A polygenic p factor for major psychiatric disorders

Mapping Intimacies ◽

10.1101/287987 ◽

2018 ◽

Author(s):

Saskia Selzam ◽

Jonathan R. I. Coleman ◽

Avshalom Caspi ◽

Terrie E. Moffitt ◽

Robert Plomin

Keyword(s):

Psychiatric Disorders ◽

Genetic Architecture ◽

Genetic Correlations ◽

Genetic Research ◽

Strong Support ◽

Principal Component ◽

Complex Trait ◽

Genome Wide ◽

P Factor ◽

First Time

AbstractIt has recently been proposed that a single dimension, called the p factor, can capture a person’s liability to mental disorder. Relevant to the p hypothesis, recent genetic research has found surprisingly high genetic correlations between pairs of psychiatric disorders. Here, for the first time we compare genetic correlations from different methods and examine their support for a genetic p factor. We tested the hypothesis of a genetic p factor by using principal component analysis on matrices of genetic correlations between major psychiatric disorders estimated by three methods – family study, Genome-wide Complex Trait Analysis, and Linkage-Disequilibrium Score Regression – and on a matrix of polygenic score correlations constructed for each individual in a UK-representative sample of 7,026 unrelated individuals. All disorders loaded on a first unrotated principal component, which accounted for 57%, 43%, 34% and 19% of the variance respectively for each method. Our results showed that all four methods provided strong support for a genetic p factor that represents the pinnacle of the hierarchical genetic architecture of psychopathology.

The Genetic Architecture of Amygdala Nuclei

10.1101/2021.06.30.21258615 ◽

2021 ◽

Author(s):

Mary S Mufford ◽

Dennis van der Meer ◽

Tobias Kaufmann ◽

Oleksandr Frei ◽

Raj Ramesar ◽

...

Keyword(s):

Statistical Power ◽

Genetic Architecture ◽

Brain Mri ◽

Genetic Correlations ◽

Complex Trait ◽

Central Nucleus ◽

Association Analyses ◽

Genome Wide ◽

Amygdala Volume ◽

Shared Genetic

Background: Whereas a number of genetic variants influencing total amygdala volume have been identified in previous research, genetic architecture of its distinct nuclei have yet to be thoroughly explored. We aimed to investigate whether increased phenotypic specificity through segmentation of the nuclei aids genetic discoverability and sheds light on the extent of shared genetic architecture and biological pathways between the nuclei and disorders associated with the amygdala. Methods: T1-weighted brain MRI scans (n=36,352, mean age= 64.26 years, 52% female) of trans-ancestry individuals from the UK Biobank were segmented into nine amygdala nuclei with FreeSurfer v6.1, and genome-wide association analyses were performed on the full sample and a European-only subset (n=31,690). We estimated heritability using Genome-wide Complex Trait Analysis, derived estimates of polygenicity, discoverability and power using MiXer, and identified genetic correlations and shared loci with psychiatric disorders using Linkage Disequilibrium Score Regression and conjunctional FDR, followed by functional annotation using FUMA. Results: The SNP-based heritability of the nuclei ranged between 0.17-0.33, and the central nucleus had the greatest statistical power for discovery. Across the whole amygdala and the nuclei volumes, 38 novel significant (p < 5x10-9) loci were identified, with most loci mapped to the central nucleus. The mapped genes and associated pathways revealed both unique and shared effects across the nuclei, and immune-related pathways were particularly enriched across several nuclei. Conclusions: These findings indicate that the amygdala nuclei volumes have significant genetic heritability, increased power for discovery compared to whole amygdala volume, may have unique and shared genetic architectures, and a significant immune component to their aetiology.

Assessing the evidence for shared genetic risks across psychiatric disorders and traits

Psychological Medicine ◽

10.1017/s0033291717003440 ◽

2017 ◽

Vol 48 (11) ◽

pp. 1759-1774 ◽

Cited By ~ 40

Author(s):

Joanna Martin ◽

Mark J. Taylor ◽

Paul Lichtenstein

Keyword(s):

Psychiatric Disorders ◽

Genetic Architecture ◽

Molecular Genetic ◽

Genetic Research ◽

Twin Studies ◽

Future Research ◽

Psychiatric Genetic ◽

Genome Wide ◽

Clinical Disorders ◽

Genetic Risks

AbstractGenetic influences play a significant role in risk for psychiatric disorders, prompting numerous endeavors to further understand their underlying genetic architecture. In this paper, we summarize and review evidence from traditional twin studies and more recent genome-wide molecular genetic analyses regarding two important issues that have proven particularly informative for psychiatric genetic research. First, emerging results are beginning to suggest that genetic risk factors for some (but not all) clinically diagnosed psychiatric disorders or extreme manifestations of psychiatric traits in the population share genetic risks with quantitative variation in milder traits of the same disorder throughout the general population. Second, there is now evidence for substantial sharing of genetic risks across different psychiatric disorders. This extends to the level of characteristic traits throughout the population, with which some clinical disorders also share genetic risks. In this review, we summarize and evaluate the evidence for these two issues, for a range of psychiatric disorders. We then critically appraise putative interpretations regarding the potential meaning of genetic correlation across psychiatric phenotypes. We highlight several new methods and studies which are already using these insights into the genetic architecture of psychiatric disorders to gain additional understanding regarding the underlying biology of these disorders. We conclude by outlining opportunities for future research in this area.

