scholarly journals Pleiotropic Loci Associated With Foot Disorders and Common Periparturient Diseases in Holstein Cattle

2021 ◽  
Vol 12 ◽  
Author(s):  
Ellen Lai ◽  
Alexa L. Danner ◽  
Thomas R. Famula ◽  
Anita M. Oberbauer
Keyword(s):  
Genetic Correlation ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
White Line ◽  
Milk Fever ◽  
Genome Wide ◽  
Welfare Issue ◽  
Foot Lesions ◽  
Health Disorders ◽  
Sole Ulcers

Lameness is an animal welfare issue that incurs substantial financial and environmental costs. This condition is commonly caused by digital dermatitis (DD), sole ulcers (SU), and white line disease (WLD). Susceptibility to these three foot disorders is due in part to genetics, indicating that genomic selection against these foot lesions can be used to reduce lameness prevalence. It is unclear whether selection against foot lesions will lead to increased susceptibility to other common diseases such as mastitis and metritis. Thus, the aim of this study was to determine the genetic correlation between causes of lameness and other common health disorders to identify loci contributing to the correlation. Genetic correlation estimates between SU and DD and between SU and WLD were significantly different from zero (p < 0.05), whereas estimates between DD and mastitis, DD and milk fever, and SU and metritis were suggestive (p < 0.1). All five of these genetic correlation estimates were positive. Two-trait genome-wide association studies (GWAS) for each of these five pairs of traits revealed common regions of association on BTA1 and BTA8 for pairs that included DD or SU as one of the traits, respectively. Other regions of association were unique to the pair of traits and not observed in GWAS for other pairs of traits. The positive genetic correlation estimates between foot disorders and other health disorders imply that selection against foot disorders may also decrease susceptibility to other health disorders. Linkage disequilibrium blocks defined around significant and suggestive SNPs from the two-trait GWAS included genes and QTL that were functionally relevant, supporting that these regions included pleiotropic loci.

scholarly journals Cross-cancer genome-wide association study of endometrial cancer and epithelial ovarian cancer identifies genetic risk regions associated with risk of both cancers

2020 ◽  
Author(s):  
Dylan M. Glubb ◽  
Deborah J. Thompson ◽  
Katja K.H. Aben ◽  
Ahmad Alsulimani ◽  
Frederic Amant ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Endometrial Cancer ◽  
Epithelial Ovarian Cancer ◽  
Genetic Correlation ◽  
Target Genes ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Molecular Features ◽  
Genome Wide

AbstractAccumulating evidence suggests a relationship between endometrial cancer and epithelial ovarian cancer. For example, endometrial cancer and epithelial ovarian cancer share epidemiological risk factors and molecular features observed across histotypes are held in common (e.g. serous, endometrioid and clear cell). Independent genome-wide association studies (GWAS) for endometrial cancer and epithelial ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. Using GWAS summary statistics, we explored the shared genetic etiology between endometrial cancer and epithelial ovarian cancer. Genetic correlation analysis using LD Score regression revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10−5). To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e. inverse-variance meta-analysis, co-localization, and M-values), and performed analyses by stratified by subtype. We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10−9). In addition, four novel regions at 7p22.2, 7q22.1, 9p12 and 11q13.3 were identified at a sub-genome wide threshold (P < 5 × 10−7). Integration with promoter-associated HiChIP chromatin loops from immortalized endometrium and epithelial ovarian cell lines, and expression quantitative trait loci (eQTL) data highlighted candidate target genes for further investigation.

scholarly journals Detecting Local Genetic Correlations with Scan Statistics

2019 ◽  
Author(s):  
Hanmin Guo ◽  
James J. Li ◽  
Qiongshi Lu ◽  
Lin Hou
Keyword(s):  
Genetic Correlation ◽  
Association Studies ◽  
Genetic Correlations ◽  
Scan Statistics ◽  
Genome Wide Association Studies ◽  
Scan Statistic ◽  
Multiple Phenotypes ◽  
Genome Wide ◽  
Statistic Approach ◽  
Shared Genetic

AbstractGenetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to five phenotypically distinct but genetically correlated psychiatric disorders, we identified 49 non-overlapping genome regions associated with multiple disorders, including multiple hub regions showing concordant effects on more than two disorders. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

scholarly journals Assessing Causal Relationship Between Human Blood Metabolites and Five Neurodegenerative Diseases With GWAS Summary Statistics

2021 ◽  
Vol 15 ◽  
Author(s):  
Haimiao Chen ◽  
Jiahao Qiao ◽  
Ting Wang ◽  
Zhonghe Shao ◽  
Shuiping Huang ◽  
...  
Keyword(s):  
Neurodegenerative Diseases ◽  
Genetic Correlation ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Metabolic Pathway Analysis ◽  
Genome Wide ◽  
Maximum Likelihood Methods ◽  
Weighted Median ◽  
The Relationship

Background: Neurodegenerative diseases (NDDs) are the leading cause of disability worldwide while their metabolic pathogenesis is unclear. Genome-wide association studies (GWASs) offer an unprecedented opportunity to untangle the relationship between metabolites and NDDs.Methods: By leveraging two-sample Mendelian randomization (MR) approaches and relying on GWASs summary statistics, we here explore the causal association between 486 metabolites and five NDDs including Alzheimer’s Disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson’s disease (PD), and multiple sclerosis (MS). We validated our MR results with extensive sensitive analyses including MR-PRESSO and MR-Egger regression. We also performed linkage disequilibrium score regression (LDSC) and colocalization analyses to distinguish causal metabolite-NDD associations from genetic correlation and LD confounding of shared causal genetic variants. Finally, a metabolic pathway analysis was further conducted to identify potential metabolite pathways.Results: We detected 164 metabolites which were suggestively associated with the risk of NDDs. Particularly, 2-methoxyacetaminophen sulfate substantially affected ALS (OR = 0.971, 95%CIs: 0.961 ∼ 0.982, FDR = 1.04E-4) and FTD (OR = 0.924, 95%CIs: 0.885 ∼ 0.964, FDR = 0.048), and X-11529 (OR = 1.604, 95%CIs: 1.250 ∼ 2.059, FDR = 0.048) and X-13429 (OR = 2.284, 95%CIs: 1.457 ∼ 3.581, FDR = 0.048) significantly impacted FTD. These associations were further confirmed by the weighted median and maximum likelihood methods, with MR-PRESSO and the MR-Egger regression removing the possibility of pleiotropy. We also observed that ALS or FTD can alter the metabolite levels, including ALS and FTD on 2-methoxyacetaminophen sulfate. The LDSC and colocalization analyses showed that none of the identified associations could be driven by genetic correlation or confounding by LD with common causal loci. Multiple metabolic pathways were found to be involved in NDDs, such as “urea cycle” (P = 0.036), “arginine biosynthesis” (P = 0.004) on AD and “phenylalanine, tyrosine and tryptophan biosynthesis” (P = 0.046) on ALS.Conclusion: our study reveals robust bidirectional causal associations between servaral metabolites and neurodegenerative diseases, and provides a novel insight into metabolic mechanism for pathogenesis and therapeutic strategies of these diseases.

scholarly journals Genome-wide association analysis reveals extensive genetic overlap between mood instability and psychiatric disorders but divergent patterns of genetic effects

2021 ◽  
Author(s):  
Guy Hindley ◽  
Kevin S O'Connell ◽  
Zillur Rahman ◽  
Oleksandr Frei ◽  
Shahram Bahrami ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Correlation ◽  
Association Studies ◽  
Single Gene ◽  
Genetic Effects ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Determinants ◽  
Mood Instability ◽  
Genome Wide

Mood instability (MOOD) is a transdiagnostic phenomenon with a prominent neurobiological basis. Recent genome-wide association studies found significant positive genetic correlation between MOOD and major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. Summary statistics for schizophrenia (SCZ, n=105,318), bipolar disorder (BIP, n=413,466), DEP (n=450,619), attention-deficit hyperactivity disorder (ADHD, n=53,293) and MOOD (n=363,705), were analysed using the bivariate causal mixture model and conjunctional false discovery rate methods to estimate the proportion of shared variants influencing MOOD and each disorder, and identify jointly associated genomic loci. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (rg=0.10-0.62). Of 10.4K genomic variants influencing MOOD, 4K-9.4K were estimated to influence psychiatric disorders. MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25, with consistent genetic effects in independent samples. Fifty-three jointly associated loci were overlapping across two or more disorders (transdiagnostic), seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene-set, synapse organization. The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions of shared loci suggest divergent effects on corresponding neurobiological mechanisms which may relate to differences in the core clinical features of each disorder.

