Enrichment Of Single Nucleotide Polymorphisms Research Articles

Investigating causality and shared genetic architecture between body mass index and cognitive function: a genome-wide cross-trait analysis and bi-directional Mendelian randomization study

Background and objectivesObservational studies have established a connection between body mass index (BMI) and an increased risk of cognitive decline. However, a comprehensive investigation into the causal relationships between BMI and cognitive function across diverse age groups, as well as the genetic underpinnings of this relationship, has been notably lacking. This study aims to investigate causality and the shared genetic underpinnings of between BMI and cognitive function by conducting a thorough genome-wide analysis, thereby provide valuable insights for developing personalized intervention strategies to promote cognitive health.MethodsGenetic associations between BMI and cognitive function were thoroughly investigated through covariate genetic analysis and chained imbalance score regression, utilizing data from genome-wide association studies (GWAS). Bi-directional Mendelian Randomization (MR) was employed to uncover associations and potential functional genes were further scrutinized through Cross-trait meta-analysis and Summary-data-based MR (SMR). Subsequently, a detailed examination of the expression profiles of the identified risk SNPs in tissues and cells was conducted.ResultsThe study found a significant negative correlation between BMI and cognitive function (β = −0.16, P = 1.76E-05), suggesting a causal linkage where higher BMI values were predictive of cognitive impairment. We identified 5 genetic loci (rs6809216, rs7187776, rs11713193, rs13096480, and rs13107325) between BMI and cognitive function by cross-trait meta-analysis and 5 gene-tissue pairs were identified by SMR analysis. Moreover, two novel risk genes TUFM and MST1R were shared by both cross-trait analysis and SMR analysis, which had not been observed in previous studies. Furthermore, significant enrichment of single nucleotide polymorphisms (SNPs) at tissue- and cell-specific levels was identified for both BMI and cognitive function, predominantly within the brain.ConclusionThis study uncovers a causal relationship between BMI and cognitive function, with the discovery of TUFM and MST1R as shared genetic factors associated with both conditions. This novel finding offers new insights into the development of preventative strategies for cognitive decline in obese individuals, and further enhances our understanding of the underlying pathophysiology of these conditions. Furthermore, these findings could serve as a guide for the development of innovative therapeutic approaches to address cognitive decline in obese individuals.

Genetic correlation between smoking behavior and gastroesophageal reflux disease: insights from integrative multi-omics data

BackgroundObservational studies have preliminarily revealed an association between smoking and gastroesophageal reflux disease (GERD). However, little is known about the causal relationship and shared genetic architecture between the two. This study aims to explore their common genetic correlations by leveraging genome-wide association studies (GWAS) of smoking behavior—specifically, smoking initiation (SI), never smoking (NS), ever smoking (ES), cigarettes smoked per day (CPD), age of smoking initiation(ASI) and GERD.MethodsFirstly, we conducted global cross-trait genetic correlation analysis and heritability estimation from summary statistics (HESS) to explore the genetic correlation between smoking behavior and GERD. Then, a joint cross-trait meta-analysis was performed to identify shared “pleiotropic SNPs” between smoking behavior and GERD, followed by co-localization analysis. Additionally, multi-marker analyses using annotation (MAGMA) were employed to explore the degree of enrichment of single nucleotide polymorphism (SNP) heritability in specific tissues, and summary data-based Mendelian randomization (SMR) was further utilized to investigate potential functional genes. Finally, Mendelian randomization (MR) analysis was conducted to explore the causal relationship between the smoking behavior and GERD.ResultsConsistent genetic correlations were observed through global and local genetic correlation analyses, wherein SI, ES, and CPD showed significantly positive genetic correlations with GERD, while NS and ASI showed significantly negative correlations. HESS analysis also identified multiple significantly associated loci between them. Furthermore, three novel “pleiotropic SNPs” (rs4382592, rs200968, rs1510719) were identified through cross-trait meta-analysis and co-localization analysis to exist between SI, NS, ES, ASI, and GERD, mapping the genes MED27, HIST1H2BO, MAML3 as new pleiotropic genes between SI, NS, ES, ASI, and GERD. Moreover, both smoking behavior and GERD were found to be co-enriched in multiple brain tissues, with GMPPB, RNF123, and RBM6 identified as potential functional genes co-enriched in Cerebellar Hemisphere, Cerebellum, Cortex/Nucleus accumbens in SI and GERD, and SUOX identified in Caudate nucleus, Cerebellum, Cortex in NS and GERD. Lastly, consistent causal relationships were found through MR analysis, indicating that SI, ES, and CPD increase the risk of GERD, while NS and higher ASI decrease the risk.ConclusionWe identified genetic loci associated with smoking behavior and GERD, as well as brain tissue sites of shared enrichment, prioritizing three new pleiotropic genes and four new functional genes. Finally, the causal relationship between smoking behavior and GERD was demonstrated, providing insights for early prevention strategies for GERD.

