Human genetics and clinical aspects of neurodevelopmental disorders

This chapter traverses contemporary understandings of the genetic architecture of human disease, and explores the clinical implications of the current state of knowledge. Many different classes of genetic mutations have been implicated as being involved in predisposition to certain diseases, and researchers are continually uncovering other means by which genetics plays an important role in human disease, such as with somatic genetic mosaicism. Putative “de novo” mutations can represent cases of parental mosaicism (including in the germline), which could be revealed by careful genotyping of parental tissues other than peripheral blood lymphocytes. There is an increasingly rich literature regarding rare mutations with seemingly large phenotypic effects. Privacy concerns have added to the difficulties of implementing genomics-guided medicine. With the advent of exome and whole genome sequencing (WGS), one needs to focus again on families over several generations, so as to attempt to minimize genetic differences, locus heterogeneity and environmental influences

Cross-language measurement equivalence of the Center for Epidemiologic Studies Depression (CES-D) scale in systemic sclerosis: A comparison of Canadian and Dutch patients

Contains fulltext : 118230.pdf (publisher's version ) (Open Access)OBJECTIVES: Increasingly, medical research involves patients who complete outcomes in different languages. This occurs in countries with more than one common language, such as Canada (French/English) or the United States (Spanish/English), as well as in international multi-centre collaborations, which are utilized frequently in rare diseases such as systemic sclerosis (SSc). In order to pool or compare outcomes, instruments should be measurement equivalent (invariant) across cultural or linguistic groups. This study provides an example of how to assess cross-language measurement equivalence by comparing the Center for Epidemiologic Studies Depression (CES-D) scale between English-speaking Canadian and Dutch SSc patients. METHODS: The CES-D was completed by 922 English-speaking Canadian and 213 Dutch SSc patients. Confirmatory factor analysis (CFA) was used to assess the factor structure in both samples. The Multiple-Indicator Multiple-Cause (MIMIC) model was utilized to assess the amount of differential item functioning (DIF). RESULTS: A two-factor model (positive and negative affect) showed excellent fit in both samples. Statistically significant, but small-magnitude, DIF was found for 3 of 20 items on the CES-D. The English-speaking Canadian sample endorsed more feeling-related symptoms, whereas the Dutch sample endorsed more somatic/retarded activity symptoms. The overall estimate in depression scores between English and Dutch was not influenced substantively by DIF. CONCLUSIONS: CES-D scores from English-speaking Canadian and Dutch SSc patients can be compared and pooled without concern that measurement differences may substantively influence results. The importance of assessing cross-language measurement equivalence in rheumatology studies prior to pooling outcomes obtained in different languages should be emphasized.8 p

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings