Mendelian or Complex traits.}, journal = {Bioinformatics}, volume = {31}, year = {2015}, month = {2015 Nov 15}, pages = {3706-8}, abstract = {

MOTIVATION: There is great interest in analyzing next generation sequence data that has been generated for pedigrees. However, unlike for population-based data there are only a limited number of rare variant methods to analyze pedigree data. One limitation is the ability to evaluate type I and II errors for family-based methods, due to lack of software that can simulate realistic sequence data for pedigrees.

SUMMARY: We developed RarePedSim (Rare-variant Pedigree-based Simulator), a program to simulate region/gene-level genotype and phenotype data for complex and Mendelian traits for any given pedigree structure. Using a genetic model, sequence variant data can be generated either conditionally or unconditionally on pedigree members{\textquoteright} qualitative or quantitative phenotypes. Additionally, qualitative or quantitative traits can be generated conditional on variant data. Sequence data can either be simulated using realistic population demographic models or obtained from sequence-based studies. Variant sites can be annotated with positions, allele frequencies and functionality. For rare variants, RarePedSim is the only program that can efficiently generate both genotypes and phenotypes, regardless of pedigree structure. Data generated by RarePedSim are in standard Linkage file (.ped) and Variant Call (.vcf) formats, ready to be used for a variety of purposes, including evaluation of type I error and power, for association methods including mixed models and linkage analysis methods.

AVAILABILITY AND IMPLEMENTATION: bioinformatics.org/simped/rare

CONTACT: sleal@bcm.edu.

}, keywords = {Base Sequence, Chromosome Segregation, Humans, Pedigree, Quantitative Trait, Heritable, Sequence Analysis, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btv412}, author = {Li, Biao and Wang, Gao T and Leal, Suzanne M} } @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 {131, title = {The Matchmaker Exchange: a platform for rare disease gene discovery.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Oct}, pages = {915-21}, abstract = {

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.

}, keywords = {Database Management Systems, Databases, Genetic, Genetic Association Studies, Genetic Predisposition to Disease, Humans, Information Dissemination, Rare Diseases, Software}, issn = {1098-1004}, doi = {10.1002/humu.22858}, author = {Philippakis, Anthony A and Azzariti, Danielle R and Beltran, Sergi and Brookes, Anthony J and Brownstein, Catherine A and Brudno, Michael and Brunner, Han G and Buske, Orion J and Carey, Knox and Doll, Cassie and Dumitriu, Sergiu and Dyke, Stephanie O M and den Dunnen, Johan T and Firth, Helen V and Gibbs, Richard A and Girdea, Marta and Gonzalez, Michael and Haendel, Melissa A and Hamosh, Ada and Holm, Ingrid A and Huang, Lijia and Hurles, Matthew E and Hutton, Ben and Krier, Joel B and Misyura, Andriy and Mungall, Christopher J and Paschall, Justin and Paten, Benedict and Robinson, Peter N and Schiettecatte, Fran{\c c}ois and Sobreira, Nara L and Swaminathan, Ganesh J and Taschner, Peter E and Terry, Sharon F and Washington, Nicole L and Z{\"u}chner, Stephan and Boycott, Kym M and Rehm, Heidi L} } @article {193, title = {New tools for Mendelian disease gene identification: PhenoDB variant analysis module; and GeneMatcher, a web-based tool for linking investigators with an interest in the same gene.}, journal = {Hum Mutat}, volume = {36}, year = {2015}, month = {2015 Apr}, pages = {425-31}, abstract = {

Identifying the causative variant from among the thousands identified by whole-exome sequencing or whole-genome sequencing is a formidable challenge. To make this process as efficient and flexible as possible, we have developed a Variant Analysis Module coupled to our previously described Web-based phenotype intake tool, PhenoDB (http://researchphenodb.net and http://phenodb.org). When a small number of candidate-causative variants have been identified in a study of a particular patient or family, a second, more difficult challenge becomes proof of causality for any given variant. One approach to this problem is to find other cases with a similar phenotype and mutations in the same candidate gene. Alternatively, it may be possible to develop biological evidence for causality, an approach that is assisted by making connections to basic scientists studying the gene of interest, often in the setting of a model organism. Both of these strategies benefit from an open access, online site where individual clinicians and investigators could post genes of interest. To this end, we developed GeneMatcher (http://genematcher.org), a freely accessible Website that enables connections between clinicians and researchers across the world who share an interest in the same gene(s).

