Shared genetic liability and causal effects between major depressive disorder and insomnia.

Deciphering the genetic relationships between major depressive disorder (MDD) and insomnia may facilitate understanding biological mechanisms as well as inform more effective treatment regimens for these conditions. Here, we attempted to investigate mechanisms underlying relationships between MDD and insomnia in the context of shared genetic variations. Shared genetic variation was evaluated by polygenic analysis. In two-sample bidirectional Mendelian randomization (MR) analysis, causal relationships between MDD and insomnia were investigated; the list of shared genomic loci was identified using cross-trait meta-analysis. Putatively causal genes for the two diseases were prioritized by fine-mapping of transcriptome-wide associations. Polygenic analysis identified 15.1 thousand variants as causally influencing MDD, and 10.8 thousand variants as influencing insomnia. Among these variants, 8.5 thousand were shared between the two diseases. MR analysis suggests that genetic liability to MDD and to insomnia have mutual causal effects [MDD on insomnia with odds ratio (OR) = 1.25 and insomnia on MDD with OR = 2.23]. Cross-trait meta-analyses identified 89 genomic loci as being shared between MDD and insomnia, with some of them being prioritized as causal in subsequent fine-mapping of transcriptome-wide association signals. Analysis highlights possible role of endogenous production of nitric oxide in the brain, and the gonadotropic secretion in the pituitary as possibly physiological connectors of MDD and insomnia. Here, we show a substantial shared genetic liability and mutual causal links between MDD and insomnia. Presented findings provide novel insight into phenotypic relationship between these two interconnected conditions.

Gut microbiome and major depressive disorder: insights from two-sample Mendelian randomization.

BACKGROUND: Existing evidence suggests that alterations in the gut microbiome are closely associated with major depressive disorder (MDD). We aimed to reveal the causal relationships between MDD and various microbial taxa in the gut. METHODS: We used the two-sample Mendelian randomization (TSMR) to explore the bidirectional causal effects between gut microbiota and MDD. The genome-wide association studies summary results of gut microbiota were obtained from two large consortia, the MibioGen consortium and the Dutch Microbiome Project, which we analyzed separately. RESULTS: Our TSMR analysis identified 10 gut bacterial taxa that were protective against MDD, including phylum Actinobacteria, order Clostridiales, and family Bifidobacteriaceae (OR: 0.96 ∼ 0.98). Ten taxa were associated with an increased risk of MDD, including phyla Firmicutes and Proteobacteria, class Actinobacteria, and genus Alistipes (OR: 1.01 ∼ 1.09). On the other hand, MDD may decrease the abundance of 12 taxa, including phyla Actinobacteria and Firmicutes, families Bifidobacteriaceae and Defluviitaleaceae (OR: 0.63 ∼ 0.88). MDD may increase the abundance of 8 taxa, including phylum Bacteroidetes, genera Parabacteroides, and Bacteroides (OR: 1.12 ∼ 1.43). CONCLUSIONS: Our study supports that there are mutual causal relationships between certain gut microbiota and the development of MDD suggesting that gut microbiota may be targeted in the treatment of MDD.

Causal Associations between Posttraumatic Stress Disorder and COVID-19.

OBJECTIVE: We aimed to evaluate bidirectional genetic relationships between posttraumatic stress disorder (PTSD) and COVID-19. METHODS: We investigated potential causal associations between PTSD and two COVID-19 conditions (COVID-19 hospitalization and SARS-CoV-2 infection) via Mendelian randomization (MR) analyses. Three genome-wide association study (GWAS) summary datasets were used in the study, including PTSD (N = 174,659), SARS-CoV-2 infection (N = 2,597,856), and COVID-19 hospitalization (N = 2,095,324). We performed a literature-based analysis to uncover molecular pathways connecting PTSD and COVID-19. RESULTS: We found that PTSD exerts a causal effect on SARS-CoV-2 infection (odds ratio (OR): 1.10, 95% confidence interval (CI): 1.00-1.21, p = 0.048) and hospitalized COVID-19 (OR: 1.34, 95% CI: 1.07-1.67, p = 0.001). However, both SARS-CoV-2 infection and hospitalized COVID-19 were not associated with the risk of PTSD. Pathway analysis revealed that several immunity-related genes may link PTSD to COVID-19. CONCLUSIONS: Our study suggests that PTSD was associated with increased risks for COVID-19 susceptibility and severity. Early diagnosis and effective treatment of PTSD in individuals infected with the coronavirus may improve the management of the outcomes of COVID-19.

