Determining the stability of genome-wide factors in BMI between ages 40 to 69 years.

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of genetic variation influencing BMI from midlife and beyond is unknown. By analyzing BMI data collected from 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 73 years. We then applied genomic structural equation modeling (gSEM) to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin variation in BMI from middle to early old age in men and women alike.

Download Full-text

Insights into the genetic basis of retinal detachment

Human Molecular Genetics ◽

10.1093/hmg/ddz294 ◽

2019 ◽

Vol 29 (4) ◽

pp. 689-702 ◽

Cited By ~ 2

Author(s):

Thibaud S Boutin ◽

David G Charteris ◽

Aman Chandra ◽

Susan Campbell ◽

Caroline Hayward ◽

...

Keyword(s):

Retinal Detachment ◽

Association Studies ◽

Genetic Correlations ◽

Self Report ◽

Cataract Operation ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Associations ◽

Data Set ◽

Genome Wide

Abstract Retinal detachment (RD) is a serious and common condition, but genetic studies to date have been hampered by the small size of the assembled cohorts. In the UK Biobank data set, where RD was ascertained by self-report or hospital records, genetic correlations between RD and high myopia or cataract operation were, respectively, 0.46 (SE = 0.08) and 0.44 (SE = 0.07). These correlations are consistent with known epidemiological associations. Through meta-analysis of genome-wide association studies using UK Biobank RD cases (N = 3 977) and two cohorts, each comprising ~1 000 clinically ascertained rhegmatogenous RD patients, we uncovered 11 genome-wide significant association signals. These are near or within ZC3H11B, BMP3, COL22A1, DLG5, PLCE1, EFEMP2, TYR, FAT3, TRIM29, COL2A1 and LOXL1. Replication in the 23andMe data set, where RD is self-reported by participants, firmly establishes six RD risk loci: FAT3, COL22A1, TYR, BMP3, ZC3H11B and PLCE1. Based on the genetic associations with eye traits described to date, the first two specifically impact risk of a RD, whereas the last four point to shared aetiologies with macular condition, myopia and glaucoma. Fine-mapping prioritized the lead common missense variant (TYR S192Y) as causal variant at the TYR locus and a small set of credible causal variants at the FAT3 locus. The larger study size presented here, enabled by resources linked to health records or self-report, provides novel insights into RD aetiology and underlying pathological pathways.

Download Full-text

Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

Twin Research and Human Genetics ◽

10.1017/thg.2018.12 ◽

2018 ◽

Vol 21 (2) ◽

pp. 84-88 ◽

Cited By ~ 6

Author(s):

W. David Hill

Keyword(s):

Genetic Information ◽

Drug Targets ◽

Large Scale ◽

Association Studies ◽

Meta Analysis ◽

Genetic Correlations ◽

Data Sets ◽

Genome Wide Association Studies ◽

Nootropic Drug ◽

Genome Wide

Intelligence and educational attainment are strongly genetically correlated. This relationship can be exploited by Multi-Trait Analysis of GWAS (MTAG) to add power to Genome-wide Association Studies (GWAS) of intelligence. MTAG allows the user to meta-analyze GWASs of different phenotypes, based on their genetic correlations, to identify association's specific to the trait of choice. An MTAG analysis using GWAS data sets on intelligence and education was conducted by Lam et al. (2017). Lam et al. (2017) reported 70 loci that they described as ‘trait specific’ to intelligence. This article examines whether the analysis conducted by Lam et al. (2017) has resulted in genetic information about a phenotype that is more similar to education than intelligence.

Download Full-text

Analysis of shared heritability in common disorders of the brain

Science ◽

10.1126/science.aap8757 ◽

2018 ◽

Vol 360 (6395) ◽

pp. eaap8757 ◽

Cited By ~ 362

Author(s):

◽

Verneri Anttila ◽

Brendan Bulik-Sullivan ◽

Hilary K. Finucane ◽

Raymond K. Walters ◽

...

Keyword(s):

Psychiatric Disorders ◽

Neurological Disorders ◽

Statistical Power ◽

Association Studies ◽

Genetic Correlations ◽

Genome Wide Association Studies ◽

Brain Disorders ◽

Cognitive Measures ◽

Genome Wide ◽

The Brain

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

Download Full-text

A combined analysis of genetically correlated traits identifies 107 loci associated with intelligence

10.1101/160291 ◽

2017 ◽

Cited By ~ 5

Author(s):

W. D. Hill ◽

G. Davies ◽

A. M. McIntosh ◽

C. R. Gale ◽

I. J. Deary

Keyword(s):

Household Income ◽

Association Studies ◽

Meta Analysis ◽

Genetic Correlations ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Correlated Traits ◽