Investigating the shared genetic architecture between frailty and insomnia.

The epidemiological association between frailty and insomnia is well established, yet the presence of a common genetic etiology is still uncertain. Further exploration is needed to ascertain the causal relationship between frailty and insomnia. Utilizing data obtained from genome-wide association studies (GWAS) summaries, we utilized the linkage disequilibrium score regression (LDSC) to determine the genetic correlation existing between frailty and insomnia. The determination of causality was achieved through the application of two-sample Mendelian randomization. We investigated the enrichment of single nucleotide polymorphism (SNP) at various tissue types utilizing stratified LD score regression (S-LDSC) and multimarker analysis of genome annotation (MAGMA). Common risk SNPs were identified using Multi-Trait Analysis of GWAS (MTAG) and Cross-Phenotype Association (CPASSOC). We further investigated the expression profiles of risk genes in tissues using Summary-data-based Mendelian randomization(SMR) based on pooled data, to explore potential functional genes. Our findings indicated a significant genetic correlation between frailty and insomnia, highlighting SNPs sharing risk (rs34290943, rs10865954), with a pronounced correlation in the localized genomic region 3p21.31. Partitioned genetic analysis revealed 24 functional elements significantly associated with both frailty and insomnia. Furthermore, mendelian randomization revealed a causal connection between frailty and insomnia. The genetic correlation between frailty and insomnia showed enrichment in 11 brain regions (S-LDSC) and 9 brain regions (MAGMA), where four functional genes (RMB6, MST1R, RF123, and FAM212A) were identified. This study suggests the existence of a genetic correlation and common risk genes between frailty and insomnia, contributing to a deeper comprehension of their pathogenesis and assists in identifying potential therapeutic targets.

The Genetic Base for Drug Addiction.

Objective: There are many evidences about relationship between eating behavior and drug addiction. A number of susceptibility loci that point to shared higher order genetic pathways underling addiction were found in genetic studies. This study assumed that a genome-wide association study (GWAS) of food addiction would produce important enrichment in genes and pathways related to addiction. Methods: This study done among 314 women of European ancestry, by using a GWAS of food addiction, which is determined by the modified Yale Food Addiction Scale (mYFAS). Results for enrichment of single nucleotide polymorphisms (SNPs) (n 5 44), genes (n 5 238) and pathways (n 5 11) involved in drug addiction were tested. Results: Two loci met GW-significance (P< 2.5 10 -8) with no obvious roles in eating behavior, they are mapping to 17q21.31 and 11q13.4. GW results were significantly enriched for gene members of the MAPK signaling pathway (P = 0.02). After adjustment for multiple testing, candidate SNP or gene for drug addiction was not linked with food addiction. Conclusions: limited support was delivered for shared genetic underpinnings of drug addiction and food addiction, although the GWAS of mYFAS, need further investigation and follow up.

The Genetic Base for Drug Addiction.

