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Dive into the research topics where Malgorzata Jaremko is active.

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Featured researches published by Malgorzata Jaremko.


Molecular Genetics & Genomic Medicine | 2014

The allelic spectrum of Charcot–Marie–Tooth disease in over 17,000 individuals with neuropathy

Christina DiVincenzo; Christopher Elzinga; Adam C. Medeiros; Izabela Karbassi; Jeremiah R. Jones; Matthew C. Evans; Corey Braastad; Crystal M. Bishop; Malgorzata Jaremko; Zhenyuan Wang; Khalida Liaquat; Carol Hoffman; Michelle York; Sat Dev Batish; James R. Lupski; Joseph Higgins

We report the frequency, positive rate, and type of mutations in 14 genes (PMP22, GJB1, MPZ, MFN2, SH3TC2, GDAP1, NEFL, LITAF, GARS, HSPB1, FIG4, EGR2, PRX, and RAB7A) associated with Charcot–Marie–Tooth disease (CMT) in a cohort of 17,880 individuals referred to a commercial genetic testing laboratory. Deidentified results from sequencing assays and multiplex ligation‐dependent probe amplification (MLPA) were analyzed including 100,102 Sanger sequencing, 2338 next‐generation sequencing (NGS), and 21,990 MLPA assays. Genetic abnormalities were identified in 18.5% (n = 3312) of all individuals. Testing by Sanger and MLPA (n = 3216) showed that duplications (dup) (56.7%) or deletions (del) (21.9%) in the PMP22 gene accounted for the majority of positive findings followed by mutations in the GJB1 (6.7%), MPZ (5.3%), and MFN2 (4.3%) genes. GJB1 del and mutations in the remaining genes explained 5.3% of the abnormalities. Pathogenic mutations were distributed as follows: missense (70.6%), nonsense (14.3%), frameshift (8.7%), splicing (3.3%), in‐frame deletions/insertions (1.8%), initiator methionine mutations (0.8%), and nonstop changes (0.5%). Mutation frequencies, positive rates, and the types of mutations were similar between tests performed by either Sanger (n = 17,377) or NGS (n = 503). Among patients with a positive genetic finding in a CMT‐related gene, 94.9% were positive in one of four genes (PMP22, GJB1, MPZ, or MFN2).


Pediatric Diabetes | 2016

Characteristics of maturity onset diabetes of the young in a large diabetes center

Christina Chambers; Alexandra Fouts; Fran Dong; Kevin Colclough; Zhenyuan Wang; Sat Dev Batish; Malgorzata Jaremko; Sian Ellard; Andrew T. Hattersley; Georgeanna J. Klingensmith; Andrea K. Steck

Maturity onset diabetes of the young (MODY) is a monogenic form of diabetes caused by a mutation in a single gene, often not requiring insulin. The aim of this study was to estimate the frequency and clinical characteristics of MODY at the Barbara Davis Center. A total of 97 subjects with diabetes onset before age 25, a random C‐peptide ≥0.1 ng/mL, and negative for all diabetes autoantibodies (GADA, IA‐2, ZnT8, and IAA) were enrolled, after excluding 21 subjects with secondary diabetes or refusal to participate. Genetic testing for MODY 1–5 was performed through Athena Diagnostics, and all variants of unknown significance were further analyzed at Exeter, UK. A total of 22 subjects [20 (21%) when excluding two siblings] were found to have a mutation in hepatocyte nuclear factor 4A (n = 4), glucokinase (n = 8), or hepatocyte nuclear factor 1A (n = 10). Of these 22 subjects, 13 had mutations known to be pathogenic and 9 (41%) had novel mutations, predicted to be pathogenic. Only 1 of the 22 subjects had been given the appropriate MODY diagnosis prior to testing. Compared with MODY‐negative subjects, the MODY‐positive subjects had lower hemoglobin A1c level and no diabetic ketoacidosis at onset; however, these characteristics are not specific for MODY. In summary, this study found a high frequency of MODY mutations with the majority of subjects clinically misdiagnosed. Clinicians should have a high index of suspicion for MODY in youth with antibody‐negative diabetes.


Human Mutation | 2016

A Standardized DNA Variant Scoring System for Pathogenicity Assessments in Mendelian Disorders.

Izabela Karbassi; Glenn A. Maston; Angela Love; Christina DiVincenzo; Corey Braastad; Christopher Elzinga; Alison Bright; Domenic Previte; Ke Zhang; Charles M. Rowland; Michele McCarthy; Jennifer Lapierre; Felicita Dubois; Katelyn A. Medeiros; Sat Dev Batish; Jeffrey G. Jones; Khalida Liaquat; Carol Hoffman; Malgorzata Jaremko; Zhenyuan Wang; Weimin Sun; Arlene M. Buller-Burckle; Charles M. Strom; Steven B. Keiles; Joseph Higgins

We developed a rules‐based scoring system to classify DNA variants into five categories including pathogenic, likely pathogenic, variant of uncertain significance (VUS), likely benign, and benign. Over 16,500 pathogenicity assessments on 11,894 variants from 338 genes were analyzed for pathogenicity based on prediction tools, population frequency, co‐occurrence, segregation, and functional studies collected from internal and external sources. Scores were calculated by trained scientists using a quantitative framework that assigned differential weighting to these five types of data. We performed descriptive and comparative statistics on the dataset and tested interobserver concordance among the trained scientists. Private variants defined as variants found within single families (n = 5,182), were either VUS (80.5%; n = 4,169) or likely pathogenic (19.5%; n = 1,013). The remaining variants (n = 6,712) were VUS (38.4%; n = 2,577) or likely benign/benign (34.7%; n = 2,327) or likely pathogenic/pathogenic (26.9%, n = 1,808). Exact agreement between the trained scientists on the final variant score was 98.5% [95% confidence interval (CI) (98.0, 98.9)] with an interobserver consistency of 97% [95% CI (91.5, 99.4)]. Variant scores were stable and showed increasing odds of being in agreement with new data when re‐evaluated periodically. This carefully curated, standardized variant pathogenicity scoring system provides reliable pathogenicity scores for DNA variants encountered in a clinical laboratory setting.


