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1.  A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data 
Nature biotechnology  2014;32(7):663-669.
High-throughput sequencing of related individuals has become an important tool for studying human disease. However, owing to technical complexity and lack of available tools, most pedigree-based sequencing studies rely on an ad hoc combination of suboptimal analyses. Here we present pedigree-VAAST (pVAAST), a disease-gene identification tool designed for high-throughput sequence data in pedigrees. pVAAST uses a sequence-based model to perform variant and gene-based linkage analysis. Linkage information is then combined with functional prediction and rare variant case-control association information in a unified statistical framework. pVAAST outperformed linkage and rare-variant association tests in simulations and identified disease-causing genes from whole-genome sequence data in three human pedigrees with dominant, recessive and de novo inheritance patterns. The approach is robust to incomplete penetrance and locus heterogeneity and is applicable to a wide variety of genetic traits. pVAAST maintains high power across studies of monogenic, high-penetrance phenotypes in a single pedigree to highly polygenic, common phenotypes involving hundreds of pedigrees.
doi:10.1038/nbt.2895
PMCID: PMC4157619  PMID: 24837662
2.  Using VAAST to Identify Disease-Associated Variants in Next-Generation Sequencing Data 
The VAAST pipeline is specifically designed to identify disease-associated alleles in next-generation sequencing data. In the protocols presented in this paper, we outline the best practices for variant prioritization using VAAST. Examples and test data are provided for case-control, small pedigree, and large pedigree analyses. These protocols will teach users the fundamentals of VAAST, VAAST 2.0, and pVAAST analyses.
doi:10.1002/0471142905.hg0614s81
PMCID: PMC4137768  PMID: 24763993
VAAST; rare-variant association test; variant classification; disease-gene identification; next-generation sequencing; genome-wide association studies; human disease; genomics; computational genomics; bioinformatics
3.  Clinical analysis of genome next-generation sequencing data using the Omicia platform 
Expert review of molecular diagnostics  2013;13(6):10.1586/14737159.2013.811907.
Aims
Next-generation sequencing is being implemented in the clinical laboratory environment for the purposes of candidate causal variant discovery in patients affected with a variety of genetic disorders. The successful implementation of this technology for diagnosing genetic disorders requires a rapid, user-friendly method to annotate variants and generate short lists of clinically relevant variants of interest. This report describes Omicia’s Opal platform, a new software tool designed for variant discovery and interpretation in a clinical laboratory environment. The software allows clinical scientists to process, analyze, interpret and report on personal genome files.
Materials & Methods
To demonstrate the software, the authors describe the interactive use of the system for the rapid discovery of disease-causing variants using three cases.
Results & Conclusion
Here, the authors show the features of the Opal system and their use in uncovering variants of clinical significance.
doi:10.1586/14737159.2013.811907
PMCID: PMC3828661  PMID: 23895124
analysis and selection tool; genome analysis; next-generation sequencing; variant annotation; variant workflows; variant annotation; whole-genome sequencing
5.  Integrating precision medicine in the study and clinical treatment of a severely mentally ill person 
PeerJ  2013;1:e177.
Background. In recent years, there has been an explosion in the number of technical and medical diagnostic platforms being developed. This has greatly improved our ability to more accurately, and more comprehensively, explore and characterize human biological systems on the individual level. Large quantities of biomedical data are now being generated and archived in many separate research and clinical activities, but there exists a paucity of studies that integrate the areas of clinical neuropsychiatry, personal genomics and brain-machine interfaces.
Methods. A single person with severe mental illness was implanted with the Medtronic Reclaim® Deep Brain Stimulation (DBS) Therapy device for Obsessive Compulsive Disorder (OCD), targeting his nucleus accumbens/anterior limb of the internal capsule. Programming of the device and psychiatric assessments occurred in an outpatient setting for over two years. His genome was sequenced and variants were detected in the Illumina Whole Genome Sequencing Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory.
Results. We report here the detailed phenotypic characterization, clinical-grade whole genome sequencing (WGS), and two-year outcome of a man with severe OCD treated with DBS. Since implantation, this man has reported steady improvement, highlighted by a steady decline in his Yale-Brown Obsessive Compulsive Scale (YBOCS) score from ∼38 to a score of ∼25. A rechargeable Activa RC neurostimulator battery has been of major benefit in terms of facilitating a degree of stability and control over the stimulation. His psychiatric symptoms reliably worsen within hours of the battery becoming depleted, thus providing confirmatory evidence for the efficacy of DBS for OCD in this person. WGS revealed that he is a heterozygote for the p.Val66Met variant in BDNF, encoding a member of the nerve growth factor family, and which has been found to predispose carriers to various psychiatric illnesses. He carries the p.Glu429Ala allele in methylenetetrahydrofolate reductase (MTHFR) and the p.Asp7Asn allele in ChAT, encoding choline O-acetyltransferase, with both alleles having been shown to confer an elevated susceptibility to psychoses. We have found thousands of other variants in his genome, including pharmacogenetic and copy number variants. This information has been archived and offered to this person alongside the clinical sequencing data, so that he and others can re-analyze his genome for years to come.