Objective: There are many evidences about relationship between eating behavior and drug addiction. A number of susceptibility loci that point to shared higher order genetic pathways underling addiction were found in genetic studies. This study assumed that a genome-wide association study (GWAS) of food addiction would produce important enrichment in genes and pathways related to addiction. Methods: This study done among 314 women of European ancestry, by using a GWAS of food addiction, which is determined by the modified Yale Food Addiction Scale (mYFAS). Results for enrichment of single nucleotide polymorphisms (SNPs) (n 5 44), genes (n 5 238) and pathways (n 5 11) involved in drug addiction were tested. Results: Two loci met GW-significance (P< 2.5 10 -8) with no obvious roles in eating behavior, they are mapping to 17q21.31 and 11q13.4. GW results were significantly enriched for gene members of the MAPK signaling pathway (P = 0.02). After adjustment for multiple testing, candidate SNP or gene for drug addiction was not linked with food addiction. Conclusions: limited support was delivered for shared genetic underpinnings of drug addiction and food addiction, although the GWAS of mYFAS, need further investigation and follow up.

RNA-seq based transcriptome profiling provides important insights into progression of gastric cancer

Abstract Background Gastric cancer (GC), ranking fifth among cancers, is the third most common cause of cancer related mortality in the world. The symptoms of GC occur rather in the late stages, making early diagnosis a challenging task to clinicians compromising disease management. Therefore, efforts are underway to develop effective biomarkers for early diagnosis of the disease and to identify potential targets for drug development. Methods Illumina RNA-Seq based transcriptome profiles of six tumor and normal tissue pairs at different stages (stage I, II and III) of GC were generated to identify differentially expressed genes (DEGs) and single nucleotide polymorphisms (SNPs) implicated in gastric tumorigenesis. The major clusters of candidate genes at different stages of GC and associated enriched gene ontology terms were analysed. Transcription factors involved in GC were also identified and the protein-protein interaction networks were constructed. Results A total of 2207 differentially expressed genes including 972 upregulated genes and 1235 downregulated genes were identified covering stage I, II and III of disease progression. The SNP profiles revealed gene enrichment in cancer related pathways including apoptosis, mTOR and MAPK signalling. The DEGs were accommodated in various ontology categories primarily with digestion system and digestive tract development processes. Functional enrichment of SNPs showed GO categories such as immune system process, regulation of signalling, response to stress, transport, etc. Furthermore, 18 upregulated and 21 downregulated transcription factors were identified during cancer progression. Conclusion Stage-specific identification of DEGs and transcription factors may help in the better understanding of the molecular pathogenesis of gastric cancer. Our findings will also provide useful leads for developing future strategies for the management of gastric cancer. Legal entity responsible for the study The author. Funding Defence Institute of Physiology & Allied Sciences (DIPAS), Defence Research and Development Organisation (DRDO). Disclosure The author has declared no conflicts of interest.

Genome-wide association study of panic disorder reveals genetic overlap with neuroticism and depression.

Panic disorder (PD) has a lifetime prevalence of 2-4% and heritability estimates of 40%. The contributory genetic variants remain largely unknown, with few and inconsistent loci having been reported. The present report describes the largest genome-wide association study (GWAS) of PD to date comprising genome-wide genotype data of 2248 clinically well-characterized PD patients and 7992 ethnically matched controls. The samples originated from four European countries (Denmark, Estonia, Germany, and Sweden). Standard GWAS quality control procedures were conducted on each individual dataset, and imputation was performed using the 1000 Genomes Project reference panel. A meta-analysis was then performed using the Ricopili pipeline. No genome-wide significant locus was identified. Leave-one-out analyses generated highly significant polygenic risk scores (PRS) (explained variance of up to 2.6%). Linkage disequilibrium (LD) score regression analysis of the GWAS data showed that the estimated heritability for PD was 28.0-34.2%. After correction for multiple testing, a significant genetic correlation was found between PD and major depressive disorder, depressive symptoms, and neuroticism. A total of 255 single-nucleotide polymorphisms (SNPs) with p < 1 × 10-4 were followed up in an independent sample of 2408 PD patients and 228,470 controls from Denmark, Iceland and the Netherlands. In the combined analysis, SNP rs144783209 showed the strongest association with PD (pcomb = 3.10 × 10-7). Sign tests revealed a significant enrichment of SNPs with a discovery p-value of <0.0001 in the combined follow up cohort (p = 0.048). The present integrative analysis represents a major step towards the elucidation of the genetic susceptibility to PD.