Genetic mechanisms of COVID-19 and its association with smoking and alcohol consumption.

We aimed to investigate the genetic mechanisms associated with coronavirus disease of 2019 (COVID-19) outcomes in the host and to evaluate the possible associations between smoking and drinking behavior and three COVID-19 outcomes: severe COVID-19, hospitalized COVID-19 and COVID-19 infection. We described the genomic loci and risk genes associated with the COVID-19 outcomes, followed by functional analyses of the risk genes. Then, a summary data-based Mendelian randomization (SMR) analysis, and a transcriptome-wide association study (TWAS) were performed for the severe COVID-19 dataset. A two-sample Mendelian randomization (MR) analysis was used to evaluate the causal associations between various measures of smoking and alcohol consumption and the COVID-19 outcomes. A total of 26 protein-coding genes, enriched in chemokine binding, cytokine binding and senescence-related functions, were associated with either severe COVID-19 or hospitalized COVID-19. The SMR and the TWAS analyses highlighted functional implications of some GWAS hits and identified seven novel genes for severe COVID-19, including CCR5, CCR5AS, IL10RB, TAC4, RMI1 and TNFSF15, some of which are targets of approved or experimental drugs. According to our studies, increasing consumption of cigarettes per day by 1 standard deviation is related to a 2.3-fold increase in susceptibility to severe COVID-19 and a 1.6-fold increase in COVID-19-induced hospitalization. Contrarily, no significant links were found between alcohol consumption or binary smoking status and COVID-19 outcomes. Our study revealed some novel COVID-19 related genes and suggested that genetic liability to smoking may quantitatively contribute to an increased risk for a severe course of COVID-19.

Causal effect of COVID-19 on Alzheimer's disease: A Mendelian randomization study.

It was reported that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may cause brain size reduction and cognitive decline. Whether COVID-19 may contribute to the development of Alzheimer's disease (AD) is not known. We conducted genetic correlation and Mendelian randomization (MR) analyses to assess genetic relationships and potential causal associations between AD and three COVID-19 outcomes (SARS-CoV-2 infection, COVID-19 hospitalization, and critical COVID-19) by utilizing genome-wide association study datasets on these traits. A map of COVID-19-driven molecular pathways was constructed to investigate potential mechanisms underlying the COVID-19 and AD connection. Genetic correlation analyses indicated that AD had a significant positive genetic correlation with hospitalized COVID-19 (rg = 0.271). The MR analysis from the inverse-variance-weighted model showed that genetic liabilities to hospitalized COVID-19 (odds ratio: 1.02, 95% confidence interval: 1.01-1.03) and critical COVID-19 (1.01, 1.00-1.02) were associated with an increased risk for AD. However, no causal effect of genetic liability to SARS-CoV-2 infection on AD was detected (1.03, 0.97-1.09). A total of 60 functionally interconnected genes were reported to mediate the COVID-19-AD connection, which showed functional enrichment in immunity-related pathways and tissue enrichment in the lung and brain. Our study suggests that severe COVID-19 may contribute to the development of AD, while suffering a mild case of COVID-19 may not increase the risk for AD. The influence of COVID-19 on AD may be mediated by immunity-related pathways acting predominantly in the lung and brain.

Causal associations and shared genetics between hypertension and COVID-19.

