@article {920, title = {KMT2B-related disorders: expansion of the phenotypic spectrum and long-term efficacy of deep brain stimulation.}, journal = {Brain}, volume = {143}, year = {2020}, month = {2020 12 05}, pages = {3242-3261}, abstract = {

Heterozygous mutations in KMT2B are associated with an early-onset, progressive and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5-37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke Fahn Marsden{\textquoteright}s Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at 6 months, 1 year and last follow-up (motor, P = 0.001, P = 0.004, and P = 0.012; disability, P = 0.009, P = 0.002 and P = 0.012). At 1 year post-deep brain stimulation, >50\% of subjects showed BFMDRS-M and BFMDRS-D improvements of >30\%. In the long-term deep brain stimulation cohort (deep brain stimulation inserted for >5 years, n = 8), improvement of >30\% was maintained in 5/8 and 3/8 subjects for the BFMDRS-M and BFMDRS-D, respectively. The greatest BFMDRS-M improvements were observed for trunk (53.2\%) and cervical (50.5\%) dystonia, with less clinical impact on laryngeal dystonia. Improvements in gait dystonia decreased from 20.9\% at 1 year to 16.2\% at last assessment; no patient maintained a fully independent gait. Reduction of BFMDRS-D was maintained for swallowing (52.9\%). Five patients developed mild parkinsonism following deep brain stimulation. KMT2B-related disease comprises an expanding continuum from infancy to adulthood, with early evidence of genotype-phenotype correlations. Except for laryngeal dysphonia, deep brain stimulation provides a significant improvement in quality of life and function with sustained clinical benefit depending on symptoms distribution.

}, keywords = {Adolescent, Adult, Child, Child, Preschool, Chromosome Deletion, Cohort Studies, Computer Simulation, Deep Brain Stimulation, Disease Progression, Dystonic Disorders, Endocrine System Diseases, Female, Fetal Growth Retardation, Gait Disorders, Neurologic, Histone-Lysine N-Methyltransferase, Humans, Laryngeal Diseases, Male, Mutation, Mutation, Missense, Phenotype, Quality of Life, Treatment Outcome, Young Adult}, issn = {1460-2156}, doi = {10.1093/brain/awaa304}, author = {Cif, Laura and Demailly, Diane and Lin, Jean-Pierre and Barwick, Katy E and Sa, Mario and Abela, Lucia and Malhotra, Sony and Chong, Wui K and Steel, Dora and Sanchis-Juan, Alba and Ngoh, Adeline and Trump, Natalie and Meyer, Esther and Vasques, Xavier and Rankin, Julia and Allain, Meredith W and Applegate, Carolyn D and Attaripour IsfAHni, Sanaz and Baleine, Julien and Balint, Bettina and Bassetti, Jennifer A and Baple, Emma L and Bhatia, Kailash P and Blanchet, Catherine and Burglen, Lydie and Cambonie, Gilles and Seng, Emilie Chan and Bastaraud, Sandra Chantot and Cyprien, Fabienne and Coubes, Christine and d{\textquoteright}Hardemare, Vincent and Doja, Asif and Dorison, Nathalie and Doummar, Diane and Dy-Hollins, Marisela E and Farrelly, Ellyn and FitzPatrick, David R and Fearon, Conor and Fieg, Elizabeth L and Fogel, Brent L and Forman, Eva B and Fox, Rachel G and Gahl, William A and Galosi, Serena and Gonzalez, Victoria and Graves, Tracey D and Gregory, Allison and Hallett, Mark and Hasegawa, Harutomo and Hayflick, Susan J and Hamosh, Ada and Hully, Marie and Jansen, Sandra and Jeong, Suh Young and Krier, Joel B and Krystal, Sidney and Kumar, Kishore R and Laurencin, Chlo{\'e} and Lee, Hane and Lesca, Gaetan and Fran{\c c}ois, Laurence Lion and Lynch, Timothy and MAHnt, Neil and Martinez-Agosto, Julian A and Milesi, Christophe and Mills, Kelly A and Mondain, Michel and Morales-Briceno, Hugo and Ostergaard, John R and Pal, Swasti and Pallais, Juan C and Pavillard, Fr{\'e}d{\'e}rique and Perrigault, Pierre-Francois and Petersen, Andrea K and Polo, Gustavo and Poulen, Gaetan and Rinne, Tuula and Roujeau, Thomas and Rogers, Caleb and Roubertie, Agathe and SAHgian, Michelle and Schaefer, Elise and Selim, Laila and Selway, Richard and Sharma, Nutan and Signer, Rebecca and Soldatos, Ariane G and Stevenson, David A and Stewart, Fiona and Tchan, Michel and Verma, Ishwar C and de Vries, Bert B A and Wilson, Jenny L and Wong, Derek A and Zaitoun, Raghda and Zhen, Dolly and Znaczko, Anna and Dale, Russell C and de Gusm{\~a}o, Claudio M and Friedman, Jennifer and Fung, Victor S C and King, Mary D and Mohammad, Shekeeb S and Rohena, Luis and Waugh, Jeff L and Toro, Camilo and Raymond, F Lucy and Topf, Maya and Coubes, Philippe and Gorman, Kathleen M and Kurian, Manju A} } @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 {404, title = {Gene-based segregation method for identifying rare variants in family-based sequencing studies.}, journal = {Genet Epidemiol}, volume = {41}, year = {2017}, month = {2017 05}, pages = {309-319}, abstract = {