Improved detection of common variants in coronary artery disease and blood pressure using a pleiotropy cFDR method

Plenty of genome-wide association studies (GWASs) have identified numerous single nucleotide polymorphisms (SNPs) for coronary artery disease (CAD) and blood pressure (BP). However, these SNPs only explain a small proportion of the heritability of two traits/diseases. Although high BP is a major risk factor for CAD, the genetic intercommunity between them remain largely unknown. To recognize novel loci associated with CAD and BP, a genetic-pleiotropy-informed conditional false discovery rate (cFDR) method was applied on two summary statistics of CAD and BP from existing GWASs. Stratified Q-Q and fold enrichment plots showed a high pleiotropic enrichment of SNPs associated with two traits. Adopting a cFDR of 0.05 as a threshold, 55 CAD-associated loci (25 variants being novel) and 47 BP loci (18 variants being novel) were identified, 25 of which were pleiotropic loci (13 variants being novel) for both traits. Among the 32 genes these 25 SNPs were annotated to, 20 genes were newly detected compared to previous GWASs. This study showed the cFDR approach could improve gene discovery by incorporating GWAS datasets of two related traits. These findings may provide novel understanding of etiology relationships between CAD and BP.

Identifying and ranking potential driver genes of Alzheimer's disease using multiview evidence aggregation.

MotivationLate onset Alzheimer’s disease is currently a disease with no known effective treatment options. To better understand disease, new multi-omic data-sets have recently been generated with the goal of identifying molecular causes of disease. However, most analytic studies using these datasets focus on uni-modal analysis of the data. Here, we propose a data driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our article are: (i) a general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature sets and identifying other potential driver genes which have similar feature representations, and (ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types.ResultsWe demonstrate the utility of our machine learning algorithm on two benchmark multiview datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimer’s. We show that our ranked genes show a significant enrichment for single nucleotide polymorphisms associated with Alzheimer’s and are enriched in pathways that have been previously associated with the disease.Availability and implementationSource code and link to all feature sets is available at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking.

Genetic associations of breast and prostate cancer are enriched for regulatory elements identified in disease-related tissues.

Although genome-wide association studies (GWAS) have identified hundreds of risk loci for breast and prostate cancer, only a few studies have characterized the GWAS association signals across functional genomic annotations with a particular focus on single nucleotide polymorphisms (SNPs) located in DNA regulatory elements. In this study, we investigated the enrichment pattern of GWAS signals for breast and prostate cancer in genomic functional regions located in normal tissue and cancer cell lines. We quantified the overall enrichment of SNPs with breast and prostate cancer association p values < 1 × 10-8 across regulatory categories. We then obtained annotations for DNaseI hypersensitive sites (DHS), typical enhancers, and super enhancers across multiple tissue types, to assess if significant GWAS signals were selectively enriched in annotations found in disease-related tissue. Finally, we quantified the enrichment of breast and prostate cancer SNP heritability in regulatory regions, and compared the enrichment pattern of SNP heritability with GWAS signals. DHS, typical enhancers, and super enhancers identified in the breast cancer cell line MCF-7 were observed with the highest enrichment of genome-wide significant variants for breast cancer. For prostate cancer, GWAS signals were mostly enriched in DHS and typical enhancers identified in the prostate cancer cell line LNCaP. With progressively stringent GWAS p value thresholds, an increasing trend of enrichment was observed for both diseases in DHS, typical enhancers, and super enhancers located in disease-related tissue. Results from heritability enrichment analysis supported the selective enrichment pattern of functional genomic regions in disease-related cell lines for both breast and prostate cancer. Our results suggest the importance of studying functional annotations identified in disease-related tissues when characterizing GWAS results, and further demonstrate the role of germline DNA regulatory elements from disease-related tissue in breast and prostate carcinogenesis.