}, keywords = {Computational Biology, Databases, Genetic, Genetic Association Studies, Genetic Diseases, Inborn, Genetic Variation, Genomics, Internet, Phenotype, Software, Web Browser}, issn = {1098-1004}, doi = {10.1002/humu.22769}, author = {Sobreira, Nara and Schiettecatte, Fran{\c c}ois and Boehm, Corinne and Valle, David and Hamosh, Ada} } @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 {270, title = {Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline.}, journal = {BMC Bioinformatics}, volume = {15}, year = {2014}, month = {2014 Jan 29}, pages = {30}, abstract = {

BACKGROUND: Massively parallel DNA sequencing generates staggering amounts of data. Decreasing cost, increasing throughput, and improved annotation have expanded the diversity of genomics applications in research and clinical practice. This expanding scale creates analytical challenges: accommodating peak compute demand, coordinating secure access for multiple analysts, and sharing validated tools and results.

RESULTS: To address these challenges, we have developed the Mercury analysis pipeline and deployed it in local hardware and the Amazon Web Services cloud via the DNAnexus platform. Mercury is an automated, flexible, and extensible analysis workflow that provides accurate and reproducible genomic results at scales ranging from individuals to large cohorts.

CONCLUSIONS: By taking advantage of cloud computing and with Mercury implemented on the DNAnexus platform, we have demonstrated a powerful combination of a robust and fully validated software pipeline and a scalable computational resource that, to date, we have applied to more than 10,000 whole genome and whole exome samples.

}, keywords = {Genome, Genomics, High-Throughput Nucleotide Sequencing, Humans, Internet, Software}, issn = {1471-2105}, doi = {10.1186/1471-2105-15-30}, author = {Reid, Jeffrey G and Carroll, Andrew and Veeraraghavan, Narayanan and Dahdouli, Mahmoud and Sundquist, Andreas and English, Adam and Bainbridge, Matthew and White, Simon and Salerno, William and Buhay, Christian and Yu, Fuli and Muzny, Donna and Daly, Richard and Duyk, Geoff and Gibbs, Richard A and Boerwinkle, Eric} } @article {250, title = {Power analysis and sample size estimation for sequence-based association studies.}, journal = {Bioinformatics}, volume = {30}, year = {2014}, month = {2014 Aug 15}, pages = {2377-8}, abstract = {

MOTIVATION: Statistical methods have been developed to test for complex trait rare variant (RV) associations, in which variants are aggregated across a region, which is typically a gene. Power analysis and sample size estimation for sequence-based RV association studies are challenging because of the necessity to realistically model the underlying allelic architecture of complex diseases within a suitable analytical framework to assess the performance of a variety of RV association methods in an unbiased manner.

SUMMARY: We developed SEQPower, a software package to perform statistical power analysis for sequence-based association data under a variety of genetic variant and disease phenotype models. It aids epidemiologists in determining the best study design, sample size and statistical tests for sequence-based association studies. It also provides biostatisticians with a platform to fairly compare RV association methods and to validate and assess novel association tests.

AVAILABILITY AND IMPLEMENTATION: The SEQPower program, source code, multi-platform executables, documentation, list of association tests, examples and tutorials are available at http://bioinformatics.org/spower.

}, keywords = {Alleles, Data Interpretation, Statistical, Disease, Genetic Association Studies, Genetic Variation, Humans, Phenotype, Sample Size, Sequence Analysis, DNA, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btu296}, author = {Wang, Gao T and Li, Biao and Santos-Cortez, Regie P Lyn and Peng, Bo and Leal, Suzanne M} } @article {210, title = {PRIMUS: rapid reconstruction of pedigrees from genome-wide estimates of identity by descent.}, journal = {Am J Hum Genet}, volume = {95}, year = {2014}, month = {2014 Nov 06}, pages = {553-64}, abstract = {

Understanding and correctly utilizing relatedness among samples is essential for genetic analysis; however, managing sample records and pedigrees can often be error prone and incomplete. Data sets ascertained by random sampling often harbor cryptic relatedness that can be leveraged in genetic analyses for maximizing power. We have developed a method that uses genome-wide estimates of~pairwise identity by descent to identify families and quickly reconstruct and score all possible pedigrees that fit the genetic data by using up to third-degree relatives, and we have included it in the software package PRIMUS (Pedigree Reconstruction and Identification of the~Maximally Unrelated Set). Here, we validate its performance on simulated, clinical, and HapMap pedigrees. Among these samples, we demonstrate that PRIMUS can verify reported pedigree structures and identify cryptic relationships. Finally, we show that PRIMUS reconstructed pedigrees, all of which were previously unknown, for 203 families from a cohort collected in Starr County, TX (1,890 samples).

