Author Correction: A population-specific reference panel empowers genetic studies of Anabaptist populations.

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Estimating sleep parameters using an accelerometer without sleep diary

Funding Information: This work was made possible thanks to the following grants: NIH grants HL-094307 (AIP), and; MRC grant MR/ P012167/1. We would like to thank Dr. Sarah Charman, Dr. Paul Innerd, Matthew Goodman and Sara McHugh-Grant for their contributions to the collection of PSG data. Publisher Copyright: © 2018, The Author(s).Peer reviewe

Homozygous microdeletion of exon 5 in ZNF277 in a girl with specific language impairment

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Genomic Landscape of a Three-Generation Pedigree Segregating Affective Disorder

Bipolar disorder (BPD) is a common psychiatric illness with a complex mode of inheritance. Besides traditional linkage and association studies, which require large sample sizes, analysis of common and rare chromosomal copy number variants (CNVs) in extended families may provide novel insights into the genetic susceptibility of complex disorders. Using the Illumina HumanHap550 BeadChip with over 550,000 SNP markers, we genotyped 46 individuals in a three-generation Old Order Amish pedigree with 19 affected (16 BPD and three major depression) and 27 unaffected subjects. Using the PennCNV algorithm, we identified 50 CNV regions that ranged in size from 12 to 885 kb and encompassed at least 10 single nucleotide polymorphisms (SNPs). Of 19 well characterized CNV regions that were available for combined genotype-expression analysis 11 (58%) were associated with expression changes of genes within, partially within or near these CNV regions in fibroblasts or lymphoblastoid cell lines at a nominal P value <0.05. To further investigate the mode of inheritance of CNVs in the large pedigree, we analyzed a set of four CNVs, located at 6q27, 9q21.11, 12p13.31 and 15q11, all of which were enriched in subjects with affective disorders. We additionally show that these variants affect the expression of neuronal genes within or near the rearrangement. Our analysis suggests that family based studies of the combined effect of common and rare CNVs at many loci may represent a useful approach in the genetic analysis of disease susceptibility of mental disorders

Parental and Grandparental Ages in the Autistic Spectrum Disorders: A Birth Cohort Study

Background: A number of studies have assessed ages of parents of children with autistic spectrum disorders (ASD), and reported both maternal and paternal age effects. Here we assess relationships with grandparental ages. Methods and Findings: We compared the parental and grandparental ages of children in the population-based Avon Longitudinal Study of Parents and Children (ALSPAC), according to their scores in regard to 4 autistic trait measures and whether they had been given a diagnosis of ASD. Mean maternal and paternal ages of ASD cases were raised, but this appears to be secondary to a maternal grandmother age effect (P = 0.006): OR = 1.66[95%CI 1.16, 2.37] for each 10-year increase in the grandmother’s age at the birth of the mother. Trait measures also revealed an association between the maternal grandmother’s age and the major autistic trait–the Coherence Scale (regression coefficient b = 0.142, [95%CI = 0.057, 0.228]P = 0.001). After allowing for confounders the effect size increased to b = 0.217[95%CI 0.125, 0.308](P,0.001) for each 10 year increase in age. Conclusions: Although the relationship between maternal grandmother’s age and ASD and a major autistic trait was unexpected, there is some biological plausibility, for the maternal side at least, given that the timing of female meiosis I permits direct effects on the grandchild’s genome during the grandmother’s pregnancy. An alternative explanation is the meiotic mismatch methylation (3 M) hypothesis, presented here for the first time. Nevertheless the findings should b

Identifying Prototypical Components in Behaviour Using Clustering Algorithms

Quantitative analysis of animal behaviour is a requirement to understand the task solving strategies of animals and the underlying control mechanisms. The identification of repeatedly occurring behavioural components is thereby a key element of a structured quantitative description. However, the complexity of most behaviours makes the identification of such behavioural components a challenging problem. We propose an automatic and objective approach for determining and evaluating prototypical behavioural components. Behavioural prototypes are identified using clustering algorithms and finally evaluated with respect to their ability to represent the whole behavioural data set. The prototypes allow for a meaningful segmentation of behavioural sequences. We applied our clustering approach to identify prototypical movements of the head of blowflies during cruising flight. The results confirm the previously established saccadic gaze strategy by the set of prototypes being divided into either predominantly translational or rotational movements, respectively. The prototypes reveal additional details about the saccadic and intersaccadic flight sections that could not be unravelled so far. Successful application of the proposed approach to behavioural data shows its ability to automatically identify prototypical behavioural components within a large and noisy database and to evaluate these with respect to their quality and stability. Hence, this approach might be applied to a broad range of behavioural and neural data obtained from different animals and in different contexts