scholarly journals Coinherited genetics of multiple myeloma and its precursor, monoclonal gammopathy of undetermined significance

2020 ◽  
Vol 4 (12) ◽  
pp. 2789-2797
Author(s):  
Alyssa I. Clay-Gilmour ◽  
Michelle A. T. Hildebrandt ◽  
Elizabeth E. Brown ◽  
Jonathan N. Hofmann ◽  
John J. Spinelli ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Genetic Susceptibility ◽  
Genetic Correlation ◽  
Monoclonal Gammopathy ◽  
Association Studies ◽  
Continuous Measure ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Undetermined Significance

Abstract So far, 23 germline susceptibility loci have been associated with multiple myeloma (MM) risk. It is unclear whether the genetic variation associated with MM susceptibility also predisposes to its precursor, monoclonal gammopathy of undetermined significance (MGUS). Leveraging 2434 MM cases, 754 MGUS cases, and 2 independent sets of controls (2567/879), we investigated potential shared genetic susceptibility of MM and MGUS by (1) performing MM and MGUS genome-wide association studies (GWAS); (2) validating the association of a polygenic risk score (PRS) based on 23 established MM loci (MM-PRS) with risk of MM, and for the first time with MGUS; and (3) examining genetic correlation of MM and MGUS. Heritability and genetic estimates yielded 17% (standard error [SE] ±0.04) and 15% (SE ±0.11) for MM and MGUS risk, respectively, and a 55% (SE ±0.30) genetic correlation. The MM-PRS was associated with risk of MM when assessed continuously (odds ratio [OR], 1.17 per SD; 95% confidence interval [CI], 1.13-1.21) or categorically (OR, 1.70; 95% CI, 1.38-2.09 for highest; OR, 0.71; 95% CI, 0.55-0.90 for lowest compared with middle quintile). The MM-PRS was similarly associated with MGUS (OR, 1.19 per SD; 95% CI, 1.14-1.26 as a continuous measure, OR, 1.77, 95%CI: 1.29-2.43 for highest and OR, 0.70, 95%CI: 0.50-0.98 for lowest compared with middle quintile). MM and MGUS associations did not differ by age, sex, or MM immunoglobulin isotype. We validated a 23-SNP MM-PRS in an independent series of MM cases and provide evidence for its association with MGUS. Our results suggest shared common genetic susceptibility to MM and MGUS.

scholarly journals Shared Genetic Risk between Eating Disorder- and Substance-Use-Related Phenotypes: Evidence from Genome-Wide Association Studies

2019 ◽  
Author(s):  
Melissa A. Munn-Chernoff ◽  
Emma C. Johnson ◽  
Yi-Ling Chou ◽  
Jonathan R.I. Coleman ◽  
Laura M. Thornton ◽  
...  
Keyword(s):  
Substance Use ◽  
Eating Disorder ◽  
Binge Eating ◽  
Genetic Correlation ◽  
Association Studies ◽  
Genetic Correlations ◽  
Smoking Initiation ◽  
Genome Wide Association Studies ◽  
Current Smoking ◽  
Genome Wide

AbstractEating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa (BN) and problem alcohol use (genetic correlation [rg], twin-based=0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge-eating, AN without binge-eating, and a BN factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder (MDD). Total sample sizes per phenotype ranged from ~2,400 to ~537,000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg=0.18; false discovery rate q=0.0006), cannabis initiation and AN (rg=0.23; q<0.0001), and cannabis initiation and AN with binge-eating (rg=0.27; q=0.0016). Conversely, significant negative genetic correlations were observed between three non-diagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge-eating (rgs=-0.19 to −0.23; qs<0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for MDD loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships between these behaviors.

scholarly journals The putative causal effect of type 2 diabetes in risk of cataract: a Mendelian randomization study in East Asian