To evaluate the genetic relationship between hypertension and COVID-19 and explore the molecular pathways linking hypertension to COVID-19. We performed genetic correlation and Mendelian randomization (MR) analyses to assess potential associations between hypertension and hospitalized COVID-19. We compared genome-wide association signals to reveal shared genetic variation between hypertension and hospitalized COVID-19. Moreover, hypertension-driven molecular pathways were constructed based on large-scale literature data to understand the influence of hypertension on COVID-19 at the molecular level. Hypertension has a positive genetic correlation with COVID-19 (rg = 0.19). The MR analyses indicate that genetic liability to hypertension confers a causal effect on hospitalized COVID-19 (odds ratio [OR]: 1.05, confidence interval [CI]: 1.00-1.09, p = 0.030). Hypertension and hospitalized COVID-19 have three overlapping loci and share eight protein-coding risk genes, including ABO, CSF2, FUT2, IZUMO1, MAMSTR, NPNT, RASIP1, and WNT3. Molecular pathway analysis suggests that hypertension may promote the development of COVID-19 through the induction of inflammatory pathways. Our study suggests that genetically determined hypertension may increase the risk for severe COVID-19. The shared genetic variation and the connecting molecular pathways may underline causal links between hypertension and COVID-19.

A phenome-wide investigation of risk factors for severe COVID-19.

With the continued spread of COVID-19 globally, it is crucial to identify the potential risk or protective factors associated with COVID-19. Here, we performed genetic correlation analysis and Mendelian randomization analysis to examine genetic relationships between COVID-19 hospitalization and 405 health conditions and lifestyle factors in 456 422 participants from the UK Biobank. The genetic correlation analysis revealed 134 positive and 65 negative correlations, including those with intakes of a variety of dietary components. The MR analysis indicates that a set of body fat-related traits, maternal smoking around birth, basal metabolic rate, lymphocyte count, peripheral enthesopathies and allied syndromes, blood clots in the leg, and arthropathy are causal risk factors for severe COVID-19, while higher education attainment, physical activity, asthma, and never smoking status protect against the illness. Our findings have implications for risk stratification in patients with COVID-19 and the prevention of its severe outcomes.

The signature of SARS-CoV-2 evolution reflects selective pressures within human guts.

In somatic cells, microRNAs (miRNAs) bind to the genomes of RNA viruses and influence their translation and replication. In London and Berlin samples represented in GISAID database, we traced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineages and divided these sequenced in two groups, "Ancestral variants" and "Omicrons," and analyzed them through the prism of the tissue-specific binding between host miRNAs and viral messenger RNAs. We demonstrate a significant number of miRNA-binding sites in the NSP4 region of the SARS-CoV-2 genome, with evidence of evolutionary pressure within this region exerted by human intestinal miRNAs. Notably, in infected cells, NSP4 promotes the formation of double-membrane vesicles, which serve as the scaffolds for replication-transcriptional complexes and protect viral RNA from intracellular destruction. In 3 years of selection, the loss of many miRNA-binding sites in general and those within the NSP4 in particular has shaped the SARS-CoV-2 genomes. With that, the descendants of the BA.2 variants were promoted as dominant strains, which define current momentum of the pandemics.

Shared genetics and causal associations between COVID-19 and multiple sclerosis.

Neuroinflammation caused by COVID-19 negatively impacts brain metabolism and function, while pre-existing brain pathology may contribute to individuals' vulnerability to the adverse consequences of COVID-19. We used summary statistics from genome-wide association studies (GWAS) to perform Mendelian randomization (MR) analyses, thus assessing potential associations between multiple sclerosis (MS) and two COVID-19 outcomes (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infection and COVID-19 hospitalization). Genome-wide risk genes were compared between the GWAS datasets on hospitalized COVID-19 and MS. Literature-based analysis was conducted to construct molecular pathways connecting MS and COVID-19. We found that genetic liability to MS confers a causal effect on hospitalized COVID-19 (odd ratio [OR]: 1.09, 95% confidence interval: 1.03-1.16) but not on SARS-CoV-2 infection (1.03, 1.00-1.05). Genetic liability to hospitalized COVID-19 confers a causal effect on MS (1.15, 1.02-1.30). Hospitalized COVID-19 and MS share five risk genes within two loci, including TNFAIP8, HSD17B4, CDC37, PDE4A, and KEAP1. Pathway analysis identified a panel of immunity-related genes that may mediate the links between MS and COVID-19. Our study suggests that MS was associated with a 9% increased risk for COVID-19 hospitalization, while hospitalized COVID-19 was associated with a 15% increased risk for MS. Immunity-related pathways may underlie the link between MS on COVID-19.