Neuromuscular Disorders | 2015

Molecular combing compared to Southern blot for measuring D4Z4 contractions in FSHD

Jessica Vasale; Fatih Z Boyar; Michael Jocson; Vladimira Sulcova; Patricia Chan; Khalida Liaquat; Carol Hoffman; Marc Meservey; Isabell Chang; David Tsao; Kerri Hensley; Yan Liu; Renius Owen; Corey Braastad; Weimin Sun; Pierre Walrafen; Jun Komatsu; Jia-Chi Wang; Aaron Bensimon; Arturo Anguiano; Malgorzata Jaremko; Zhenyuan Wang; Sat Dev Batish; Charles M. Strom; Joseph Higgins

We compare molecular combing to Southern blot in the analysis of the facioscapulohumeral muscular dystrophy type 1 locus (FSHD1) on chromosome 4q35-qter (chr 4q) in genomic DNA specimens sent to a clinical laboratory for FSHD testing. A de-identified set of 87 genomic DNA specimens determined by Southern blot as normal (n = 71), abnormal with D4Z4 macrosatellite repeat array contractions (n = 7), indeterminate (n = 6), borderline (n = 2), or mosaic (n = 1) was independently re-analyzed by molecular combing in a blinded fashion. The molecular combing results were identical to the Southern blot results in 75 (86%) of cases. All contractions (n = 7) and mosaics (n = 1) detected by Southern blot were confirmed by molecular combing. Of the 71 samples with normal Southern blot results, 67 (94%) had concordant molecular combing results. The four discrepancies were either mosaic (n = 2), rearranged (n = 1), or borderline by molecular combing (n = 1). All indeterminate Southern blot results (n = 6) were resolved by molecular combing as either normal (n = 4), borderline (n = 1), or rearranged (n = 1). The two borderline Southern blot results showed a D4Z4 contraction on the chr 4qA allele and a normal result by molecular combing. Molecular combing overcomes a number of technical limitations of Southern blot by providing direct visualization of D4Z4 macrosatellite repeat arrays on specific chr 4q and chr 10q alleles and more precise D4Z4 repeat sizing. This study suggests that molecular combing has superior analytical validity compared to Southern blot for determining D4Z4 contraction size, detecting mosaicism, and resolving borderline and indeterminate Southern blot results. Further studies are needed to establish the clinical validity and diagnostic accuracy of these findings in FSHD.


Neurology | 2016

Analysis of 141 Epilepsy Related Genes Using Next Generation Sequencing in 390 Patients (P5.154)

Zhenyuan Wang; Malgorzata Jaremko; Sat Dev Batish; Marc Meservey; Michelle McCarthy; Adam C. Medeiros; John Walsh; Susan Kimberly Allen; Norman Rochibaud; Christina DiVincenzo; Thomas Casiello; Martin Bazinet; Diem Doan; Michele McCarthy; Izabela Karbassi; Valerie Storozuk; Mariola Kacprzyk; Khalida Liaquat; Carol Hoffman; Meagan Krasner; Whitney Dodge; Matthew C. Evans; Christopher Elzinga; Michelle York; Corey Braastad; Joseph Higgins


Neurology | 2016

Analytical Performance of a Genome-Phenome Analyzer for Use in a Clinical Laboratory (P5.133)

Joseph Higgins; Zhenyuan Wang; Malgorzata Jaremko; Sat Dev Batish; Michelle York


Neurology | 2015

Variants in the COL6A1, COL6A2, and COL6A3 Genes in Collagen VI-related Congenital Muscular Dystrophy (P2.042)

Malgorzata Jaremko; Keith Morneau; Tiffany Smith; Zhenyuan Wang; Funda Suer; Carol Hoffman; Khalida Liaquat; Michele McCarthy; Jennifer Lapierre; Valerie Storozuk; Marc Meservey; Sat Dev Batish; Joseph Higgins


Neurology | 2015

The Frequency of Serum Neural Autoantibodies in Suspected Autoimmune Epilepsy (P6.284)

Brian Sansoucy; Malgorzata Jaremko; Kaylan Burnham; Keith Mourneau; Amy Goldberger; Vishal Patel; Zhenyuan Wang; Funda Suer; Sat Dev Batish; Joseph Higgins


Neurology | 2015

Frequencies of Mutations in 20 Genes Associated with Hereditary Ataxia: Experience from a US Clinical Laboratory (P2.135)

Zhenyuan Wang; Marc Meservey; Michele McCarthy; Sat Dev Batish; Malgorzata Jaremko; Funda Suer; Joseph Higgins


Neurology | 2015

Molecular Combing compared to Southern Blot in Measuring D4Z4 Contractions in FSHD (P2.027)

Jessica Vasale; Michael Jocson; Fatih Z Boyar; Khalida Liaquat; Carol Hoffman; Vladimira Sulcova; David Tsao; Kerri Hensley; Yan Liu; Patricia Chan; Renius Owen; Corey Braastad; Weimin Sun; Arturo Anguiano; Malgorzata Jaremko; Zhenyuan Wang; Funda Suer; Sat Dev Batish; Charles M. Strom; Joseph Higgins

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