ASD and schizophrenia show distinct developmental profiles in common genetic overlap with population-based social communication difficulties

Difficulties in social communication are part of the phenotypic overlap between autism spectrum disorders (ASD) and schizophrenia. Both conditions follow, however, distinct developmental patterns. Symptoms of ASD typically occur during early childhood, whereas most symptoms characteristic of schizophrenia do not appear before early adulthood. We investigated whether overlap in common genetic in fluences between these clinical conditions and impairments in social communication depends on the developmental stage of the assessed trait. Social communication difficulties were measured in typically-developing youth (Avon Longitudinal Study of Parents and Children,N⩽5553, longitudinal assessments at 8, 11, 14 and 17 years) using the Social Communication Disorder Checklist. Data on clinical ASD (PGC-ASD: 5305 cases, 5305 pseudo-controls; iPSYCH-ASD: 7783 cases, 11 359 controls) and schizophrenia (PGC-SCZ2: 34 241 cases, 45 604 controls, 1235 trios) were either obtained through the Psychiatric Genomics Consortium (PGC) or the Danish iPSYCH project. Overlap in genetic in fluences between ASD and social communication difficulties during development decreased with age, both in the PGC-ASD and the iPSYCH-ASD sample. Genetic overlap between schizophrenia and social communication difficulties, by contrast, persisted across age, as observed within two independent PGC-SCZ2 subsamples, and showed an increase in magnitude for traits assessed during later adolescence. ASD- and schizophrenia-related polygenic effects were unrelated to each other and changes in trait-disorder links reflect the heterogeneity of genetic factors in fluencing social communication difficulties during childhood versus later adolescence. Thus, both clinical ASD and schizophrenia share some genetic in fluences with impairments in social communication, but reveal distinct developmental profiles in their genetic links, consistent with the onset of clinical symptom

Calculation of Tajima’s D and other neutrality test statistics from low depth next-generation sequencing data

BACKGROUND: A number of different statistics are used for detecting natural selection using DNA sequencing data, including statistics that are summaries of the frequency spectrum, such as Tajima’s D. These statistics are now often being applied in the analysis of Next Generation Sequencing (NGS) data. However, estimates of frequency spectra from NGS data are strongly affected by low sequencing coverage; the inherent technology dependent variation in sequencing depth causes systematic differences in the value of the statistic among genomic regions. RESULTS: We have developed an approach that accommodates the uncertainty of the data when calculating site frequency based neutrality test statistics. A salient feature of this approach is that it implicitly solves the problems of varying sequencing depth, missing data and avoids the need to infer variable sites for the analysis and thereby avoids ascertainment problems introduced by a SNP discovery process. CONCLUSION: Using an empirical Bayes approach for fast computations, we show that this method produces results for low-coverage NGS data comparable to those achieved when the genotypes are known without uncertainty. We also validate the method in an analysis of data from the 1000 genomes project. The method is implemented in a fast framework which enables researchers to perform these neutrality tests on a genome-wide scale

A meta-analysis of genome-wide association studies of epigenetic age acceleration

Funding: Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping and DNA methylation profiling of the GS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” ((STRADL) Reference 104036/Z/14/Z)). Funding details for the cohorts included in the study by Lu et al. (2018) can be found in their publication. HCW is supported by a JMAS SIM fellowship from the Royal College of Physicians of Edinburgh and by an ESAT College Fellowship from the University of Edinburgh. AMM & HCW acknowledge the support of the Dr. Mortimer and Theresa Sackler Foundation. SH acknowledges support from grant 1U01AG060908-01. REM is supported by Alzheimer’s Research UK major project grant ARUK-PG2017B-10. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data Availability: Summary statistics from the research reported in the manuscript will be made available immediately following publication on the Edinburgh Data Share portal with a permanent digital object identifier (DOI). According to the terms of consent for Generation Scotland participants, requests for access to the individual-level data must be reviewed by the GS Access Committee ([email protected]). Individual-level data are not immediately available, due to confidentiality considerations and our legal obligation to protect personal information. These data will, however, be made available upon request and after review by the GS access committee, once ethical and data governance concerns regarding personal data have been addressed by the receiving institution through a Data Transfer Agreement.Peer reviewedPublisher PD

Eukaryotic Pangenomes

International audienceThe first eukaryotes emerged from their prokaryotic ancestors more than 1.5 billion years ago and rapidly spread over the planet, first in the ocean, later on as land animals, plants, and fungi. Taking advantage of an expanding genome complexity and flexibility, they invaded almost all known ecological niches, adapting their body plan, physiology, and metabolism to new environments. This increase in genome complexity came along with an increase in gene repertoire, mainly from molecular reassortment of existing protein domains, but sometimes from the capture of a piece of viral genome or of a transposon sequence. With increasing sequencing and computing powers, it has become possible to undertake deciphering eukaryotic genome contents to an unprecedented scale, collecting all genes belonging to a given species, aiming at compiling all essential and dispensable genes making eukaryotic life possible.In this chapter, eukaryotic core- and pangenomes concepts will be described, as well as notions of closed or open genomes. Among all eukaryotes presently sequenced, ascomycetous yeasts are arguably the most well-described clade and the pangenome of Saccharomyces cerevisiae, Candida glabrata, Candida albicans as well as Schizosaccharomyces species will be reviewed. For scientific and economical reasons, many plant genomes have been sequenced too and the gene content of soybean, cabbage, poplar, thale cress, rice, maize, and barley will be outlined. Planktonic life forms, such as Emiliana huxleyi, a chromalveolate or Micromonas pusilla, a green alga, will be detailed and their pangenomes pictured. Mechanisms generating genetic diversity, such as interspecific hybridization, whole-genome duplications, segmental duplications, horizontal gene transfer, and single-gene duplication will be depicted and exemplified. Finally, computing approaches used to calculate core- and pangenome contents will be briefly described, as well as possible future directions in eukaryotic comparative genomics