2021 ◽  
Author(s):  
Haoyang Zhang ◽  
Xuehao Xiu ◽  
Angli Xue ◽  
Yuedong Yang ◽  
Yuanhao Yang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Correlation ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Population Based ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractBackgroundThe epidemiological association between type 2 diabetes and cataract has been well-established. However, it remains unclear whether the two diseases share a genetic basis, and if so, whether this reflects a causal relationship.MethodsWe utilized East Asian population-based genome-wide association studies (GWAS) summary statistics of type 2 diabetes (Ncase=36,614, Ncontrol=155,150) and cataract (Ncase=24,622, Ncontrol=187,831) to comprehensively investigate the shared genetics between the two diseases. We performed 1. linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) to estimate the genetic correlation and local genetic correlation between type 2 diabetes and cataract; 2. multiple Mendelian randomization (MR) analyses to infer the putative causality between type 2 diabetes and cataract; and 3. Summary-data-based Mendelian randomization (SMR) to identify candidate risk genes underling the causality.ResultsWe observed a strong genetic correlation (rg=0.58; p-value=5.60×10−6) between type 2 diabetes and cataract. Both ρ-HESS and multiple MR methods consistently showed a putative causal effect of type 2 diabetes on cataract, with estimated liability-scale MR odds ratios (ORs) at around 1.10 (95% confidence interval [CI] ranging from 1.06 to 1.17). In contrast, no evidence supports a causal effect of cataract on type 2 diabetes. SMR analysis identified two novel genes MIR4453HG (βSMR=−0.34, p-value=6.41×10−8) and KCNK17 (βSMR=−0.07, p-value=2.49×10−10), whose expression levels were likely involved in the putative causality of type 2 diabetes on cataract.ConclusionsOur results provided robust evidence supporting a causal effect of type 2 diabetes on the risk of cataract in East Asians, and posed new paths on guiding prevention and early-stage diagnosis of cataract in type 2 diabetes patients.Key MessagesWe utilized genome-wide association studies of type 2 diabetes and cataract in a large Japanese population-based cohort and find a strong genetic overlap underlying the two diseases.We performed multiple Mendelian randomization models and consistently disclosed a putative causal effect of type 2 diabetes on the development of cataract.We revealed two candidate genes MIR4453HG and KCNK17 whose expression levelss are likely relevant to the causality between type 2 diabetes and cataract.Our study provided theoretical fundament at the genetic level for improving early diagnosis, prevention and treatment of cataract in type 2 diabetes patients in clinical practice

scholarly journals Detecting local genetic correlations with scan statistics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hanmin Guo ◽  
James J. Li ◽  
Qiongshi Lu ◽  
Lin Hou
Keyword(s):  
Genetic Correlation ◽  
Association Studies ◽  
Genetic Correlations ◽  
Scan Statistics ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Scan Statistic ◽  
Multiple Phenotypes ◽  
Genome Wide ◽  
Statistic Approach

AbstractGenetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to seven phenotypically distinct but genetically correlated neuropsychiatric traits, we identify 227 non-overlapping genome regions associated with multiple traits, including multiple hub regions showing concordant effects on five or more traits. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

scholarly journals Integrating gene expression with summary association statistics to identify susceptibility genes for 30 complex traits

2016 ◽  
Author(s):  
Nicholas Mancuso ◽  
Huwenbo Shi ◽  
Pagé Goddard ◽  
Gleb Kichaev ◽  
Alexander Gusev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Correlation ◽  
Complex Traits ◽  
Association Studies ◽  
Susceptibility Genes ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Causal Variants

AbstractAlthough genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. We leverage recently introduced methods to integrate gene expression measurements from 45 expression panels with summary GWAS data to perform 30 transcriptome-wide association studies (TWASs). We identify 1,196 susceptibility genes whose expression is associated with these traits; of these, 168 reside more than 0.5Mb away from any previously reported GWAS significant variant, thus providing new risk loci. Second, we find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, 8 are not found through genetic correlation at the SNP level. Third, we use bi-directional regression to find evidence for BMI causally influencing triglyceride levels, and triglyceride levels causally influencing LDL. Taken together, our results provide insights into the role of expression to susceptibility of complex traits and diseases.

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