Identifying the genetic basis and molecular mechanisms underlying phenotypic correlation between complex human traits using a gene-based approach

Abstract Phenotypic correlations between complex human traits have long been observed based on epidemiological studies. However, the genetic basis and underlying mechanisms are largely unknown. The recent accumulation of GWAS data has made it possible to analyze the genetic similarity between human traits through comparative analysis. Here we developed a gene-based approach to measure genetic similarity between a pair of traits and to delineate the shared genes/pathways, through three steps: 1) translating SNP-phenotype association profile to gene-phenotype association profile by integrating GWAS with eQTL data; 2) measuring the similarity between a pair of traits by a normalized distance between the two gene-phenotype association profiles; 3) delineating genes/pathways supporting the similarity. Application of this approach to a set of GWAS data covering 59 human traits detected significant similarity between many known and unexpected pairs of traits; a significant fraction of them are not detectable by SNP based similarity measures. Examples include Height and Schizophrenia, Cancer and Alzheimer’s Disease, and Rheumatoid Arthritis and Crohn’s disease. Functional analysis revealed specific genes/pathways shared by these pairs. For example, Height and Schizophrenia are co-associated with genes involved in neural development, skeletal muscle regeneration, protein synthesis, magnesium homeostasis, and immune response, suggesting growth and development as a common theme underlying both traits. Our approach can detect yet unknown relationships between complex traits and generate mechanistic hypotheses, and has the potential to improve diagnosis and treatment by transferring knowledge from one disease to another.

Download Full-text

Identifying the genetic basis and molecular mechanisms underlying phenotypic correlation between complex human traits using a gene-based approach

10.1101/2021.02.09.430368 ◽

2021 ◽

Author(s):

Jialiang Gu ◽

Chris Fuller ◽

Jiashun Zheng ◽

Hao Li

Keyword(s):

Neural Development ◽

Complex Traits ◽

Genetic Similarity ◽

Genetic Basis ◽

Molecular Mechanisms ◽

Gwas Data ◽

Skeletal Muscle Regeneration ◽

Phenotypic Correlation ◽

Significant Similarity ◽

Complex Human Traits

AbstractPhenotypic correlations between complex human traits have long been observed based on epidemiological studies. However, the genetic basis and underlying mechanisms are largely unknown. The recent accumulation of GWAS data has made it possible to analyze the genetic similarity between human traits through comparative analysis. Here we developed a gene-based approach to measure genetic similarity between a pair of traits and to delineate the shared genes/pathways, through three steps: 1) translating SNP-phenotype association profile to genephenotype association profile by integrating GWAS with eQTL data; 2) measuring the similarity between a pair of traits by a normalized distance between the two gene-phenotype association profiles; 3) delineating genes/pathways supporting the similarity. Application of this approach to a set of GWAS data covering 59 human traits detected significant similarity between many known and unexpected pairs of traits; a significant fraction of them are not detectable by SNP based similarity measures. Examples include Height and Schizophrenia, Cancer and Alzheimer’s Disease, and Rheumatoid Arthritis and Crohn’s disease. Functional analysis revealed specific genes/pathways shared by these pairs. For example, Height and Schizophrenia are co-associated with genes involved in neural development, skeletal muscle regeneration, protein synthesis, magnesium homeostasis, and immune response, suggesting growth and development as a common theme underlying both traits. Our approach can detect yet unknown relationships between complex traits and generate mechanistic hypotheses, and has the potential to improve diagnosis and treatment by transferring knowledge from one disease to another.

Download Full-text

PhenoSpD: an integrated toolkit for phenotypic correlation estimation and multiple testing correction using GWAS summary statistics

10.1101/148627 ◽

2017 ◽

Cited By ~ 4

Author(s):

Jie Zheng ◽

Tom G. Richardson ◽

Louise A. C. Millard ◽

Gibran Hemani ◽

Christopher Raistrick ◽

...

Keyword(s):

Complex Traits ◽

Multiple Testing ◽

Large Scale ◽

Genome Wide Association Study ◽

Phenotypic Correlation ◽

Summary Statistics ◽

Multiple Testing Correction ◽

Individual Level ◽

Phenotypic Correlations ◽

Complex Human Traits

AbstractBackgroundIdentifying phenotypic correlations between complex traits and diseases can provide useful etiological insights. Restricted access to individual-level phenotype data makes it difficult to estimate large-scale phenotypic correlation across the human phenome. State-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate phenotypic correlation using genome-wide association study (GWAS) summary statistics.ResultsHere, we present an integrated R toolkit, PhenoSpD, to 1) apply metaCCA (or LD score regression) to estimate phenotypic correlations using GWAS summary statistics; and 2) to utilize the estimated phenotypic correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest it is possible to estimate phenotypic correlation using samples with only a partial overlap, but as overlap decreases correlations will attenuate towards zero and multiple testing correction will be more stringent than in perfectly overlapping samples. In a case study, PhenoSpD using GWAS results suggested 324.4 independent tests among 452 metabolites, which is close to the 296 independent tests estimated using true phenotypic correlation. We further applied PhenoSpD to estimated 7,503 pair-wise phenotypic correlations among 123 metabolites using GWAS summary statistics from Kettunen et al. and PhenoSpD suggested 44.9 number of independent tests for theses metabolites.ConclusionPhenoSpD integrates existing methods and provides a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics, which is particularly valuable for post-GWAS analysis of complex molecular traits.AvailabilityR code and documentation for PhenoSpD V1.0.0 is available online (https://github.com/MRCIEU/PhenoSpD).