Whole-exome sequencing using family data has identified rare coding variants in Mendelian diseases or complex diseases with Mendelian subtypes, using filters based on variant novelty, functionality, and segregation with the phenotype within families. However, formal statistical approaches are limited. We propose a gene-based segregation test (GESE) that quantifies the uncertainty of the filtering approach. It is constructed using the probability of segregation events under the null hypothesis of Mendelian transmission. This test takes into account different degrees of relatedness in families, the number of functional rare variants in the gene, and their minor allele frequencies in the corresponding population. In addition, a weighted version of this test allows incorporating additional subject phenotypes to improve statistical power. We show via simulations that the GESE and weighted GESE tests maintain appropriate type I error rate, and have greater power than several commonly used region-based methods. We apply our method to whole-exome sequencing data from 49 extended pedigrees with severe, early-onset chronic obstructive pulmonary disease (COPD) in the Boston Early-Onset COPD study (BEOCOPD) and identify several promising candidate genes. Our proposed methods show great potential for identifying rare coding variants of large effect and high penetrance for family-based sequencing data. The proposed tests are implemented in an R package that is available on CRAN (https://cran.r-project.org/web/packages/GESE/).

}, keywords = {Age of Onset, Boston, Computer Simulation, Databases, Genetic, Family, Genetic Variation, Genome, Human, Humans, Models, Genetic, Penetrance, Pulmonary Disease, Chronic Obstructive, Reference Standards, Sequence Analysis, DNA}, issn = {1098-2272}, doi = {10.1002/gepi.22037}, author = {Qiao, Dandi and Lange, Christoph and Laird, Nan M and Won, Sungho and Hersh, Craig P and Morrow, Jarrett and Hobbs, Brian D and Lutz, Sharon M and Ruczinski, Ingo and Beaty, Terri H and Silverman, Edwin K and Cho, Michael H} } @article {47, title = {Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.}, journal = {Genet Epidemiol}, volume = {40}, year = {2016}, month = {2016 Sep}, pages = {502-11}, abstract = {

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows.

}, keywords = {Computer Simulation, Genetic Association Studies, Genetic Variation, Humans, Likelihood Functions, Models, Genetic, Phenotype}, issn = {1098-2272}, doi = {10.1002/gepi.21985}, author = {Wang, Longfei and Lee, Sungyoung and Gim, Jungsoo and Qiao, Dandi and Cho, Michael and Elston, Robert C and Silverman, Edwin K and Won, Sungho} } @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 {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 {279, title = {Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data.}, journal = {Am J Hum Genet}, volume = {94}, year = {2014}, month = {2014 Jan 02}, pages = {33-46}, abstract = {

Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits.

}, keywords = {Alleles, Autistic Disorder, Computer Simulation, Exome, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Haplotypes, Humans, Linkage Disequilibrium, Models, Genetic, Phenotype, Sequence Analysis, DNA}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2013.11.021}, author = {He, Zongxiao and O{\textquoteright}Roak, Brian J and Smith, Joshua D and Wang, Gao and Hooker, Stanley and Santos-Cortez, Regie Lyn P and Li, Biao and Kan, Mengyuan and Krumm, Nik and Nickerson, Deborah A and Shendure, Jay and Eichler, Evan E 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} }