Population-based study of genetic variation in individuals with autism spectrum disorders from Croatia

<p>Abstract</p> <p>Background</p> <p>Genome-wide studies on autism spectrum disorders (ASDs) have mostly focused on large-scale population samples, but examination of rare variations in isolated populations may provide additional insights into the disease pathogenesis.</p> <p>Methods</p> <p>As a first step in the genetic analysis of ASD in Croatia, we characterized genetic variation in a sample of 103 subjects with ASD and 203 control individuals, who were genotyped using the Illumina HumanHap550 BeadChip. We analyzed the genetic diversity of the Croatian population and its relationship to other populations, the degree of relatedness via Runs of Homozygosity (ROHs), and the distribution of large (>500 Kb) copy number variations.</p> <p>Results</p> <p>Combining the Croatian cohort with several previously published populations in the FastME analysis (an alternative to Neighbor Joining) revealed that Croatian subjects cluster, as expected, with Southern Europeans; in addition, individuals from the same geographic region within Europe cluster together. Whereas Croatian subjects could be separated from a sample of healthy control subjects of European origin from North America, Croatian ASD cases and controls are well mixed. A comparison of runs of homozygosity indicated that the number and the median length of regions of homozygosity are higher for ASD subjects than for controls (p = 6 × 10^-3). Furthermore, analysis of copy number variants found a higher frequency of large chromosomal rearrangements (>2 Mb) in ASD cases (5/103) than in ethnically matched control subjects (1/197, p = 0.019).</p> <p>Conclusions</p> <p>Our findings illustrate the remarkable utility of high-density genotype data for subjects from a limited geographic area in dissecting genetic heterogeneity with respect to population and disease related variation.</p

The Genetic Effect of Copy Number Variations on the Risk of Type 2 Diabetes in a Korean Population

BACKGROUND: Unlike Caucasian populations, genetic factors contributing to the risk of type 2 diabetes mellitus (T2DM) are not well studied in Asian populations. In light of this, and the fact that copy number variation (CNV) is emerging as a new way to understand human genomic variation, the objective of this study was to identify type 2 diabetes-associated CNV in a Korean cohort. METHODOLOGY/PRINCIPAL FINDINGS: Using the Illumina HumanHap300 BeadChip (317,503 markers), genome-wide genotyping was performed to obtain signal and allelic intensities from 275 patients with type 2 diabetes mellitus (T2DM) and 496 nondiabetic subjects (Total n = 771). To increase the sensitivity of CNV identification, we incorporated multiple factors using PennCNV, a program that is based on the hidden Markov model (HMM). To assess the genetic effect of CNV on T2DM, a multivariate logistic regression model controlling for age and gender was used. We identified a total of 7,478 CNVs (average of 9.7 CNVs per individual) and 2,554 CNV regions (CNVRs; 164 common CNVRs for frequency>1%) in this study. Although we failed to demonstrate robust associations between CNVs and the risk of T2DM, our results revealed a putative association between several CNVRs including chr15:45994758-45999227 (P = 8.6E-04, P(corr) = 0.01) and the risk of T2DM. The identified CNVs in this study were validated using overlapping analysis with the Database of Genomic Variants (DGV; 71.7% overlap), and quantitative PCR (qPCR). The identified variations, which encompassed functional genes, were significantly enriched in the cellular part, in the membrane-bound organelle, in the development process, in cell communication, in signal transduction, and in biological regulation. CONCLUSION/SIGNIFICANCE: We expect that the methods and findings in this study will contribute in particular to genome studies of Asian populations

Transcriptomic signatures across human tissues identify functional rare genetic variation

© 2020 American Association for the Advancement of Science. All rights reserved. INTRODUCTION: The human genome contains tens of thousands of rare (minor allele frequency 800 genomes matched with transcriptomes across 49 tissues, we were able to study RVs that underlie extreme changes in the transcriptome. To capture the diversity of these extreme changes, we developed and integrated approaches to identify expression, allele-specific expression, and alternative splicing outliers, and characterized the RV landscape underlying each outlier signal. We demonstrate that personal genome interpretation and RV discovery is enhanced by using these signals. This approach provides a new means to integrate a richer set of functional RVs into models of genetic burden, improve disease gene identification, and enable the delivery of precision genomics