Shared genetic underpinnings of childhood obesity and adult cardiometabolic diseases

BackgroundObesity during childhood can lead to increased risk of adverse cardiometabolic diseases such as type 2 diabetes and coronary artery disease during adult life. Evidence for strong genetic correlations between child and adult body mass index (BMI) suggest the possibility of shared genetic effects. We performed a test for pleiotropy (shared genetics) and functional enrichment of single nucleotide polymorphisms (SNPs) associated with childhood BMI and 15 adult cardiometabolic traits using a unified statistical approach that integrates pleiotropy and functional annotation data.ResultsPleiotropic genetic effects were significantly abundant in 13 out of 15 childhood BMI-adult cardiometabolic trait tests (P < 3.3 × 10−3). SNPs associated with both childhood BMI and adult traits were more likely to be functionally deleterious than SNPs associated with neither trait. Genetic variants associated with increased childhood obesity tend to increase risk of cardiometabolic diseases in adulthood. We replicated 39 genetic loci that are known to be associated with childhood BMI and adult traits (coronary artery disease, HDL cholesterol, myocardial infarction, triglycerides, total cholesterol, type 2 diabetes, BMI, waist circumference, and waist-to-hip ratio) in previous genome-wide association studies. We also found a novel association of rs12446632 near GPRC5B, which is highly expressed in adipose tissue and the central nervous system, with adult HDL cholesterol.ConclusionsThis study found significant pleiotropic genetic effects and enrichment of functional annotations in genetic variants that were jointly associated with childhood obesity and adult cardiometabolic diseases. The findings provide new avenues to disentangle the genetic basis of life course associations between childhood obesity and adult cardiometabolic diseases.

Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies.

Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (P ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population and common genetic variants can explain up to 80% of their variabilities (median heritability 34.8%). We observed omnigenic impact across the genome and examined the enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks, exhibit a symmetric pattern across left and right hemispheres, and are consistent in females and males (correlation = 0.638). We repeated the main analysis in Alzheimer's Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, which demonstrated that more stable estimates can be obtained from the UK Biobank.

ShRangeSim: Simulation of Single Nucleotide Polymorphism Clusters in Next-Generation Sequencing Data

Genomic variations are in the focus of research to uncover mechanisms of host-pathogen interactions and diseases such as cancer. Nowadays, next-generation sequencing (NGS) data are analyzed through dedicated pipelines to detect them. Surrogate NGS data in conjunction with genomic variations help to evaluate pipelines and validate their outcomes, fostering selection of proper tools for a given scientific question. I describe how existing approaches for simulating NGS data in conjunction with genomic variations fail to model local enrichments of single nucleotide polymorphisms (SNPs), so called SNP clusters. Two distributions for count data are applied to publicly available collections of genomic variations. The results suggest modeling of SNP cluster sizes by overdispersion-aware distributions.

Novel Loci Associated With Attention-Deficit/Hyperactivity Disorder Are Revealed by Leveraging Polygenic Overlap With Educational Attainment

Attention-deficit/hyperactivity disorder (ADHD) is a common and highly heritable psychiatric condition. By exploiting the reported relationship between ADHD and educational attainment (EA), we aimed to improve discovery of ADHD-associated genetic variants and to investigate genetic overlap between these phenotypes. A conditional/conjunctional false discovery rate (condFDR/conjFDR) method was applied to genome-wide association study (GWAS) data on ADHD (2,064 trios, 896 cases, and 2,455 controls) and EA (n=328,917) to identify ADHD-associated loci and loci overlapping between ADHD and EA. Identified single nucleotide polymorphisms (SNPs) were tested for association in an independent population-based study of ADHD symptoms (n=17,666). Genetic correlation between ADHD and EA was estimated using LD score regression and Pearson correlation. At levels of condFDR<0.01 and conjFDR<0.05, we identified 5 ADHD-associated loci, 3 of these being shared between ADHD and EA. None of these loci had been identified in the primary ADHD GWAS, demonstrating the increased power provided by the condFDR/conjFDR analysis. Leading SNPs for 4 of 5 identified regions are in introns of protein coding genes (KDM4A, MEF2C, PINK1, RUNX1T1), whereas the remaining one is an intergenic SNP on chromosome 2 at 2p24. Consistent direction of effects in the independent study of ADHD symptoms was shown for 4 of 5 identified loci. A polygenic overlap between ADHD and EA was supported by significant genetic correlation (rg=-0.403, p=7.90×10-8) and >10-fold mutual enrichment of SNPs associated with both traits. We identified 5 novel loci associated with ADHD and provided evidence for a shared genetic basis between ADHD and EA. These findings could aid understanding of the genetic risk architecture of ADHD and its relation to EA.