Conclusions. To our knowledge, this is the first study in the clinical neurosciences that integrates detailed neuropsychiatric phenotyping, deep brain stimulation for OCD and clinical-grade WGS with management of genetic results in the medical treatment of one person with severe mental illness. We offer this as an example of precision medicine in neuropsychiatry including brain-implantable devices and genomics-guided preventive health care.
doi:10.7717/peerj.177
PMCID: PMC3792182  PMID: 24109560
Genomics; Deep brain stimulation; Whole genome sequencing; Ethics; Neurosurgery; Obsessive compulsive disorder
6.  EGASP: Introduction 
Genome Biology  2006;7(Suppl 1):S1.
doi:10.1186/gb-2006-7-s1-s1
PMCID: PMC1810546  PMID: 16925831
7.  EGASP: the human ENCODE Genome Annotation Assessment Project 
Genome Biology  2006;7(Suppl 1):S2.
Background
We present the results of EGASP, a community experiment to assess the state-of-the-art in genome annotation within the ENCODE regions, which span 1% of the human genome sequence. The experiment had two major goals: the assessment of the accuracy of computational methods to predict protein coding genes; and the overall assessment of the completeness of the current human genome annotations as represented in the ENCODE regions. For the computational prediction assessment, eighteen groups contributed gene predictions. We evaluated these submissions against each other based on a 'reference set' of annotations generated as part of the GENCODE project. These annotations were not available to the prediction groups prior to the submission deadline, so that their predictions were blind and an external advisory committee could perform a fair assessment.
Results
The best methods had at least one gene transcript correctly predicted for close to 70% of the annotated genes. Nevertheless, the multiple transcript accuracy, taking into account alternative splicing, reached only approximately 40% to 50% accuracy. At the coding nucleotide level, the best programs reached an accuracy of 90% in both sensitivity and specificity. Programs relying on mRNA and protein sequences were the most accurate in reproducing the manually curated annotations. Experimental validation shows that only a very small percentage (3.2%) of the selected 221 computationally predicted exons outside of the existing annotation could be verified.
Conclusion
This is the first such experiment in human DNA, and we have followed the standards established in a similar experiment, GASP1, in Drosophila melanogaster. We believe the results presented here contribute to the value of ongoing large-scale annotation projects and should guide further experimental methods when being scaled up to the entire human genome sequence.
doi:10.1186/gb-2006-7-s1-s2
PMCID: PMC1810551  PMID: 16925836
8.  Global analysis of disease-related DNA sequence variation in 10 healthy individuals: Implications for whole genome-based clinical diagnostics 
Background
Understanding how sequence variants within healthy genomes are distributed with respect to ethnicity and disease-implicated genes is an essential first step toward establishing baselines for personalized genomic medicine.
Methods
In this study, we present an analysis of 10 genomes from healthy individuals of various ethnicities, produced using six different sequencing technologies. In total, these genomes contain more than 34 million single-nucleotide variants.
Results
We have analyzed these variants from a clinical perspective, assaying the influence of sequencing technology and ethnicity on prognosis. We have also examined the utility of OMIM and the disease-gene literature for determining the impact of rare, personal variants on an individual’s health.
Conclusions
Our analyses demonstrate that clinical prognoses are complicated by sequencing platform-specific errors and ethnicity. We show that disease-causing alleles are globally distributed along ethnic lines, with alleles known to be disease causing in Eurasians being significantly more likely to be homozygous in Africans.
doi:10.1097/GIM.0b013e31820ed321
PMCID: PMC3558030  PMID: 21325948
personal genomes; genome analysis; personalized genomics
9.  A standard variation file format for human genome sequences 
Genome Biology  2010;11(8):R88.
Here we describe the Genome Variation Format (GVF) and the 10Gen dataset. GVF, an extension of Generic Feature Format version 3 (GFF3), is a simple tab-delimited format for DNA variant files, which uses Sequence Ontology to describe genome variation data. The 10Gen dataset, ten human genomes in GVF format, is freely available for community analysis from the Sequence Ontology website and from an Amazon elastic block storage (EBS) snapshot for use in Amazon's EC2 cloud computing environment.
doi:10.1186/gb-2010-11-8-r88
PMCID: PMC2945790  PMID: 20796305
10.  Genome-Wide Analysis of Human Disease Alleles Reveals That Their Locations Are Correlated in Paralogous Proteins 
PLoS Computational Biology  2008;4(11):e1000218.
