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Dive into the research topics where Yoon-ha Lee is active.

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Featured researches published by Yoon-ha Lee.


Nature Genetics | 2009

Microduplications of 16p11.2 are associated with schizophrenia.

Shane McCarthy; Vladimir Makarov; George Kirov; Anjene Addington; Jon McClellan; Seungtai Yoon; Diana O. Perkins; Diane E. Dickel; Mary Kusenda; Olga Krastoshevsky; Verena Krause; Ravinesh A. Kumar; Detelina Grozeva; Dheeraj Malhotra; Tom Walsh; Elaine H. Zackai; Jaya Ganesh; Ian D. Krantz; Nancy B. Spinner; Patricia Roccanova; Abhishek Bhandari; Kevin Pavon; B. Lakshmi; Anthony Leotta; Jude Kendall; Yoon-ha Lee; Vladimir Vacic; Sydney Gary; Lilia M. Iakoucheva; Timothy J. Crow

Recurrent microdeletions and microduplications of a 600-kb genomic region of chromosome 16p11.2 have been implicated in childhood-onset developmental disorders. We report the association of 16p11.2 microduplications with schizophrenia in two large cohorts. The microduplication was detected in 12/1,906 (0.63%) cases and 1/3,971 (0.03%) controls (P = 1.2 × 10−5, OR = 25.8) from the initial cohort, and in 9/2,645 (0.34%) cases and 1/2,420 (0.04%) controls (P = 0.022, OR = 8.3) of the replication cohort. The 16p11.2 microduplication was associated with a 14.5-fold increased risk of schizophrenia (95% CI (3.3, 62)) in the combined sample. A meta-analysis of datasets for multiple psychiatric disorders showed a significant association of the microduplication with schizophrenia (P = 4.8 × 10−7), bipolar disorder (P = 0.017) and autism (P = 1.9 × 10−7). In contrast, the reciprocal microdeletion was associated only with autism and developmental disorders (P = 2.3 × 10−13). Head circumference was larger in patients with the microdeletion than in patients with the microduplication (P = 0.0007).


Nature Methods | 2014

accurate de novo and transmitted indel detection in exome-capture data using microassembly

Giuseppe Narzisi; Jason O'Rawe; Ivan Iossifov; Han Fang; Yoon-ha Lee; Zihua Wang; Yiyang Wu; Gholson J. Lyon; Michael Wigler; Michael C. Schatz

We present an open-source algorithm, Scalpel (http://scalpel.sourceforge.net/), which combines mapping and assembly for sensitive and specific discovery of insertions and deletions (indels) in exome-capture data. A detailed repeat analysis coupled with a self-tuning k-mer strategy allows Scalpel to outperform other state-of-the-art approaches for indel discovery, particularly in regions containing near-perfect repeats. We analyzed 593 families from the Simons Simplex Collection and demonstrated Scalpels power to detect long (≥30 bp) transmitted events and enrichment for de novo likely gene-disrupting indels in autistic children.


American Journal of Human Genetics | 2012

Rare De Novo Germline Copy-Number Variation in Testicular Cancer

Zsofia K. Stadler; Diane Esposito; Sohela Shah; Joseph Vijai; Boris Yamrom; Dan Levy; Yoon-ha Lee; Jude Kendall; Anthony Leotta; Michael Ronemus; Nichole Hansen; Kara Sarrel; Rohini Rau-Murthy; Kasmintan Schrader; Noah D. Kauff; Robert Klein; Steven M. Lipkin; Rajmohan Murali; Mark E. Robson; Joel Sheinfeld; Darren R. Feldman; George J. Bosl; Larry Norton; Michael Wigler; Kenneth Offit

Although heritable factors are an important determinant of risk of early-onset cancer, the majority of these malignancies appear to occur sporadically without identifiable risk factors. Germline de novo copy-number variations (CNVs) have been observed in sporadic neurocognitive and cardiovascular disorders. We explored this mechanism in 382 genomes of 116 early-onset cancer case-parent trios and unaffected siblings. Unique de novo germline CNVs were not observed in 107 breast or colon cancer trios or controls but were indeed found in 7% of 43 testicular germ cell tumor trios; this percentage exceeds background CNV rates and suggests a rare de novo genetic paradigm for susceptibility to some human malignancies.


Nature Protocols | 2016

Indel variant analysis of short-read sequencing data with Scalpel

Han Fang; Ewa A. Bergmann; Kanika Arora; Vladimir Vacic; Michael C. Zody; Ivan Iossifov; Jason O'Rawe; Yiyang Wu; Laura Jimenez Barron; Julie Rosenbaum; Michael Ronemus; Yoon-ha Lee; Zihua Wang; Esra Dikoglu; Vaidehi Jobanputra; Gholson J. Lyon; Michael Wigler; Michael C. Schatz; Giuseppe Narzisi

As the second most common type of variation in the human genome, insertions and deletions (indels) have been linked to many diseases, but the discovery of indels of more than a few bases in size from short-read sequencing data remains challenging. Scalpel (http://scalpel.sourceforge.net) is an open-source software for reliable indel detection based on the microassembly technique. It has been successfully used to discover mutations in novel candidate genes for autism, and it is extensively used in other large-scale studies of human diseases. This protocol gives an overview of the algorithm and describes how to use Scalpel to perform highly accurate indel calling from whole-genome and whole-exome sequencing data. We provide detailed instructions for an exemplary family-based de novo study, but we also characterize the other two supported modes of operation: single-sample and somatic analysis. Indel normalization, visualization and annotation of the mutations are also illustrated. Using a standard server, indel discovery and characterization in the exonic regions of the example sequencing data can be completed in ∼5 h after read mapping.