Bidirectional causal associations between type 2 diabetes and COVID-19.

Observational studies have reported high comorbidity between type 2 diabetes (T2D) and severe COVID-19. However, the causality between T2D and COVID-19 has yet to be validated. We performed genetic correlation and Mendelian randomization (MR) analyses to assess genetic relationships and potential causal associations between T2D and three COVID-19 outcomes (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infection, COVID-19 hospitalization, and critical COVID-19). Molecular pathways connecting SARS-CoV-2 and COVID-19 were reconstructed to extract insights into the potential mechanisms underlying the connection. We identified a high genetic overlap between T2D and each COVID-19 outcome (genetic correlations 0.21-0.28). The MR analyses indicated that genetic liability to T2D confers a causal effect on hospitalized COVID-19 (odds ratio 1.08, 95% confidence interval [CI] 1.04-1.12) and critical COVID-19 (1.09, 1.03-1.16), while genetic liability to SARS-CoV-2 infection exerts a causal effect on T2D (1.25, 1.00-1.56). There was suggestive evidence that T2D was associated with an increased risk for SARS-CoV-2 infection (1.02, 1.00-1.03), while critical COVID-19 (1.06, 1.00-1.13) and hospitalized COVID-19 (1.09, 0.99-1.19) were associated with an increased risk for T2D. Pathway analysis identified a panel of immunity-related genes that may mediate the links between T2D and COVID-19 at the molecular level. Our study provides robust support for the bidirectional causal associations between T2D and COVID-19. T2D may contribute to amplifying the severity of COVID-19, while the liability to COVID-19 may increase the risk for T2D.

Causal associations between COVID-19 and childhood mental disorders.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can invade both the peripheral and central nervous systems and impact the function of the brain. Therefore, it is necessary to evaluate the mutual influences between COVID-19 outcomes and childhood mental disorders. METHODS: We examined genetic correlations and potential causalities between three childhood mental disorders and three COVID-19 phenotypes by genetically proxied analyses. The three mental disorders included attention-deficit/hyperactivity disorder (ADHD, N = 292,548), Tourette's syndrome (TS, N = 14,307), and autism spectrum disorder (ASD, N = 46,350). The three COVID-19 traits included SARS-CoV-2 infection (N = 2,597,856), hospitalized COVID-19 (N = 2,095,324), and critical COVID-19 (N = 1,086,211). Literature-based analysis was used to build gene-based pathways connecting ADHD and COVID-19. RESULTS: ADHD was positively correlated with the three COVID-19 outcomes (Rg: 0.22 ~ 0.30). Our Mendelian randomization (MR) analyses found that ADHD confers a causal effect on hospitalized COVID-19 (odds ratio (OR): 1.36, 95% confidence interval (CI): 1.10-1.69). TS confers a causal effect on critical COVID-19 (OR: 1.14, 95% CI: 1.04-1.25). Genetic liability to the COVID-19 outcomes may not increase the risk for the childhood mental disorders. Pathway analysis identified several immunity-related genes that may link ADHD to COVID-19, including CRP, OXT, IL6, PON1, AR, TNFSF12, and IL10. CONCLUSIONS: Our study suggests that both ADHD and TS may augment the severity of COVID-19 through immunity-related pathways. However, our results did not support a causal role of COVID-19 in the risk for the childhood mental disorders.