}, keywords = {Base Sequence, Computer Simulation, Exome, Gene Frequency, Genetics, Population, Humans, Molecular Sequence Data, Pedigree, Pulmonary Disease, Chronic Obstructive, Sequence Analysis, DNA, Software, Texas}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2014.10.005}, author = {Staples, Jeffrey and Qiao, Dandi and Cho, Michael H and Silverman, Edwin K and Nickerson, Deborah A and Below, Jennifer E} } @article {249, title = {Variant association tools for quality control and analysis of large-scale sequence and genotyping array data.}, journal = {Am J Hum Genet}, volume = {94}, year = {2014}, month = {2014 May 01}, pages = {770-83}, abstract = {

Currently there is great interest in detecting associations between complex traits and rare variants. In this report, we describe Variant Association Tools (VAT) and the VAT pipeline, which implements best practices for rare-variant association studies. Highlights of VAT include variant-site and call-level quality control (QC), summary statistics, phenotype- and genotype-based sample selection, variant annotation, selection of variants for association analysis, and a collection of rare-variant association methods for analyzing qualitative and quantitative traits. The association testing framework for VAT is regression based, which readily allows for flexible construction of association models with multiple covariates and weighting themes based on allele frequencies or predicted functionality. Additionally, pathway analyses, conditional analyses, and analyses of gene-gene and gene-environment interactions can be performed. VAT is capable of rapidly scanning through data by using multi-process computation, adaptive permutation, and simultaneously conducting association analysis via multiple methods. Results are available in text or graphic file formats and additionally can be output to relational databases for further annotation and filtering. An interface to R language also facilitates user implementation of novel association methods. The VAT{\textquoteright}s data QC and association-analysis pipeline can be applied to sequence, imputed, and genotyping array, e.g., "exome chip," data, providing a reliable and reproducible computational environment in which to analyze small- to large-scale studies with data from the latest genotyping and sequencing technologies. Application of the VAT pipeline is demonstrated through analysis of data from the 1000 Genomes project.

}, keywords = {Genetic Association Studies, Genetic Variation, Genotyping Techniques, High-Throughput Nucleotide Sequencing, Humans, Multifactorial Inheritance, Quality Control, Software}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2014.04.004}, author = {Wang, Gao T and Peng, Bo and Leal, Suzanne M} } @article {310, title = {PhenoDB: a new web-based tool for the collection, storage, and analysis of phenotypic features.}, journal = {Hum Mutat}, volume = {34}, year = {2013}, month = {2013 Apr}, pages = {566-71}, abstract = {

To interpret whole exome/genome sequence data for clinical and research purposes, comprehensive phenotypic information, knowledge of pedigree structure, and results of previous clinical testing are essential. With these requirements in mind and to meet the needs of the Centers for Mendelian Genomics project, we have developed PhenoDB (http://phenodb.net), a secure, Web-based portal for entry, storage, and analysis of phenotypic and other clinical information. The phenotypic features are organized hierarchically according to the major headings and subheadings of the Online Mendelian Inheritance in Man (OMIM{\textregistered}) clinical synopses, with further subdivisions according to structure and function. Every string allows for a free-text entry. All of the approximately 2,900 features use the preferred term from Elements of Morphology and are fully searchable and mapped to the Human Phenotype Ontology and Elements of Morphology. The PhenoDB allows for ascertainment of relevant information from a case in a family or cohort, which is then searchable by family, OMIM number, phenotypic feature, mode of inheritance, genes screened, and so on. The database can also be used to format phenotypic data for submission to dbGaP for appropriately consented individuals. PhenoDB was built using Django, an open source Web development tool, and is freely available through the Johns Hopkins McKusick-Nathans Institute of Genetic Medicine (http://phenodb.net).

}, keywords = {Databases, Factual, Genomics, Humans, Internet, Medical Informatics, Phenotype, Software}, issn = {1098-1004}, doi = {10.1002/humu.22283}, author = {Hamosh, Ada and Sobreira, Nara and Hoover-Fong, Julie and Sutton, V Reid and Boehm, Corinne and Schiettecatte, Fran{\c c}ois and Valle, David} } @article {326, title = {SEQCHIP: a powerful method to integrate sequence and genotype data for the detection of rare variant associations.}, journal = {Bioinformatics}, volume = {28}, year = {2012}, month = {2012 Jul 01}, pages = {1745-51}, abstract = {

MOTIVATION: Next-generation sequencing greatly increases the capacity to detect rare-variant complex-trait associations. However, it is still expensive to sequence a large number of samples and therefore often small datasets are used. Given cost constraints, a potentially more powerful two-step strategy is to sequence a subset of the sample to discover variants, and genotype the identified variants in the remaining sample. If only cases are sequenced, directly combining sequence and genotype data will lead to inflated type-I errors in rare-variant association analysis. Although several methods have been developed to correct for the bias, they are either underpowered or theoretically invalid. We proposed a new method SEQCHIP to integrate genotype and sequence data, which can be used with most existing rare-variant tests.