GWAS with principal component analysis identifies a gene comprehensively controlling rice architecture

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1904964116 ◽

2019 ◽

Vol 116 (42) ◽

pp. 21262-21267 ◽

Cited By ~ 22

Author(s):

Kenji Yano ◽

Yoichi Morinaka ◽

Fanmiao Wang ◽

Peng Huang ◽

Sayaka Takehara ◽

...

Keyword(s):

Principal Component Analysis ◽

Genome Wide Association Study ◽

Crop Yields ◽

Principal Component ◽

Complex Trait ◽

Component Analysis ◽

Gene Encoding ◽

Signal Suppression ◽

Genome Wide ◽

A Genome

Elucidation of the genetic control of rice architecture is crucial due to the global demand for high crop yields. Rice architecture is a complex trait affected by plant height, tillering, and panicle morphology. In this study, principal component analysis (PCA) on 8 typical traits related to plant architecture revealed that the first principal component (PC), PC1, provided the most information on traits that determine rice architecture. A genome-wide association study (GWAS) using PC1 as a dependent variable was used to isolate a gene encoding rice, SPINDLY (OsSPY), that activates the gibberellin (GA) signal suppression protein SLR1. The effect of GA signaling on the regulation of rice architecture was confirmed in 9 types of isogenic plant having different levels of GA responsiveness. Further population genetics analysis demonstrated that the functional allele of OsSPY associated with semidwarfism and small panicles was selected in the process of rice breeding. In summary, the use of PCA in GWAS will aid in uncovering genes involved in traits with complex characteristics.

An atlas of genetic correlations between psychiatric disorders and human blood plasma proteome

European Psychiatry ◽

10.1192/j.eurpsy.2019.6 ◽

2020 ◽

Vol 63 (1) ◽

Author(s):

Shiqiang Cheng ◽

Fanglin Guan ◽

Mei Ma ◽

Lu Zhang ◽

Bolun Cheng ◽

...

Keyword(s):

Psychiatric Disorders ◽

Plasma Proteins ◽

Genome Wide Association Study ◽

Genetic Relationships ◽

Genetic Correlations ◽

Tumor Necrosis Factor Receptor ◽

Autism Spectrum ◽

Human Blood Plasma ◽

Plasma Proteome ◽

Genome Wide

Abstract Background. Psychiatric disorders are a group of complex psychological syndromes with high prevalence. Recent studies observed associations between altered plasma proteins and psychiatric disorders. This study aims to systematically explore the potential genetic relationships between five major psychiatric disorders and more than 3,000 plasma proteins. Methods. The genome-wide association study (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) were driven from the Psychiatric GWAS Consortium. The GWAS datasets of 3,283 human plasma proteins were derived from recently published study, including 3,301 study subjects. Linkage disequilibrium score (LDSC) regression analysis were conducted to evaluate the genetic correlations between psychiatric disorders and each of the 3,283 plasma proteins. Results. LDSC observed several genetic correlations between plasma proteins and psychiatric disorders, such as ADHD and lysosomal Pro-X carboxypeptidase (p value = 0.015), ASD and extracellular superoxide dismutase (Cu-Zn; p value = 0.023), BD and alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase 6 (p value = 0.007), MDD and trefoil factor 1 (p value = 0.011), and SCZ and insulin-like growth factor-binding protein 6 (p value = 0.011). Additionally, we detected four common plasma proteins showing correlation evidence with both BD and SCZ, such as tumor necrosis factor receptor superfamily member 1B (p value = 0.012 for BD, p value = 0.011 for SCZ). Conclusions. This study provided an atlas of genetic correlations between psychiatric disorders and plasma proteome, providing novel clues for pathogenetic and biomarkers, therapeutic studies of psychiatric disorders.

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.

Genome-wide association study reveals the genetic architecture of 27 yield-related traits in tomato

10.21203/rs.3.rs-86537/v1 ◽

2020 ◽

Author(s):

Jie Ye ◽

Xin Wang ◽

Wenqian Wang ◽

Huiyang Yu ◽

Guo Ai ◽

...