Incidental germline findings during molecular profiling of tumor tissues for precision oncology: molecular survey and methodological obstacles.

BACKGROUND: A fraction of patients referred for complex molecular profiling of biopsied tumors may harbor germline variants in genes associated with the development of hereditary cancer syndromes (HCS). Neither the bioinformatic analysis nor the reporting of such incidental germline findings are standardized. METHODS: Data from Next-Generation Sequencing (NGS) of biopsied tumor samples referred for complex molecular profiling were analyzed for germline variants in HCS-associated genes. Analysis of variant origin was performed employing bioinformatic algorithms followed by manual curation. When possible, the origin of the variant was validated by Sanger sequencing of the sample of normal tissue. The variants' pathogenicity was assessed according to ACMG/AMP. RESULTS: Tumors were sampled from 183 patients (Males: 75 [41.0%]; Females: 108 [59.0%]; mean [SD] age, 57.7 [13.3] years) and analysed by targeted NGS. The most common tumor types were colorectal (19%), pancreatic (13%), and lung cancer (10%). A total of 56 sequence variants in genes associated with HCS were detected in 40 patients. Of them, 17 variants found in 14 patients were predicted to be of germline origin, with 6 variants interpreted as pathogenic (PV) or likely pathogenic (LPV), and 9 as variants of uncertain significance (VUS). For the 41 out of 42 (97%) missense variants in HCS-associated genes, the results of computational prediction of variant origin were concordant with that of experimental examination. We estimate that Sanger sequencing of a sample of normal tissue would be required for ~ 1-7% of the total assessed cases with PV or LPV, when necessity to follow with genetic counselling referral in ~ 2-15% of total assessed cases (PV, LPV or VUS found in HCS genes). CONCLUSION: Incidental findings of pathogenic germline variants are common in data from cancer patients referred for complex molecular profiling. We propose an algorithm for the management of patients with newly detected variants in genes associated with HCS.

Genetic Relationships between Attention-Deficit/Hyperactivity Disorder, Autism Spectrum Disorder, and Intelligence.

INTRODUCTION: Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) commonly co-occur; both traits exert an influence on intelligence scores. Genetic relationships between these three traits are far from being clear. METHODS: The summary results of genome-wide association studies of ADHD (20,183 cases and 35,191 controls), ASD (18,381 cases and 27,969 controls), and intelligence (269,867 participants) were used for the analyses. Local genetic correlation analysis and polygenic overlap analysis were used to explore the shared genetic components between ADHD, ASD, and intelligence. Mendelian randomization (MR) analysis was used to examine the causal associations between ADHD, ASD, and intelligence. A cross-trait meta-analysis was performed to identify pleiotropic genetic variants across the three traits. RESULTS: Our results showed that intelligence has a positive and negative genetic correlation with ASD and ADHD, respectively, including three hub genomic regions showing correlated genetic effects across the three traits. Polygenic overlap analysis indicated that all the risk variants contributing to ADHD are overlapped with half of those for intelligence, and the majority of the shared variants have opposite effect directions between them. The majority of risk variants (80%) of ASD are overlapped with almost all the risk variants of intelligence (97%). Notably, some ASD/intelligence overlapping variants displayed opposing effects on these two conditions. MR analysis showed that the genetic liability to higher intelligence was associated with an increased risk for ASD (OR = 1.12) and a decreased risk for ADHD (OR = 0.78). Cross-trait meta-analyses identified 170 pleiotropic genomic loci across the three traits, including 12 novel loci. Functional analyses of the novel genes support their potential involvement in neurodevelopment. CONCLUSION: Our results suggest that ADHD is associated with inheriting a reduced set of low-intelligence alleles, whereas ASD results from incongruous effects from a mixture of high-intelligence and low-intelligence contributing alleles summed up with additional, ASD-specific risk variants not associated with intelligence.

Involvement of the long intergenic non-coding RNA LINC00461 in schizophrenia.