Novel common variants associated with body mass index and coronary artery disease detected using a pleiotropic cFDR method

Genome-wide association studies (GWAS) have been successfully applied in identifying single nucleotide polymorphisms (SNPs) associated with body mass index (BMI) and coronary heart disease (CAD). However, the SNPs to date can only explain a small percentage of the genetic variances of traits. Here, we applied a genetic pleiotropic conditional false discovery rate (cFDR) method that combines summary statistic p values from different multi-center GWAS datasets, to detect common genetic variants associated with these two traits. The enrichment of SNPs associated with BMI and CAD was assessed by conditional Q-Q plots and the common variants were identified by the cFDR method. By applying the cFDR level of 0.05, 7 variants were identified to be associated with CAD (2 variants being novel), 34 variants associated with BMI (11 variants being novel), and 3 variants associated with both BMI and CAD (2 variants being novel). The SNP rs653178 (ATXN2) is noteworthy as this variant was replicated in an independent analysis. SNP rs12411886 (CNNM2) and rs794356 (HIP1) were of note as the annotated genes may be associated with processes that are functionally important in lipid metabolism. In conclusion, the cFDR method identified novel variants associated with BMI and/or CAD by effectively incorporating different GWAS datasets.

Bromodomain protein 4 discriminates tissue-specific super-enhancers containing disease-specific susceptibility loci in prostate and breast cancer

BackgroundEpigenetic information can be used to identify clinically relevant genomic variants single nucleotide polymorphisms (SNPs) of functional importance in cancer development. Super-enhancers are cell-specific DNA elements, acting to determine tissue or cell identity and driving tumor progression. Although previous approaches have been tried to explain risk associated with SNPs in regulatory DNA elements, so far epigenetic readers such as bromodomain containing protein 4 (BRD4) and super-enhancers have not been used to annotate SNPs. In prostate cancer (PC), androgen receptor (AR) binding sites to chromatin have been used to inform functional annotations of SNPs.ResultsHere we establish criteria for enhancer mapping which are applicable to other diseases and traits to achieve the optimal tissue-specific enrichment of PC risk SNPs. We used stratified Q-Q plots and Fisher test to assess the differential enrichment of SNPs mapping to specific categories of enhancers. We find that BRD4 is the key discriminant of tissue-specific enhancers, showing that it is more powerful than AR binding information to capture PC specific risk loci, and can be used with similar effect in breast cancer (BC) and applied to other diseases such as schizophrenia.ConclusionsThis is the first study to evaluate the enrichment of epigenetic readers in genome-wide associations studies for SNPs within enhancers, and provides a powerful tool for enriching and prioritizing PC and BC genetic risk loci. Our study represents a proof of principle applicable to other diseases and traits that can be used to redefine molecular mechanisms of human phenotypic variation.

Shared genetic variants suggest common pathways in allergy and autoimmune diseases

The relationship between allergy and autoimmune disorders is complex and poorly understood. We sought to investigate commonalities in genetic loci and pathways between allergy and autoimmune diseases to elucidate shared disease mechanisms. We meta-analyzed 2 genome-wide association studies on self-reported allergy and sensitization comprising a total of 62,330 subjects. These results were used to calculate enrichment for single nucleotide polymorphisms (SNPs) previously associated with autoimmune diseases. Furthermore, we probed for enrichment within genetic pathways and of transcription factor binding sites and characterized commonalities in variant burden on tissue-specific regulatory sites by calculating the enrichment of allergy SNPs falling in gene regulatory regions in various cells using Encode Roadmap DNase-hypersensitive site data. Finally, we compared the allergy data with those of all known diseases. Among 290 loci previously associated with 16 autoimmune diseases, we found a significant enrichment of loci also associated with allergy (P=1.4e-17) encompassing 29 loci at a false discovery rate of less than 0.05. Such enrichment seemed to be a general characteristic for autoimmune diseases.Among the common loci, 48% had the same direction of effect for allergy and autoimmune diseases. Additionally, we observed an enrichment of allergy SNPs falling within immune pathways and regions of chromatin accessible in immune cells that was also represented in patients with autoimmune diseases but not those with other diseases. We identified shared susceptibility loci and commonalities in pathways between allergy and autoimmune diseases, suggesting shared disease mechanisms. Further studies of these shared genetic mechanisms might help in understanding the complex relationship between these diseases, including the parallel increase in disease prevalence.

