Additional file 1 of Machine learning derived risk prediction of anorexia nervosa

Table S1. Performance of logistic regression model in 10 random shuffles. Table S2. AUCs for fraction of the training dataset (from 10 % to 90 % of the original), after rerunning 10 times. Table S3. AUCs of different size for training dataset (from 10 % more to 90 % more than the original, randomly selected from fold3), after rerunning 10 times. Member lists of the Genetic Consortium for Anorexia Nervosa (GCAN)/the Wellcome Trust Case Control Consortium 3 (WTCCC 3)/the Price Foundation Collaborative Group. Supplementary acknowledgements. Ethic committee information. (DOCX 45 kb

Additional file 2: Figure S1. of Machine learning derived risk prediction of anorexia nervosa

ROC curve for linear regression model in shuffle 1. (PDF 17 kb

Polygenic risk score of non‐melanoma skin cancer predicts post‐transplant skin cancer across multiple organ types

Polygenic risk scores (PRS) calculated from genome‐wide association studies (GWAS) of non-melanoma skin cancer (NMSC) in a general, non-transplant setting, have recently been shown to predict risk of, and time to post-renal transplant skin cancer. In this study, we set out to test these findings in a cohort of heart, lung and liver transplant patients to see if these scores could be applied across different organ transplant types. Using the PRS from Stapleton et al. (2018), PRS were calculated for each sample across a European ancestry heart, lung and liver transplant cohorts (n = 523) and tested as predictors time to NMSC post-transplant. The top PRS, squamous cell carcinoma (SCC) pT1x10-5, (n SNPs= 1953), SCC pT1x10-6 and SCC pT1x10-6 (n SNPs = 1061) was significantly predictive in the time to NMSC, SCC and basal cell carcinoma (BCC) analysis across organ (p = 0.006, 0.02 and 0.02 respectively). We observed here a similar direction of effect and effect size [NMSC HR = 1.31(1.08-1.59)] to that in the original, discovery study, with increased polygenic burden leading to a faster time to developing NMSC. In summary, we found that PRS of NMSC calculated from GWAS of NMSC in non-transplant populations independently replicated in this cohort of heart, lung and liver transplant.</p

Advantage of Whole Exome Sequencing over Allele-specific and Targeted Segment Sequencing, in Detection of Novel <i>TULP1</i> Mutation in Leber Congenital Amaurosis

<div><p></p><p><i>Background</i>: Leber congenital amaurosis (LCA) is a severe form of retinal dystrophy with marked underlying genetic heterogeneity. Until recently, allele-specific assays and Sanger sequencing of targeted segments were the only available approaches for attempted genetic diagnosis in this condition. A broader next-generation sequencing (NGS) strategy, such as whole exome sequencing, provides an improved molecular genetic diagnostic capacity for patients with these conditions.</p><p><i>Materials and Methods</i>: In a child with LCA, an allele-specific assay analyzing 135 known LCA-causing variations, followed by targeted segment sequencing of 61 regions in 14 causative genes was performed. Subsequently, exome sequencing was undertaken in the proband, unaffected consanguineous parents and two unaffected siblings. Bioinformatic analysis used two independent pipelines, BWA-GATK and SOAP, followed by Annovar and SnpEff to annotate the variants.</p><p><i>Results</i>: No disease-causing variants were found using the allele-specific or targeted segment Sanger sequencing assays. Analysis of variants in the exome sequence data revealed a novel homozygous nonsense mutation (c.1081C > T, p.Arg361*) in <i>TULP1</i>, a gene with roles in photoreceptor function where mutations were previously shown to cause LCA and retinitis pigmentosa. The identified homozygous variant was the top candidate using both bioinformatic pipelines.</p><p><i>Conclusions</i>: This study highlights the value of the broad sequencing strategy of exome sequencing for disease gene identification in LCA, over other existing methods. NGS is particularly beneficial in LCA where there are a large number of causative disease genes, few distinguishing clinical features for precise candidate disease gene selection, and few mutation hotspots in any of the known disease genes.</p></div