Single Measure ◽

Combined Analysis ◽

Genome Wide

AbstractIntelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including many physical and mental health variables. Both education and household income are strongly genetically correlated with intelligence, at rg =0.73 and rg =0.70 respectively. This allowed us to utilize a novel approach, Multi-Trait Analysis of Genome-wide association studies (MTAG; Turley et al. 2017), to combine two large genome-wide association studies (GWASs) of education and household income to increase power in the largest GWAS on intelligence so far (Sniekers et al. 2017). This study had four goals: firstly, to facilitate the discovery of new genetic loci associated with intelligence; secondly, to add to our understanding of the biology of intelligence differences; thirdly, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predict phenotypic intelligence variance in an independent sample. We apply MTAG to three large GWAS: Sniekers et al (2017) on intelligence, Okbay et al. (2016) on Educational attainment, and Hill et al. (2016) on household income. By combining these three samples our functional sample size increased from 78 308 participants to 147 194. We found 107 independent loci associated with intelligence, implicating 233 genes, using both SNP-based and gene-based GWAS. We find evidence that neurogenesis may explain some of the biological differences in intelligence as well as genes expressed in the synapse and those involved in the regulation of the nervous system. We show that the results of our combined analysis demonstrate the same pattern of genetic correlations as a single measure/the simple measure of intelligence, providing support for the meta-analysis of these genetically-related phenotypes. We find that our MTAG meta-analysis of intelligence shows similar genetic correlations to 26 other phenotypes when compared with a GWAS consisting solely of cognitive tests. Finally, using an independent sample of 6 844 individuals we were able to predict 7% of intelligence using SNP data alone.

Download Full-text

Variants in MARC1 and HSD17B13 reduce severity of NAFLD in children, perturb phospholipid metabolism, and suppress fibrotic pathways

10.1101/2020.06.05.20120956 ◽

2020 ◽

Author(s):

Christian A. Hudert ◽

Anna Alisi ◽

Quentin M. Anstee ◽

Annalisa Crudele ◽

Laura G. Draijer ◽

...

Keyword(s):

Metal Binding ◽

Association Studies ◽

Hepatic Metabolism ◽

Phospholipid Metabolism ◽

Lower Grade ◽

Genome Wide Association Studies ◽

Protective Allele ◽

Genome Wide ◽

Genetic Mechanisms ◽

The Stability

AbstractBackground & aimsGenome-wide association studies in adults have identified variants in HSD17B13 and MARC1 as protective against NAFLD. It is not known if they are similarly protective in children and, more generally, whether the peri-portal inflammation of pediatric NAFLD and lobular inflammation seen in adults share common genetic influences. Therefore, we aimed to: establish if these variants are associated with NAFLD in children, and to investigate the function of these variants in hepatic metabolism using metabolomics.Methods960 children (590 with NAFLD, 394 with liver histology) were genotyped for rs72613567T>TA in HSD17B13, rs2642438G>A in MARC1. Genotype-histology associations were tested using ordinal regression. Untargeted hepatic proteomics and plasma lipidomics were performed in a subset of samples. In silico tools were used to model the effect of rs2642438G>A (p.Ala165Thr) on MARC1.Resultsrs72613567T>TA in HSD17B13 was associated with lower odds of NAFLD diagnosis (OR 0.7 (95%CI 0.6-0.9) and lower grade of portal inflammation (P<0.001) whilst rs2642438G>A in MARC1 was associated with lower grade of hepatic steatosis (P=0.02). Proteomics found reduced expression of HSD17B13 in carriers of the protective allele, whereas MARC1 levels were not affected by genotype. Both variants showed downregulation of hepatic fibrotic pathways, upregulation of retinol metabolism and perturbation of phospholipid species. Modelling suggests that p.Ala165Thr would disrupt the stability and metal-binding of MARC1.ConclusionsThere are shared genetic mechanisms between pediatric and adult NAFLD, despite their differences in histology. MARC1 and HSD17B13 are involved in phospholipid metabolism and suppress fibrosis in NAFLD.

Download Full-text

Detecting Local Genetic Correlations with Scan Statistics

10.1101/808519 ◽

2019 ◽

Cited By ~ 3

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.

Download Full-text

Joint Genome-Wide Association Study Identifies Twenty-One Novel Loci for Age at Menarche and Highlights Its Causal Association with Other Complex Diseases

10.21203/rs.3.rs-955340/v1 ◽

2021 ◽

Author(s):

Gui-Juan Feng ◽

Qian Xu ◽

Jing-Jing Ni ◽

Shan-Shan Yang ◽

Bai-Xue Han ◽

...

Keyword(s):