Proceedings of the National Academy of Sciences of the United States of America | 2012

Reducing system noise in copy number data using principal components of self-self hybridizations

Yoon-ha Lee; Michael Ronemus; Jude Kendall; B. Lakshmi; Anthony Leotta; Dan Levy; Diane Esposito; Vladimir Grubor; Kenny Ye; Michael Wigler; Boris Yamrom

Genomic copy number variation underlies genetic disorders such as autism, schizophrenia, and congenital heart disease. Copy number variations are commonly detected by array based comparative genomic hybridization of sample to reference DNAs, but probe and operational variables combine to create correlated system noise that degrades detection of genetic events. To correct for this we have explored hybridizations in which no genetic signal is expected, namely “self-self” hybridizations (SSH) comparing DNAs from the same genome. We show that SSH trap a variety of correlated system noise present also in sample-reference (test) data. Through singular value decomposition of SSH, we are able to determine the principal components (PCs) of this noise. The PCs themselves offer deep insights into the sources of noise, and facilitate detection of artifacts. We present evidence that linear and piecewise linear correction of test data with the PCs does not introduce detectable spurious signal, yet improves signal-to-noise metrics, reduces false positives, and facilitates copy number determination.


bioRxiv | 2017

De novo indels within introns contribute to ASD incidence

Adriana Munoz; Boris Yamrom; Yoon-ha Lee; Peter W. Andrews; Steven Marks; Kuan-Ting Lin; Zihua Wang; Adrian R. Krainer; Robert B. Darnell; Michael Wigler; Ivan Iossifov

Copy number profiling and whole-exome sequencing has allowed us to make remarkable progress in our understanding of the genetics of autism over the past ten years, but there are major aspects of the genetics that are unresolved. Through whole-genome sequencing, additional types of genetic variants can be observed. These variants are abundant and to know which are functional is challenging. We have analyzed whole-genome sequencing data from 510 of the Simons Simplex Collections quad families and focused our attention on intronic variants. Within the introns of 546 high-quality autism target genes, we identified 63 de novo indels in the affected and only 37 in the unaffected siblings. The difference of 26 events is significantly larger than expected (p-val = 0.01) and using reasonable extrapolation shows that de novo intronic indels can contribute to at least 10% of simplex autism. The significance increases if we restrict to the half of the autism targets that are intolerant to damaging variants in the normal human population, which half we expect to be even more enriched for autism genes. For these 273 targets we observe 43 and 20 events in affected and unaffected siblings, respectively (p-value of 0.005). There was no significant signal in the number of de novo intronic indels in any of the control sets of genes analyzed. We see no signal from de novo substitutions in the introns of target genes.


Pediatric Rheumatology | 2015

Defective removal of ribonucleotides from DNA promotes systemic lupus erythematosus

Claudia Günther; Barbara Kind; Martin A. M. Reijns; Nicole Berndt; Manuel Martínez-Bueno; Christine Wolf; Victoria Tüngler; Osvaldo Chara; Yoon-ha Lee; Norbert Hubner; Louise S. Bicknell; Sophia Blum; Claudia Krug; Franziska Schmidt; Stefanie Kretschmer; Sarah Koss; Katy R. Astell; Georgia Ramantani; Anja Bauerfeind; David L. Morris; Deborah S. Cunninghame Graham; Doryen Bubeck; Andrea Leitch; Stuart H. Ralston; Elizabeth A. Blackburn; Manfred Gahr; Torsten Witte; Timothy J. Vyse; Inga Melchers; Elisabeth Mangold


arXiv: Quantitative Methods | 2011

Removing System Noise from Comparative Genomic Hybridization Data by Self-Self Analysis

Yoon-ha Lee; Michael Ronemus; Jude Kendall; B. Lakshmi; Anthony Leotta; Daniel A. Levy; Diane Esposito; Vladimir Grubor; Kenny Ye; Michael Wigler; Boris Yamrom


Archive | 2011

RareDeNovoandTransmittedCopy-NumberVariation in Autistic Spectrum Disorders

Dan Levy; Michael Ronemus; Boris Yamrom; Yoon-ha Lee; Anthony Leotta; Jude Kendall; Steven Marks; Andreas Buja; Ivan Iossifov; Michael Wigler


Blood | 2008

High-Resolution Array-Based Comparative Genome Hybridization (CGH) Identifies Novel and Recurrent Regions in CLL

Vladimir Grubor; Alexander Krasnitz; Jennifer Troge; Jennifer L. Meth; B. Lakshmi; Jude Kendall; Boris Yamrom; Garrick Alex; Deepa Pai; Nicholas Navin; Lisa A. Hufnagel; Yoon-ha Lee; Kerry Cook; Steven L. Allen; Kanti R. Rai; Rajendra N. Damle; Carlo Calissano; Nicholas Chiorazzi; Michael Wigler; Diane Esposito

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Michael Wigler

Cold Spring Harbor Laboratory

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Jude Kendall

Cold Spring Harbor Laboratory

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Boris Yamrom

Cold Spring Harbor Laboratory

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Anthony Leotta

Cold Spring Harbor Laboratory

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Diane Esposito

Cold Spring Harbor Laboratory

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Michael Ronemus

Cold Spring Harbor Laboratory

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Ivan Iossifov

Cold Spring Harbor Laboratory

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B. Lakshmi

The Feinstein Institute for Medical Research

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Dan Levy

Cold Spring Harbor Laboratory

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