‘Everyone thought I was a very very bad person… no one want to know you like the nurses and doctors’:using focus groups to elicit the views of adults with learning disability who use challenging behaviour services

and Tables S1–S3. (PDF 3090 kb

An epigenetic clock for gestational age at birth based on blood methylation data

Background: Gestational age is often used as a proxy for developmental maturity by clinicians and researchers alike. DNA methylation has previously been shown to be associated with age and has been used to accurately estimate chronological age in children and adults. In the current study, we examine whether DNA methylation in cord blood can be used to estimate gestational age at birth. Results: We find that gestational age can be accurately estimated from DNA methylation of neonatal cord blood and blood spot samples. We calculate a DNA methylation gestational age using 148 CpG sites selected through elastic net regression in six training datasets. We evaluate predictive accuracy in nine testing datasets and find that the accuracy of the DNA methylation gestational age is consistent with that of gestational age estimates based on established methods, such as ultrasound. We also find that an increased DNA methylation gestational age relative to clinical gestational age is associated with birthweight independent of gestational age, sex, and ancestry. Conclusions: DNA methylation can be used to accurately estimate gestational age at or near birth and may provide additional information relevant to developmental stage. Further studies of this predictor are warranted to determine its utility in clinical settings and for research purposes. When clinical estimates are available this measure may increase accuracy in the testing of hypotheses related to developmental age and other early life circumstances.Peer reviewe

Additional file 1: of Elevated polygenic burden for autism is associated with differential DNA methylation at birth

Supplementary Figures S1–S23. (PDF 8163 kb

Additional file 4: of Elevated polygenic burden for autism is associated with differential DNA methylation at birth

iPSYCH-Broad ASD Group participants’ affiliations. (XLSX 14 kb

Comparison of sample types from variant calls—concordance rates.

<p>The concordance rates were calculated by pairwise comparison of variant calls before (upper panels) and after filtering (lower panels). The sample types compared were DBS_2x3.2 vs WB_ref in Pilot 1 <b>(A and B)</b>, DBS_2x3.2 vs WB_ref and WB_ref vs WB_ref replica in Pilot 2 <b>(C and D)</b> and DBS_2x1.6 vs WB_ref, DBS_2x3.2 vs WB_ref, DBS_2x3.2 vs DBS_2x1.6 and WB_ref vs WB_WGA_ref in Pilot 3 <b>(E and F)</b>. The rates have been presented per variant type: SNP, insertion, deletion and multiallelic calls, and comprise the averages of all comparisons made for a given sample pair, corresponding to the number of subjects in the pilot, i.e. Pilot 1 = 7, Pilot 2 = 8 and Pilot 3 = 7. Note that for comparisons using the DBS_2x1.6 sample type (see the fig), each individual replica was firstly compared to the WB_ref or DBS_2x3.2 sample types followed by the calculation of average values hereof, which were used in the figure.</p

Study design.

<p>This study aims to identify systematic differences between DBS and WB by testing both sample types from a number of individuals. Pilot 1, Pilot 2 and Pilot 3 included seven, eight and seven subjects represented with a neonatal DBS sample and corresponding adult WB reference sample each, respectively. A minimum of two sample types per subject were prepared for library preparation: wgaDNA of DNA from 2x3.2 mm of neonatal DBSs (DBS_2×3.2) and raw control DNA from the WB reference sample (WB_ref). Pilot 2 also included a replicate sample of each of the WB_ref samples (WB_ref_replicate)(replicate sample not shown in the cartoon). Pilot 3 included two additional sample types; WGA of DNA from 2x1.6 mm neonatal DBS (DBS_2x1.6) and WGA of the WB reference sample (WB_WGA_ref). Note that the DBS_2x1.6 sample type of Pilot 3 was prepared and sequenced in triplicate using different sets of 2x1.6 mm discs for all of the subjects included. The samples were set up for library preparation using different kits in the respective pilot studies, before sequencing and data analysis. Please note the color-coding used with green, blue and orange specifying the respective pilot studies. All samples were retrieved from the Danish Neonatal Screening Biobank.</p

Exome coverage by depth.

<p>The data were presented with a box plot as percentage of exome coverage with sequencing depths greater than 30. The exact medians of the observations have been listed above the plot. From left to right, the pilots were depicted in the order: Pilot 1 with DBS_2x3.2 and WB_ref sample types, Pilot 2 with DBS_2x3.2, WB_ref and WB_ref replica sample types and Pilot 3 with DBS_2x1.6, DBS_2x3.2, WB_WGA_ref and WB_ref sample types, respectively. In the plot, the medians are given by a solid black line enclosed in boxes specifying the first and third quartiles. The whiskers represent the statistical dispersion of the data using the interquartile range (1.5*IQR). Data beyond 1.5*IQR range are outliers and plotted as dots. Note that the number of observations per sample type (not considering DBS_2x1.6, see below) equals the number of subjects included in the pilot, i.e. Pilot 1 = 7, Pilot 2 = 8 and Pilot 3 = 7. The DBS_2x1.6 sample type was plotted using all 21 observations, resulting from the triplicate experiments per subject of different sets of 2x1.6 mm discs included in Pilot 3. The coverage statistics were calculated with GATK using R to obtain the percentage of exome coverage per sample type shown.</p