Association Study ◽

Complex Traits ◽

Causal Effect ◽

Association Studies ◽

Genetic Correlations ◽

Genome Wide Association ◽

Age At Menarche ◽

Luteinizing Hormone Level ◽

Genome Wide Association Studies ◽

Genome Wide

Abstract Age at menarche (AAM) is a sign of puberty of females. It is a heritable trait associated with various adult diseases. However, the genetic mechanism that determines AAM and links it to disease risk is poorly understood. Aiming to uncover the genetic basis for AAM, we conducted a joint association study in up to 438,089 participants from 3 genome-wide association studies of European and East Asian ancestries. Twenty-one novel genomic loci were identified at the genome-wide significance level. Besides, we observed significant genetic correlations between AAM and 67 complex traits, and the highest genetic correlation was observed between AAM and body mass index (rg=-0.19, P=6.11×10−31). Latent causal variable analyses demonstrate that there is a genetically causal effect of AAM on high blood pressure (GCP=0.47, P=0.02), forced vital capacity (GCP=0.63, P=0.02), age at first live birth (GCP=0.51, P=0.03), impedance of right arm (GCP=0.41, P<1×10-7) and right leg fat percentage (GCP=-0.10, P=0.02), etc. Enrichment analysis identified 5 enriched tissues and 51 enriched gene sets. Four of the five enriched tissues were related to the nervous system, including the hypothalamus middle, hypothalamo hypophyseal system, neurosecretory systems and hypothalamus. The fifth tissue was the retina in the sensory organ. The most significant gene set was the ‘decreased circulating luteinizing hormone level’ (P=2.45×10-6). Our findings may provide useful insights that elucidate the mechanisms determining AAM and the genetic interplay between AAM and some traits of women.

Download Full-text

Geographic Confounding in Genome-Wide Association Studies

10.21203/rs.3.rs-362358/v1 ◽

2021 ◽

Author(s):

Abdel Abdellaoui ◽

Karin Verweij ◽

Michel G Nivard

Keyword(s):

Educational Attainment ◽

Social Stratification ◽

Complex Traits ◽

Association Studies ◽

Genetic Correlations ◽

Geographic Region ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Gene Environment ◽

Genome Wide

Abstract Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N = 69,772–271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

Download Full-text

Multi-breed Genetic Parameters and Genome-wide Association Studies for Mortality Rate at Birth in Pigs

10.21203/rs.3.rs-146253/v1 ◽

2021 ◽

Author(s):

Meijing An ◽

Guangliang Zhou ◽

Yang Li ◽

Tao Xiang ◽

Yunlong Ma ◽

...

Keyword(s):

Mortality Rate ◽

Genetic Parameters ◽

Association Studies ◽

Genetic Correlations ◽

Complex Trait ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Gene Expressions ◽

Genome Wide ◽

Piglet Mortality

Abstract Background Piglet mortality is an economically important complex trait that impacts sow prolificacy in the pig industry. The genetic parameters estimations and genome-wide association studies will help us to better understand the genetic fundamentals of piglet mortality. However, compared with other economically important traits, a little breakthrough in the genetic analyses of the trait has been achieved. Results In this study, we used multi-breed data sets from Yorkshire, Landrace, and Duroc sows and characterized the genetic and genomic properties of mortality rate at birth by treating each parity as a different trait. The heritability of mortality rate from parity I to III were estimated to be 0.0630, 0.1031, and 0.1140, respectively. The phenotypic and genetic correlations with its component traits were all positive with ranges from 0.0897 to 0.9054, and 0.2388 to 0.9999, respectively. Integrating the results, we identified 21 loci that were detected at least by two tools from standard MLM, FarmCPU, BLINK and mrMLM, and these loci were annotated to 22 genes. The annotations revealed that the gene expressions were associated with the reproductive system, nervous system, digestive system, and embryonic development, which are reasonably related to the piglet mortality. Conclusions In brief, the genetic properties of piglet mortality at birth were reported. These findings are expected to provide much information for understanding the genetic and genomic fundamentals of farrowing mortality and also identify candidate molecular markers for breeding practice.

Download Full-text

Recent advances in the genetic association between osteoporotic fracture and sarcopenia

Aging Pathobiology and Therapeutics ◽

10.31491/apt.2021.03.049 ◽

2021 ◽

Vol 3 (1) ◽

pp. 02-09

Author(s):

Qiaocong Chen ◽

◽

Huiling Lou ◽

Cheng Peng

Keyword(s):

Fracture Risk ◽

Osteoporotic Fracture ◽

Musculoskeletal Diseases ◽

Association Studies ◽

Genetic Correlations ◽

Osteoporotic Fractures ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Determinants ◽

Genome Wide

The risk of osteoporotic fracture can be viewed as a function of loading conditions and the ability of the bone to withstand the load. Skeletal loads are dominated by muscle action. Recently, it has become clear that bone and muscle share genetic determinants. Involvement of the musculoskeletal system manifests as bone loss (osteoporosis) and muscle wasting (sarcopenia). There is clinical evidence that osteoporotic fractures are significantly associated with sarcopenia, and sarcopenia may be a potential predictive factor for fracture risk, which suggests that there may be shared genetic determinants between sarcopenia and osteoporotic fracture. In recent years, genome-wide association studies (GWASs) studies have found that both lean mass and hand grip strength are associated with fracture risk, which may provide a possible endophenotype for elucidating the potential genetic study of fracture risk. Our effort to understand the clinical and genetic correlations between osteoporotic fracture and sarcopenia is helpful to understand the interaction between muscle and bone, and to study the etiology of complex musculoskeletal diseases. Identifying potentially important genetic variations in bone and muscle, measuring these variations using state-of-the-art technology, and replicating these experiments in humans and large animals will provide potential drug or intervention targets for osteoporotic fracture valuable in the future. Keywords: Genetics, osteoporosis, fracture, sarcopenia, genome-wide association studies, single nucleotide polymorphism

Download Full-text