RESULTS: It is demonstrated using both simulated and real datasets that the SEQCHIP method has controlled type-I errors, and is substantially more powerful than all other currently available methods.

AVAILABILITY: SEQCHIP is implemented in an R-Package and is available at http://linkage.rockefeller.edu/suzanne/seqchip/Seqchip.html.

}, keywords = {Adenoma, Case-Control Studies, Colorectal Neoplasms, Genetic Association Studies, Genetic Variation, Genotype, Humans, Phenotype, Sequence Analysis, DNA, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bts263}, author = {Liu, Dajiang J and Leal, Suzanne M} } @article {321, title = {SimRare: a program to generate and analyze sequence-based data for association studies of quantitative and qualitative traits.}, journal = {Bioinformatics}, volume = {28}, year = {2012}, month = {2012 Oct 15}, pages = {2703-4}, abstract = {

MOTIVATION: Currently, there is great interest in detecting complex trait rare variant associations using next-generation sequence data. On a monthly basis, new rare variant association methods are published. It is difficult to evaluate these methods because there is no standard to generate data and often comparisons are biased. In order to fairly compare rare variant association methods, it is necessary to generate data using realistic population demographic and phenotypic models.

RESULT: SimRare is an interactive program that integrates generation of rare variant genotype/phenotype data and evaluation of association methods using a unified platform. Variant data are generated for gene regions using forward-time simulation that incorporates realistic population demographic and evolutionary scenarios. Phenotype data can be obtained for both case-control and quantitative traits. SimRare has a user-friendly interface that allows for easy entry of genetic and phenotypic parameters. Novel rare variant association methods implemented in R can also be imported into SimRare, to evaluate their performance and compare results, e.g. power and Type I error, with other currently available methods both numerically and graphically.

}, keywords = {Genetic Association Studies, Genetic Variation, Humans, Phenotype, Quantitative Trait, Heritable, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bts499}, author = {Li, Biao and Wang, Gao and Leal, Suzanne M} } @article {316, title = {A unified method for detecting secondary trait associations with rare variants: application to sequence data.}, journal = {PLoS Genet}, volume = {8}, year = {2012}, month = {2012}, pages = {e1003075}, abstract = {

Next-generation sequencing has made possible the detection of rare variant (RV) associations with quantitative traits (QT). Due to high sequencing cost, many studies can only sequence a modest number of selected samples with extreme QT. Therefore association testing in individual studies can be underpowered. Besides the primary trait, many clinically important secondary traits are often measured. It is highly beneficial if multiple studies can be jointly analyzed for detecting associations with commonly measured traits. However, analyzing secondary traits in selected samples can be biased if sample ascertainment is not properly modeled. Some methods exist for analyzing secondary traits in selected samples, where some burden tests can be implemented. However p-values can only be evaluated analytically via asymptotic approximations, which may not be accurate. Additionally, potentially more powerful sequence kernel association tests, variable selection-based methods, and burden tests that require permutations cannot be incorporated. To overcome these limitations, we developed a unified method for analyzing secondary trait associations with RVs (STAR) in selected samples, incorporating all RV tests. Statistical significance can be evaluated either through permutations or analytically. STAR makes it possible to apply more powerful RV tests to analyze secondary trait associations. It also enables jointly analyzing multiple cohorts ascertained under different study designs, which greatly boosts power. The performance of STAR and commonly used RV association tests were comprehensively evaluated using simulation studies. STAR was also implemented to analyze a dataset from the SardiNIA project where samples with extreme low-density lipoprotein levels were sequenced. A significant association between LDLR and systolic blood pressure was identified, which is supported by pharmacogenetic studies. In summary, for sequencing studies, STAR is an important tool for detecting secondary-trait RV associations.

}, keywords = {Blood Pressure, Computer Simulation, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Humans, Italy, Lipoproteins, LDL, Models, Genetic, Phenotype, Quantitative Trait Loci, Software}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1003075}, author = {Liu, Dajiang J and Leal, Suzanne M} }