The Genetic Architecture of Major Depressive Disorder in Han Chinese Women

Despite the moderate, well-demonstrated heritability of major depressive disorder (MDD), there has been limited success in identifying replicable genetic risk loci, suggesting a complex genetic architecture. Research is needed to quantify the relative contribution of classes of genetic variation across the genome to inform future genetic studies of MDD. To apply aggregate genetic risk methods to clarify the genetic architecture of MDD by estimating and partitioning heritability by chromosome, minor allele frequency, and functional annotations and to test for enrichment of rare deleterious variants. The CONVERGE (China, Oxford, and Virginia Commonwealth University Experimental Research on Genetic Epidemiology) study collected data on 5278 patients with recurrent MDD from 58 provincial mental health centers and psychiatric departments of general medical hospitals in 45 cities and 23 provinces of China. Screened controls (n = 5196) were recruited from a range of locations, including general hospitals and local community centers. Data were collected from August 1, 2008, to October 31, 2012. Genetic risk for liability to recurrent MDD was partitioned using sparse whole-genome sequencing. In aggregate, common single-nucleotide polymorphisms (SNPs) explained between 20% and 29% of the variance in MDD risk, and the heritability in MDD explained by each chromosome was proportional to its length (r = 0.680; P = .0003), supporting a common polygenic etiology. Partitioning heritability by minor allele frequency indicated that the variance explained was distributed across the allelic frequency spectrum, although relatively common SNPs accounted for a disproportionate fraction of risk. Partitioning by genic annotation indicated a greater contribution of SNPs in protein-coding regions and within 3'-UTR regions of genes. Enrichment of SNPs associated with DNase I-hypersensitive sites was also found in many tissue types, including brain tissue. Examining burden scores from singleton exonic SNPs predicted to be deleterious indicated that cases had significantly more mutations than controls (odds ratio, 1.009; 95% CI, 1.003-1.014; P = .003), including those occurring in genes expressed in the brain (odds ratio, 1.011; 95% CI, 1.003-1.018; P = .004) and within nuclear-encoded genes with mitochondrial gene products (odds ratio, 1.075; 95% CI, 1.018-1.135; P = .009). Results support a complex etiology for MDD and highlight the value of analyzing components of heritability to clarify genetic architecture.

Non-invasive prenatal diagnosis of Duchenne and Becker muscular dystrophies by relative haplotype dosage.

ObjectiveDevelopment of an accurate and affordable test for the non‐invasive prenatal diagnosis of Duchenne and Becker muscular dystrophies (DMD/BMD) to implement in clinical practice.MethodCell‐free DNA was extracted from maternal blood and prepared for massively parallel sequencing on an Illumina MiSeq by targeted capture enrichment of single nucleotide polymorphisms (SNPs) across the dystrophin gene on chromosome X. Sequencing data were analysed by relative haplotype dosage.ResultsSeven healthy pregnant donors and two pregnant DMD carriers all bearing a male fetus were recruited through the non‐invasive prenatal diagnosis for single gene disorders study. Non‐invasive prenatal diagnosis testing was conducted by relative haplotype dosage analysis for X‐linked disorders where the genomic DNA from the chorionic villus sampling (for healthy pregnant donors) or from the proband (for pregnant DMD carriers) was used to identify the reference haplotype. Results for all patients showed a test accuracy of 100%, when the calculated fetal fraction was >4% and correlated with known outcomes. A recombination event was also detected in a DMD patient.ConclusionOur new test for NIPD of DMD/BMD has been shown to be accurate and reliable during initial stages of validation. It is also feasible for implementation into clinical service. © 2016 The Authors. Prenatal Diagnosis published by John Wiley & Sons, Ltd.

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