OBJECTIVE: LINC00461 is a highly conserved intergenic non-protein coding RNA that was implicated in schizophrenia at the genome-wide level. We aim to explore potential mechanisms underlying the involvement of LINC00461 in schizophrenia. METHODS: We performed a meta-analysis to investigate the association of LINC00461 rs410216 with schizophrenia, and evaluate the effects of the rs410216 on hippocampal volume and function using the functional magnetic resonance imaging (fMRI) analysis. We utilized the GTEx dataset to profile the expression distribution of LINC00461 across different brain regions, and to investigate the potential impact of the risk SNPs on the expression of LINC00461 and other nearby genes. We compared blood expression levels of LINC00461 between schizophrenia patients and controls. RESULTS: Here we show that single-nucleotide polymorphisms (SNPs) located in regulatory elements spanning the LINC00461 region are significantly associated with schizophrenia (index SNP rs410216, Pmeta = 1.43E-05); subjects carrying the risk allele of rs410216 showed decreased hippocampal volume. However, no significant association of the rs410216 variant with hippocampal activation was observed. Moreover, the expression level of LINC00461 mRNA was significantly lower in first-onset schizophrenia patients, and the risk allele also predicts a lower transcriptional level of LINC00461 in the hippocampus. CONCLUSION: Together, these convergent lines of evidence implicate inadequate LINC00461 expression in the hippocampus in the development of schizophrenia, providing novel insight into the genetic architecture and biological etiology of schizophrenia.

A New Structural Model of Apolipoprotein B100 Based on Computational Modeling and Cross Linking.

ApoB-100 is a member of a large lipid transfer protein superfamily and is one of the main apolipoproteins found on low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) particles. Despite its clinical significance for the development of cardiovascular disease, there is limited information on apoB-100 structure. We have developed a novel method based on the "divide and conquer" algorithm, using PSIPRED software, by dividing apoB-100 into five subunits and 11 domains. Models of each domain were prepared using I-TASSER, DEMO, RoseTTAFold, Phyre2, and MODELLER. Subsequently, we used disuccinimidyl sulfoxide (DSSO), a new mass spectrometry cleavable cross-linker, and the known position of disulfide bonds to experimentally validate each model. We obtained 65 unique DSSO cross-links, of which 87.5% were within a 26 Å threshold in the final model. We also evaluated the positions of cysteine residues involved in the eight known disulfide bonds in apoB-100, and each pair was measured within the expected 5.6 Å constraint. Finally, multiple domains were combined by applying constraints based on detected long-range DSSO cross-links to generate five subunits, which were subsequently merged to achieve an uninterrupted architecture for apoB-100 around a lipoprotein particle. Moreover, the dynamics of apoB-100 during particle size transitions was examined by comparing VLDL and LDL computational models and using experimental cross-linking data. In addition, the proposed model of receptor ligand binding of apoB-100 provides new insights into some of its functions.

Causal influences of neuroticism on mental health and cardiovascular disease.

We investigated the relationship between neuroticism and 16 mental and 18 physical traits using summary results of genome-wide association studies for these traits. LD score regression was used to investigate genetic correlations between neuroticism and the 34 health outcomes. Mendelian randomization was performed to investigate mutual causal relationships between neuroticism and the 34 health outcomes. Neuroticism genetically correlates with a majority of health-related traits and confers causal effects on 12 mental traits (major depressive disorder (MDD), insomnia, subjective well-being (SWB, negatively), schizophrenia, attention-deficit/hyperactivity disorder, alcohol dependence, loneliness, anorexia nervosa, anxiety disorder, bipolar disorder, obsessive-compulsive disorder, and psychiatric disorders) and two physical diseases (cardiovascular disease and hypertensive disease). Conversely, MDD, SWB, and insomnia have a causal effect on neuroticism. We highlighted key genes contributing to the causal associations between neuroticism and MDD, including RBFOX1, RERE, SOX5, and TCF4, and those contributing to the causal associations between neuroticism and cardiovascular diseases, including MAD1L1, ARNTL, RERE, and SOX6. The present study indicates that genetic variation mediates the causal influences of neuroticism on mental health and cardiovascular diseases.