The millions of mutations and polymorphisms that occur in human populations are potential predictors of disease, of our reactions to drugs, of predisposition to microbial infections, and of age-related conditions such as impaired brain and cardiovascular functions. However, predicting the phenotypic consequences and eventual clinical significance of a sequence variant is not an easy task. Computational approaches have found perturbation of conserved amino acids to be a useful criterion for identifying variants likely to have phenotypic consequences. To our knowledge, however, no study to date has explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying polymorphisms with likely phenotypic consequences. In order to investigate the potential of this approach, we have assembled a unique collection of known disease-causing variants from OMIM and the Human Genome Mutation Database (HGMD) and used them to identify and characterize pairs of sequence variants that occur at homologous positions within paralogous human proteins. Our analyses demonstrate that the locations of variants are correlated in paralogous proteins. Moreover, if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. Thus, information about variant-pairs can be used to identify potentially disease-causing variants, extend existing procedures for polymorphism prioritization, and provide a suite of candidates for further diagnostic and therapeutic purposes.
Author Summary
There exists a superabundance of human sequence variations. Testing every sequence variant for association with human disease is often infeasible, as studies must be very large—and hence expensive—to overcome the statistical penalties used to control for multiple tests. A common alternative is to assay only a subset of sequence variants for which there are prior reasons to believe they may be disease-causing. Sequence variants that change conserved amino acids, for example, are often disease-causing. As an adjunct to this approach, we have explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying disease-causing DNA sequence variations. We find that DNA sequence variants co-occur at aligned amino acid pairs more frequently than expected by chance, suggesting that similar functional constraints on paralogous proteins result in coordinated distributions of variants along their lengths. Moreover, if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. These facts provide new avenues for the identification of disease-causing sequence variations.
doi:10.1371/journal.pcbi.1000218
PMCID: PMC2565504  PMID: 18989397
11.  VAAST 2.0: Improved Variant Classification and Disease-Gene Identification Using a Conservation-Controlled Amino Acid Substitution Matrix 
Genetic Epidemiology  2013;37(6):622-634.
The need for improved algorithmic support for variant prioritization and disease-gene identification in personal genomes data is widely acknowledged. We previously presented the Variant Annotation, Analysis, and Search Tool (VAAST), which employs an aggregative variant association test that combines both amino acid substitution (AAS) and allele frequencies. Here we describe and benchmark VAAST 2.0, which uses a novel conservation-controlled AAS matrix (CASM), to incorporate information about phylogenetic conservation. We show that the CASM approach improves VAAST’s variant prioritization accuracy compared to its previous implementation, and compared to SIFT, PolyPhen-2, and MutationTaster. We also show that VAAST 2.0 outperforms KBAC, WSS, SKAT, and variable threshold (VT) using published case-control datasets for Crohn disease (NOD2), hypertriglyceridemia (LPL), and breast cancer (CHEK2). VAAST 2.0 also improves search accuracy on simulated datasets across a wide range of allele frequencies, population-attributable disease risks, and allelic heterogeneity, factors that compromise the accuracies of other aggregative variant association tests. We also demonstrate that, although most aggregative variant association tests are designed for common genetic diseases, these tests can be easily adopted as rare Mendelian disease-gene finders with a simple ranking-by-statistical-significance protocol, and the performance compares very favorably to state-of-art filtering approaches. The latter, despite their popularity, have suboptimal performance especially with the increasing case sample size.
