Genome-wide Summary Statistics Research Articles

Evidence for genetic contribution to the increased risk of type 2 diabetes in schizophrenia

The epidemiologic link between schizophrenia (SCZ) and type 2 diabetes (T2D) remains poorly understood. Here, we investigate the presence and extent of a shared genetic background between SCZ and T2D using genome-wide approaches. We performed a genome-wide association study (GWAS) and polygenic risk score analysis in a Greek sample collection (GOMAP) comprising three patient groups: SCZ only (n = 924), T2D only (n = 822), comorbid SCZ and T2D (n = 505); samples from two separate Greek cohorts were used as population-based controls (n = 1,125). We used genome-wide summary statistics from two large-scale GWAS of SCZ and T2D from the PGC and DIAGRAM consortia, respectively, to perform genetic overlap analyses, including a regional colocalisation test. We show for the first time that patients with comorbid SCZ and T2D have a higher genetic predisposition to both disorders compared to controls. We identify five genomic regions with evidence of colocalising SCZ and T2D signals, three of which contain known loci for both diseases. We also observe a significant excess of shared association signals between SCZ and T2D at nine out of ten investigated p value thresholds. Finally, we identify 29 genes associated with both T2D and SCZ, several of which have been implicated in biological processes relevant to these disorders. Together our results demonstrate that the observed comorbidity between SCZ and T2D is at least in part due to shared genetic mechanisms.

Exploring the Genetic Correlation Between Growth and Immunity Based on Summary Statistics of Genome-Wide Association Studies.

The relationship between growth and immune phenotypes has been presented in the context of physiology and energy allocation theory, but has rarely been explained genetically in humans. As more summary statistics of genome-wide association studies (GWAS) become available, it is increasingly possible to explore the genetic relationship between traits at the level of genome-wide summary statistics. In this study, publicly available summary statistics of growth and immune related traits were used to evaluate the genetic correlation coefficients between immune and growth traits, as well as the cause and effect relationship between them. In addition, pleiotropic variants and KEGG pathways were identified. As a result, we found negative correlations between birthweight and immune cell count phenotypes, a positive correlation between childhood head circumference and eosinophil counts (EO), and positive or negative correlations between childhood body mass index and immune phenotypes. Statistically significant negative effects of immune cell count phenotypes on human height, and a slight but significant negative influence of human height on allergic disease were also observed. A total of 98 genomic regions were identified as containing variants potentially related to both immunity and growth. Some variants, such as rs3184504 located in SH2B3, rs13107325 in SLC39A8, and rs1260326 located in GCKR, which have been identified to be pleiotropic SNPs among other traits, were found to also be related to growth and immune traits in this study. Meanwhile, the most frequent overlapping KEGG pathways between growth and immune phenotypes were autoimmune related pathways. Pleiotropic pathways such as the adipocytokine signaling pathway and JAK-STAT signaling pathway were also identified to be significant. The results of this study indicate the complex genetic relationship between growth and immune phenotypes, and reveal the genetic background of their correlation in the context of pleiotropy.

Shared Genetic Influence on Early Growth and Type 2 Diabetes in Adulthood

Background: There is a strong evidence of association between birthweight (BW) and type 2 diabetes (T2D) in adulthood. Although shared genetics has been suggested to be a significant contributor to these associations, to date, we know very few shared genetic loci. We conducted a systematic genome-wide investigation of shared genetic effects (pleiotropy) and enrichment of biologically functional loci implicated in BW-T2D associations. Methods: We evaluated pleiotropy, enrichment of functional annotation, and association of single nucleotide polymorphisms (SNPs) using genome-wide summary statistics of 2,893,797 SNPs for BW from the Early Growth Genetics Consortium, and T2D from the DIAbetes Genetics Replication And Meta-analysis Consortium. Analyses were conducted using the Genetic analysis incorporating Pleiotropy and Annotation (GPA). Tests for enrichment of annotations from functional datasets were performed in loci associated with pairs of traits. Results: We found significant enrichment of genetic pleiotropy (P <10-30) and biologically functional loci jointly associated with BW and T2D (enrichment fold of shared vs. BW-specific vs. T2D-specific loci: 1.45 vs. 1.24 vs. 0.57; P=1.73x10-21). A total of 178 independent SNPs in genes NYAP2-IRS1, ADCY5, ST6GAL1, CDKAL1, ANK1, HHEX-ECOC6, FAT3-MTNR1B, and MPHOSPH9 were associated with both BW and T2D at a false discovery rate of 5%. The locus at NYAP2-IRS1 (rs1515098 C) associated with reduced BW and increased T2D risk was novel. Functional analysis showed that rs62188784, in strong LD with rs1515098 (r2=0.91), has potential regulatory (cis-eQTL) effect on expression of the IRS1 gene in adipose tissues. Conclusions: We found shared genetic loci that influence early growth and type 2 diabetes, including novel shared variant in the insulin receptor substrate 1 gene. Findings suggest that genetic regulation of insulin signaling in adipose tissue potentially influences the associations of early growth and type 2 diabetes later in life. Disclosure F. Tekola-Ayele: None. A. Lee: None. T. Workalemahu: None. D. Shrestha: None.