Novel bioinformatics quality control metric for next-generation sequencing experiments in the clinical context.

As the use of next-generation sequencing (NGS) for the Mendelian diseases diagnosis is expanding, the performance of this method has to be improved in order to achieve higher quality. Typically, performance measures are considered to be designed in the context of each application and, therefore, account for a spectrum of clinically relevant variants. We present EphaGen, a new computational methodology for bioinformatics quality control (QC). Given a single NGS dataset in BAM format and a pre-compiled VCF-file of targeted clinically relevant variants it associates this dataset with a single arbiter parameter. Intrinsically, EphaGen estimates the probability to miss any variant from the defined spectrum within a particular NGS dataset. Such performance measure virtually resembles the diagnostic sensitivity of given NGS dataset. Here we present case studies of the use of EphaGen in context of BRCA1/2 and CFTR sequencing in a series of 14 runs across 43 blood samples and 504 publically available NGS datasets. EphaGen is superior to conventional bioinformatics metrics such as coverage depth and coverage uniformity. We recommend using this software as a QC step in NGS studies in the clinical context. Availability: https://github.com/m4merg/EphaGen or https://hub.docker.com/r/m4merg/ephagen.

Medical Genetics, Genomics and Bioinformatics Aid in Understanding Molecular Mechanisms of Human Diseases.

Molecular mechanisms of human disease progression often have complex genetic underpinnings, and sophisticated sequencing approaches coupled with advanced analytics [...].

A comparison of BeadChip and WGS genotyping outputs using partial validation by sanger sequencing.

BACKGROUND: Head-to-head comparison of BeadChip and WGS/WES genotyping techniques for their precision is far from straightforward. A tool for validation of high-throughput genotyping calls such as Sanger sequencing is neither scalable nor practical for large-scale DNA processing. Here we report a cross-validation analysis of genotyping calls obtained via Illumina GSA BeadChip and WGS (Illumina HiSeq X Ten) techniques. RESULTS: When compared to each other, the average precision and accuracy of BeadChip and WGS genotyping techniques exceeded 0.991 and 0.997, respectively. The average fraction of discordant variants for both platforms was found to be 0.639%. A sliding window approach was utilized to explore genomic regions not exceeding 500 bp encompassing a maximal amount of discordant variants for further validation by Sanger sequencing. Notably, 12 variants out of 26 located within eight identified regions were consistently discordant in related calls made by WGS and BeadChip. When Sanger sequenced, a total of 16 of these genotypes were successfully resolved, indicating that a precision of WGS and BeadChip genotyping for this genotype subset was at 0.81 and 0.5, respectively, with accuracy values of 0.87 and 0.61. CONCLUSIONS: We conclude that WGS genotype calling exhibits higher overall precision within the selected variety of discordantly genotyped variants, though the amount of validated variants remained insufficient.

Identifying common genome-wide risk genes for major psychiatric traits.

Major psychiatric traits are genetically inter-correlated with one another, but it not well known which genes play pleiotropic effects across different traits. We curated and compared genes identified from large-scale genome-wide association studies for seven psychiatric traits, including depression, bipolar disorder, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, anxiety and neuroticism. We then explored biological functions of the top pleiotropic genes. A total of 243 cross-trait genes were identified for the seven traits. Except for autism spectrum disorder, there was significant enrichment of overlapped genes across these psychiatric traits. Chromosome 5q14.3, 11q23.2, and 7p22.3 are the three genomic regions conferring highest pleiotropic effects for these psychiatric traits. The long non-coding gene LINC00461 showed the highest pleiotropic effects on five psychiatric traits. In silico and functional studies with mice support the vital role of LINC00461 in neurodevelopment. In sum, our study provides insights into the shared genetic liability among major psychiatric traits.