Keyword(s):

Association Study ◽

Genetic Architecture ◽

Genome Wide Association Study ◽

Genetic Research ◽

Reproductive Development ◽

Genome Wide Association ◽

Phenotypic Variance ◽

Vegetable Crop ◽

Genome Wide ◽

Malate Transporter

Abstract Tomato (Solanum lycopersicum) is a highly valuable vegetable crop and yield is one of the most important traits. Uncovering the genetic architecture of yield-related traits in tomato is critical for the management of vegetative and reproductive development, thereby enhancing yield. Here we perform a comprehensive genome-wide association study for 27 yield-related traits in tomato. A total of 239 significant associations corresponding to 129 loci, harboring many reported and novel genes related to vegetative and reproductive development, were identified, and these loci explained an average of ~8.8% of the phenotypic variance. A total of 51 loci associated with 25 traits have been under selection, especially during tomato improvement. Furthermore, a candidate gene, SlALMT15 that encodes an aluminum-activated malate transporter, was functionally characterized and shown to act as a pivotal regulator of leaf stomata formation through increasing photosynthesis and drought resistance. This study provides valuable information for tomato genetic research and breeding.

Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data

10.1101/858431 ◽

2019 ◽

Author(s):

Huwenbo Shi ◽

Kathryn S. Burch ◽

Ruth Johnson ◽

Malika K. Freund ◽

Gleb Kichaev ◽

...

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Empirical Bayes ◽

Genetic Correlations ◽

Causal Snps ◽

European Ancestry ◽

Risk Scores ◽

Common Snps ◽

Genome Wide ◽

Summary Data

AbstractDespite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze 9 complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8x enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWAS due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

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.

Examining sex-differentiated genetic effects across neuropsychiatric and behavioral traits

10.1101/2020.05.04.076042 ◽

2020 ◽

Cited By ~ 1

Author(s):

Joanna Martin ◽

Ekaterina A. Khramtsova ◽

Slavina B. Goleva ◽

Gabriëlla A M. Blokland ◽

Michela Traglia ◽

...

Keyword(s):

Sex Differences ◽

Risk Taking ◽

Genetic Architecture ◽

Genetic Correlations ◽

Related Gene ◽

Behavioral Traits ◽

Gene Sets ◽

Genome Wide ◽

Z Scores ◽

Differentiated Effects

AbstractBackgroundThe origin of sex differences in prevalence and presentation of neuropsychiatric and behavioral traits is largely unknown. Given established genetic contributions and correlations across these traits, we tested for a sex-differentiated genetic architecture within and between traits.MethodsUsing genome-wide association study (GWAS) summary statistics for 20 neuropsychiatric and behavioral traits, we tested for differences in SNP-based heritability (h2) and genetic correlation (rg<1) between sexes. For each trait, we computed z-scores from sex-stratified GWAS regression coefficients and identified genes with sex-differentiated effects. We calculated Pearson correlation coefficients between z-scores for each trait pair, to assess whether specific pairs share variants with sex-differentiated effects. Finally, we tested for sex differences in between-trait genetic correlations.ResultsWith current sample sizes (and power), we found no significant, consistent sex differences in SNP-based h2. Between-sex, within-trait genetic correlations were consistently high, although significantly less than 1 for educational attainment and risk-taking behavior. We identified genome-wide significant genes with sex-differentiated effects for eight traits. Several trait pairs shared sex-differentiated effects. The top 0.1% of genes with sex-differentiated effects across traits overlapped with neuron- and synapse-related gene sets. Most between-trait genetic correlation estimates were similar across sex, with several exceptions (e.g. educational attainment & risk-taking behavior).ConclusionsSex differences in the common autosomal genetic architecture of neuropsychiatric and behavioral phenotypes are small and polygenic, requiring large sample sizes. Genes with sex-differentiated effects are enriched for neuron-related gene sets. This work motivates further investigation of genetic, as well as environmental, influences on sex differences.

Genetic Architecture of 11 Major Psychiatric Disorders at Biobehavioral, Functional Genomic, and Molecular Genetic Levels of Analysis

10.1101/2020.09.22.20196089 ◽

2020 ◽

Author(s):

Andrew D. Grotzinger ◽

Travis T. Mallard ◽

Wonuola A. Akingbuwa ◽

Hill F. Ip ◽

Mark J. Adams ◽

...

Keyword(s):

Psychiatric Disorders ◽

Structural Equation ◽

Genetic Architecture ◽

Molecular Genetic ◽

Genetic Correlations ◽

Levels Of Analysis ◽

Functional Genomic ◽

Association Analyses ◽

Psychotic Features ◽

Genomic Regions

We systematically interrogate the joint genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic, and molecular genetic levels of analysis. We identify four broad factors (Neurodevelopmental, Compulsive, Psychotic, and Internalizing) that underlie genetic correlations among the disorders, and test whether these factors adequately explain their genetic correlations with biobehavioral traits. We introduce Stratified Genomic Structural Equation Modelling, which we use to identify gene sets and genomic regions that disproportionately contribute to pleiotropy, including protein-truncating variant intolerant genes expressed in excitatory and GABAergic brain cells that are enriched for pleiotropy between disorders with psychotic features. Multivariate association analyses detect a total of 152 (20 novel) independent loci which act on the four factors, and identify nine loci that act heterogeneously across disorders within a factor. Despite moderate to high genetic correlations across all 11 disorders, we find very little utility of, or evidence for, a single dimension of genetic risk across psychiatric disorders.