Abstract TP161: The Cerebrovascular Disease Knowledge Portal: an Open Access Data Resource to Accelerate Genomic Discoveries in Stroke

Introduction: High throughput genotyping technologies and large collaborative consortia have revolutionized the field of medical genetics. Open data access is the final barrier capitalizing fully on the opportunities available in stroke genetics research. The International Stroke Genetics Consortium (ISGC) has created the Cerebrovascular Disease Knowledge Portal (CDKP), a platform to explore and access genetic data related to cerebrovascular diseases. Methods: Funded by the NIH, the CDKP has been developed by the ISGC and the American Heart Association (AHA) Institute for Precision Cardiovascular Medicine. The CDKP seeks to democratize access to genomic data and potentiate stroke genomics research by providing open access to genetic, phenotypic and imaging data on patients with stroke. Within the CDKP, data are aggregated, integrated, and harmonized according to a pre-specified standardized pipeline. Any institution or investigator working with stroke genomic data is welcome to deposit their data or use available data. Results: The CDKP houses two types of data, for different regulatory and analytical needs: summary level data and individual level data. The CDKP offers three main features: (1) a web-based graphical user interphase that allows the exploration of genomic information through a wide menu of integrated tools for analysis and data visualization; (2) a repository of full sets of genome-wide summary statistics produced by published landmark studies in the field; and (3) a repository of individual level data, accessible through a secure cloud working space provided by the AHA Platform for Precision Medicine. The CDKP can be accessed at www.cerebrovascularportal.org . Conclusion: The CDKP advances the ISGC’s goal of liberal data sharing in stroke genomics and other areas of cardiovascular research that may benefit from genomic analyses. In the future, phenotypic datasets can be added to further enrich sharing of non-genetic data as well.

Shared genetic influences between dimensional ASD and ADHD symptoms during child and adolescent development

BackgroundShared genetic influences between attention-deficit/hyperactivity disorder (ADHD) symptoms and autism spectrum disorder (ASD) symptoms have been reported. Cross-trait genetic relationships are, however, subject to dynamic changes during development. We investigated the continuity of genetic overlap between ASD and ADHD symptoms in a general population sample during childhood and adolescence. We also studied uni- and cross-dimensional trait-disorder links with respect to genetic ADHD and ASD risk.MethodsSocial-communication difficulties (N ≤ 5551, Social and Communication Disorders Checklist, SCDC) and combined hyperactive-impulsive/inattentive ADHD symptoms (N ≤ 5678, Strengths and Difficulties Questionnaire, SDQ-ADHD) were repeatedly measured in a UK birth cohort (ALSPAC, age 7 to 17 years). Genome-wide summary statistics on clinical ASD (5305 cases; 5305 pseudo-controls) and ADHD (4163 cases; 12,040 controls/pseudo-controls) were available from the Psychiatric Genomics Consortium. Genetic trait variances and genetic overlap between phenotypes were estimated using genome-wide data.ResultsIn the general population, genetic influences for SCDC and SDQ-ADHD scores were shared throughout development. Genetic correlations across traits reached a similar strength and magnitude (cross-trait rg ≤ 1, pmin = 3 × 10−4) as those between repeated measures of the same trait (within-trait rg ≤ 0.94, pmin = 7 × 10−4). Shared genetic influences between traits, especially during later adolescence, may implicate variants in K-RAS signalling upregulated genes (p-meta = 6.4 × 10−4).Uni-dimensionally, each population-based trait mapped to the expected behavioural continuum: risk-increasing alleles for clinical ADHD were persistently associated with SDQ-ADHD scores throughout development (marginal regression R2 = 0.084%). An age-specific genetic overlap between clinical ASD and social-communication difficulties during childhood was also shown, as per previous reports. Cross-dimensionally, however, neither SCDC nor SDQ-ADHD scores were linked to genetic risk for disorder.ConclusionsIn the general population, genetic aetiologies between social-communication difficulties and ADHD symptoms are shared throughout child and adolescent development and may implicate similar biological pathways that co-vary during development. Within both the ASD and the ADHD dimension, population-based traits are also linked to clinical disorder, although much larger clinical discovery samples are required to reliably detect cross-dimensional trait-disorder relationships.

An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics.

We study the problem of testing for single marker-multiple phenotype associations based on genome-wide association study (GWAS) summary statistics without access to individual-level genotype and phenotype data. For most published GWASs, because obtaining summary data is substantially easier than accessing individual-level phenotype and genotype data, while often multiple correlated traits have been collected, the problem studied here has become increasingly important. We propose a powerful adaptive test and compare its performance with some existing tests. We illustrate its applications to analyses of a meta-analyzed GWAS dataset with three blood lipid traits and another with sex-stratified anthropometric traits, and further demonstrate its potential power gain over some existing methods through realistic simulation studies. We start from the situation with only one set of (possibly meta-analyzed) genome-wide summary statistics, then extend the method to meta-analysis of multiple sets of genome-wide summary statistics, each from one GWAS. We expect the proposed test to be useful in practice as more powerful than or complementary to existing methods.