doi:10.1002/gepi.21743
PMCID: PMC3791556  PMID: 23836555
disease-gene finder; variant classifier; aggregative association test; rare Mendelian disease; complex disease
12.  An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge 
Brownstein, Catherine A | Beggs, Alan H | Homer, Nils | Merriman, Barry | Yu, Timothy W | Flannery, Katherine C | DeChene, Elizabeth T | Towne, Meghan C | Savage, Sarah K | Price, Emily N | Holm, Ingrid A | Luquette, Lovelace J | Lyon, Elaine | Majzoub, Joseph | Neupert, Peter | McCallie Jr, David | Szolovits, Peter | Willard, Huntington F | Mendelsohn, Nancy J | Temme, Renee | Finkel, Richard S | Yum, Sabrina W | Medne, Livija | Sunyaev, Shamil R | Adzhubey, Ivan | Cassa, Christopher A | de Bakker, Paul IW | Duzkale, Hatice | Dworzyński, Piotr | Fairbrother, William | Francioli, Laurent | Funke, Birgit H | Giovanni, Monica A | Handsaker, Robert E | Lage, Kasper | Lebo, Matthew S | Lek, Monkol | Leshchiner, Ignaty | MacArthur, Daniel G | McLaughlin, Heather M | Murray, Michael F | Pers, Tune H | Polak, Paz P | Raychaudhuri, Soumya | Rehm, Heidi L | Soemedi, Rachel | Stitziel, Nathan O | Vestecka, Sara | Supper, Jochen | Gugenmus, Claudia | Klocke, Bernward | Hahn, Alexander | Schubach, Max | Menzel, Mortiz | Biskup, Saskia | Freisinger, Peter | Deng, Mario | Braun, Martin | Perner, Sven | Smith, Richard JH | Andorf, Janeen L | Huang, Jian | Ryckman, Kelli | Sheffield, Val C | Stone, Edwin M | Bair, Thomas | Black-Ziegelbein, E Ann | Braun, Terry A | Darbro, Benjamin | DeLuca, Adam P | Kolbe, Diana L | Scheetz, Todd E | Shearer, Aiden E | Sompallae, Rama | Wang, Kai | Bassuk, Alexander G | Edens, Erik | Mathews, Katherine | Moore, Steven A | Shchelochkov, Oleg A | Trapane, Pamela | Bossler, Aaron | Campbell, Colleen A | Heusel, Jonathan W | Kwitek, Anne | Maga, Tara | Panzer, Karin | Wassink, Thomas | Van Daele, Douglas | Azaiez, Hela | Booth, Kevin | Meyer, Nic | Segal, Michael M | Williams, Marc S | Tromp, Gerard | White, Peter | Corsmeier, Donald | Fitzgerald-Butt, Sara | Herman, Gail | Lamb-Thrush, Devon | McBride, Kim L | Newsom, David | Pierson, Christopher R | Rakowsky, Alexander T | Maver, Aleš | Lovrečić, Luca | Palandačić, Anja | Peterlin, Borut | Torkamani, Ali | Wedell, Anna | Huss, Mikael | Alexeyenko, Andrey | Lindvall, Jessica M | Magnusson, Måns | Nilsson, Daniel | Stranneheim, Henrik | Taylan, Fulya | Gilissen, Christian | Hoischen, Alexander | van Bon, Bregje | Yntema, Helger | Nelen, Marcel | Zhang, Weidong | Sager, Jason | Zhang, Lu | Blair, Kathryn | Kural, Deniz | Cariaso, Michael | Lennon, Greg G | Javed, Asif | Agrawal, Saloni | Ng, Pauline C | Sandhu, Komal S | Krishna, Shuba | Veeramachaneni, Vamsi | Isakov, Ofer | Halperin, Eran | Friedman, Eitan | Shomron, Noam | Glusman, Gustavo | Roach, Jared C | Caballero, Juan | Cox, Hannah C | Mauldin, Denise | Ament, Seth A | Rowen, Lee | Richards, Daniel R | Lucas, F Anthony San | Gonzalez-Garay, Manuel L | Caskey, C Thomas | Bai, Yu | Huang, Ying | Fang, Fang | Zhang, Yan | Wang, Zhengyuan | Barrera, Jorge | Garcia-Lobo, Juan M | González-Lamuño, Domingo | Llorca, Javier | Rodriguez, Maria C | Varela, Ignacio | Reese, Martin G | De La Vega, Francisco M | Kiruluta, Edward | Cargill, Michele | Hart, Reece K | Sorenson, Jon M | Lyon, Gholson J | Stevenson, David A | Bray, Bruce E | Moore, Barry M | Eilbeck, Karen | Yandell, Mark | Zhao, Hongyu | Hou, Lin | Chen, Xiaowei | Yan, Xiting | Chen, Mengjie | Li, Cong | Yang, Can | Gunel, Murat | Li, Peining | Kong, Yong | Alexander, Austin C | Albertyn, Zayed I | Boycott, Kym M | Bulman, Dennis E | Gordon, Paul MK | Innes, A Micheil | Knoppers, Bartha M | Majewski, Jacek | Marshall, Christian R | Parboosingh, Jillian S | Sawyer, Sarah L | Samuels, Mark E | Schwartzentruber, Jeremy | Kohane, Isaac S | Margulies, David M
Genome Biology  2014;15(3):R53.
Background
There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance.
Results
A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization.
Conclusions
The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
doi:10.1186/gb-2014-15-3-r53
PMCID: PMC4073084  PMID: 24667040

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