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Biology subjects

Dorschner, M.

Publications and source records attributed to Dorschner, M..

3 recordsLinked to original sources

Targeted analysis of dyslexia-associated regions on chromosomes 6, 12 and 15 in large multigenerational cohorts

Dyslexia is a common specific learning disability with a strong genetic basis that affects word reading and spelling. An increasing list of loci and genes have been implicated, but analyses to-date investigated only limited genomic variation within each locus with no confirmed pathogenic variants. In a collection of >2000 participants in families enrolled at three independent sites, we performed targeted capture and comprehensive sequencing of all exons and some regulatory elements of five candidate dyslexia risk genes (DNAAF4, CYP19A1, DCDC2, KIAA0319 and GRIN2B) for which prior evidence of association exists from more than one sample. For each of six dyslexia-related phenotypes we used both individual-single nucleotide polymorphism (SNP) and aggregate testing of multiple SNPs to evaluate evidence for association. We detected no promoter alterations and few potentially deleterious variants in the coding exons, none of which showed evidence of association with any phenotype. All genes except DNAAF4 provided evidence of association, corrected for the number of genes, for multiple non-coding variants with one or more phenotypes. Results for a variant in the downstream region of CYP19A1 and a haplotype in DCDC2 yielded particularly strong statistical significance for association. This haplotype and another in DCDC2 affected performance of real word reading in opposite directions. In KIAA0319, two missense variants annotated as tolerated/benign associated with poor performance on spelling. Ten non-coding SNPs likely affect transcription factor binding. Findings were similar regardless of whether phenotypes were adjusted for verbal IQ. Our findings from this large-scale sequencing study complement those from genome-wide association studies (GWAS), argue strongly against the causative involvement of large-effect coding variants in these five candidate genes, support an oligogenic etiology, and suggest a role of transcriptional regulation. Author SummaryFamily studies show that genes play a role in dyslexia and a small number of genomic regions have been implicated to date. However, it has proven difficult to identify the specific genetic variants in those regions that affect reading ability by using indirect measures of association with evenly spaced polymorphisms chosen without regard to likely function. Here, we use recent advances in DNA sequencing to examine more comprehensively the role of genetic variants in five previously nominated candidate dyslexia risk genes on several dyslexia-related traits. Our analysis of more than 2000 participants in families with dyslexia provides strong evidence for a contribution to dyslexia risk for the non-protein coding genetic variant rs9930506 in the CYP19A1 gene on chromosome 15 and excludes the DNAAF4 gene on the same chromosome. We identified other putative causal variants in genes DCDC2 and KIAA0319 on chromosome 6 and GRIN2B on chromosome 12. Further studies of these DNA variants, all of which were non-coding, may point to new biological pathways that affect susceptibility to dyslexia. These findings are important because they implicate regulatory variation in this complex trait that affects ability of individuals to effectively participate in our increasingly informatic world.

genetics↗

Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies

A comprehensive characterization of tumor genetic heterogeneity is critical for understanding how cancers evolve and escape treatment. Although many algorithms have been developed for capturing tumor heterogeneity, they are designed for analyzing either a single type of genomic aberration or individual biopsies. Here we present THEMIS (Tumor Heterogeneity Extensible Modeling via an Integrative System), which allows for the joint analysis of different types of genomic aberrations from multiple biopsies taken from the same patient, using a dynamic graphical model. Simulation experiments demonstrate higher accuracy of THEMIS over its ancestor, TITAN. The heterogeneity analysis results from THEMIS are validated with single cell DNA sequencing from a clinical tumor biopsy. When THEMIS is used to analyze tumor heterogeneity among multiple biopsies from the same patient, it helps to reveal the mutation accumulation history, track cancer progression, and identify the mutations related to treatment resistance. We implement our model via an extensible modeling platform, which makes our approach open, reproducible, and easy for others to extend.

bioinformatics↗

Cis-Compound Mutations are Prevalent in Triple Negative Breast Cancer and Can Drive Tumor Progression

About 16% of breast cancers fall into a clinically aggressive category designated triple negative (TNBC) due to a lack of ERBB2, estrogen receptor and progesterone receptor expression1-3. The mutational spectrum of TNBC has been characterized as part of The Cancer Genome Atlas (TCGA)4; however, snapshots of primary tumors cannot reveal the mechanisms by which TNBCs progress and spread. To address this limitation we initiated the Intensive Trial of OMics in Cancer (ITOMIC)-001, in which patients with metastatic TNBC undergo multiple biopsies over space and time5. Whole exome sequencing (WES) of 67 samples from 11 patients identified 426 genes containing multiple distinct single nucleotide variants (SNVs) within the same sample, instances we term Multiple SNVs affecting the Same Gene and Sample (MSSGS). We find that >90% of MSSGS result from cis-compound mutations (in which both SNVs affect the same allele), that MSSGS comprised of SNVs affecting adjacent nucleotides arise from single mutational events, and that most other MSSGS result from the sequential acquisition of SNVs. Some MSSGS drive cancer progression, as exemplified by a TNBC driven by FGFR2(S252W;Y375C). MSSGS are more prevalent in TNBC than other breast cancer subtypes and occur at higher-than-expected frequencies across TNBC samples within TCGA. MSSGS may denote genes that play as yet unrecognized roles in cancer progression.

clinical trials↗