Additional file 1: of Assessing known chronic kidney disease associated genetic variants in Saudi Arabian populations

Table S1. One-way ANOVA comparing the various Biochemical parameters in patients stratified for ESRD, T2D and HTN. Table S2. Correlation of biochemical parameters stratified by CKD, Type 2 Diabetes and Hypertension status. (DOCX 21 kb

Polygenic risk score as a determinant of risk of non-melanoma skin cancer in a European-descent renal transplant cohort

Renal transplant recipients have an increased risk of non-melanoma skin cancer (NMSC) compared to in the general population. Here, we show polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of NMSC in a general, nontransplant setting, can predict risk of, and time to posttransplant skin cancer. Genetic variants, reaching predefined P-value thresholds were chosen from published squamous cell carcinoma (SCC) and basal cell carcinoma (BCC) nontransplant GWAS. Using these GWAS, BCC and SCC PRS were calculated for each sample across three European ancestry renal transplant cohorts (n = 889) and tested as predictors of case:control status and time to NMSC posttransplant. BCC PRS calculated at P-value threshold 1 × 10 −5 was the most significant predictor of case:control status of NMSC posttransplant (OR = 1.61; adjusted P =.0022; AUC [full model adjusted for clinical predictors and PRS] = 0.81). SCC PRS at P-value threshold 1 × 10 −5 was the most significant predictor of time to posttransplant NMSC (adjusted P = 9.39 × 10 −7 ; HR = 1.41, concordance [full model] = 0.74). PRS of nontransplant NMSC is predictive of case:control status and time to NMSC posttransplant. These results are relevant to how genomics can risk stratify patients to help develop personalized treatment regimens

Additional file 6:Â Additional Acknowledgements. of Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)

Acknowledgements and detailed descriptions of the five studies that provided the data for the analyses in this paper. (PDF 68 kb

Application of Whole Exome Sequencing in Six Families with an Initial Diagnosis of Autosomal Dominant Retinitis Pigmentosa: Lessons Learned

<div><p>This study aimed to identify the genetics underlying dominant forms of inherited retinal dystrophies using whole exome sequencing (WES) in six families extensively screened for known mutations or genes. Thirty-eight individuals were subjected to WES. Causative variants were searched among single nucleotide variants (SNVs) and insertion/deletion variants (<i>indels</i>) and whenever no potential candidate emerged, copy number variant (CNV) analysis was performed. Variants or regions harboring a candidate variant were prioritized and segregation of the variant with the disease was further assessed using Sanger sequencing in case of SNVs and <i>indels</i>, and quantitative PCR (qPCR) for CNVs. SNV and <i>indel</i> analysis led to the identification of a previously reported mutation in <i>PRPH2</i>. Two additional mutations linked to different forms of retinal dystrophies were identified in two families: a known frameshift deletion in <i>RPGR</i>, a gene responsible for X-linked retinitis pigmentosa and p.Ser163Arg in <i>C1QTNF5</i> associated with Late-Onset Retinal Degeneration. A novel heterozygous deletion spanning the entire region of <i>PRPF31 </i>was also identified in the affected members of a fourth family, which was confirmed with qPCR. This study allowed the identification of the genetic cause of the retinal dystrophy and the establishment of a correct diagnosis in four families, including a large heterozygous deletion in <i>PRPF31</i>, typically considered one of the pitfalls of this method. Since all findings in this study are restricted to known genes, we propose that targeted sequencing using gene-panel is an optimal first approach for the genetic screening and that once known genetic causes are ruled out, WES might be used to uncover new genes involved in inherited retinal dystrophies.</p></div

Additional file 4: Table S3. of Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)

Details of LD expanded models. Number of LD-expanded (proxy) SNP-SNP models generated for each original discovered SNP-SNP model. (XLSX 34 kb

Additional file 1: Table S1. of Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)

Information for cohorts providing individual level data. Information regarding the geographic location, and numbers of individuals included from each cohort. (PDF 40 kb