Download Full-text

An integrative analysis of GWAS and intermediate molecular trait data reveals common molecular mechanisms supporting genetic similarity between seemingly unrelated complex traits

10.1101/601229 ◽

2019 ◽

Author(s):

Jialiang Gu ◽

Chris Fuller ◽

Jiashun Zheng ◽

Hao Li

Keyword(s):

Breast Cancer ◽

Complex Traits ◽

Genetic Similarity ◽

Molecular Mechanisms ◽

Association Studies ◽

Integrative Analysis ◽

Ros Generation ◽

Genome Wide Association Studies ◽

Statistical Framework ◽

Significant Gene

AbstractThe rapid accumulation of Genome Wide Association Studies (GWAS) and association studies of intermediate molecular traits provides new opportunities for comparative analysis of the genetic basis of complex human phenotypes. Using a newly developed statistical framework called Sherlock-II that integrates GWAS with eQTL (expression Quantitative Trait Loci) and metabolite-QTL data, we systematically analyzed 445 GWAS datasets, and identified 1371 significant gene-phenotype associations and 308 metabolites-phenotype associations (passing a Q value cutoff of 1/3). This integrative analysis allows us to translate SNP-phenotype associations into functionally informative gene-phenotype association profiles. Genetic similarity analyses based on these profiles clustered phenotypes into sub-trees that reveal both expected and unexpected relationships. We employed a statistical approach to delineate sets of functionally related genes that contribute to the similarity between their association profiles. This approach suggested common molecular mechanisms that connect the phenotypes in a subtree. For example, we found that fasting insulin, fasting glucose, breast cancer, prostate cancer, and lung cancer clustered into a subtree, and identified cyclic AMP/GMP signaling that connects breast cancer and insulin, NAPDH oxidase/ROS generation that connects the three cancers, and apoptosis that connects all five phenotypes. Our approach can be used to assess genetic similarity and suggest mechanistic connections between phenotypes. It has the potential to improve the diagnosis and treatment of a disease by mapping mechanistic insights from one phenotype onto others based on common molecular underpinnings.

Download Full-text

Genome-Wide Association for Itraconazole Sensitivity in Non-Resistant Clinical Isolates of Aspergillus fumigatus

10.1101/2020.08.31.275297 ◽

2020 ◽

Author(s):

Shu Zhao ◽

Wenbo Ge ◽

Akira Watanabe ◽

Jarrod R. Fortwendel ◽

John G. Gibbons

Keyword(s):

Aspergillus Fumigatus ◽

Complex Traits ◽

Genetic Basis ◽

Molecular Mechanisms ◽

Clinical Isolates ◽

Genome Wide Association ◽

Azole Resistance ◽

Genome Wide ◽

Population Structure Analysis ◽

Recent Emergence

AbstractAspergillus fumigatus is a potentially lethal opportunistic pathogen that infects over ∼200,000 people and causes ∼100,000 deaths per year globally. Treating A. fumigatus infections is particularly challenging because of the recent emergence of azole-resistance. The majority of studies focusing on the molecular mechanisms underlying azole resistance have examined azole-resistant isolates. However, isolates that are susceptible to azoles also display variation in their sensitivity, presenting a unique opportunity to identify genes contributing to azole sensitivity. Here, we used genome-wide association (GWA) analysis to identify loci involved in azole sensitivity by analyzing the association between 68,853 SNPs and itraconazole (ITCZ) minimum inhibitory concentration (MIC) in 76 clinical isolates of A. fumigatus from Japan. Population structure analysis suggests the presence of four distinct populations, with ITCZ MICs distributed relatively evenly across populations. We independently conducted GWA when treating ITCZ MIC as a quantitative trait and a binary trait and identified two SNPs with strong associations that were identified in both analyses. These SNPs fell within the coding regions of Afu2g02220 and Afu2g02140. We functionally validated Afu2g02220 by knocking it out using a CRISPR/Cas-9 approach, because orthologs of this gene are involved in sterol modification and ITCZ targets the ergosterol pathway. Knockout strains displayed no difference in growth compared to the parent strain in minimal media, yet a minor but consistent inhibition of growth in the presence of 0.15 ug/ml ITCZ. Our results suggest that GWA paired with efficient gene deletion is a powerful and unbiased strategy for identifying the genetic basis of complex traits in A. fumigatus.ImportanceAspergillus fumigatus is a pathogenic mold that can infect and kill individuals with compromised immune systems. The azole class of drugs provide antifungal activity against A. fumigatus infections and have become an essential treatment strategy. Unfortunately, A. fumigatus azole resistance has recently emerged and rapidly risen in frequency making treatment more challenging. Our understanding of the molecular basis of azole sensitivity has been shaped mainly through candidate gene studies. Unbiased approaches are necessary to understand the full repertoire of genes and genetic variants underlying azole resistance and sensitivity. Here, we provide the first application of genome-wide association analysis in A. fumigatus in the identification of a gene (Afu2g02220) that contributes to itraconazole susceptibility. Our approach, which combines association mapping and CRISPR/Cas-9 for functional validation of candidate genes, has broad application for investigating the genetic basis of complex traits in fungal systems.

Download Full-text

Molecular Basis and Clinical Aspects of Hereditary Megakaryocyte and Platelet Membrane Glycoprotein Disorders

Hämostaseologie ◽

10.1055/s-0038-1656647 ◽

1996 ◽

Vol 16 (02) ◽

pp. 114-138 ◽

Cited By ~ 6

Author(s):

R. E. Scharf

Keyword(s):

Genetic Basis ◽

Molecular Mechanisms ◽

Molecular Genetic ◽

Platelet Membrane ◽

Clinical Aspects ◽

Membrane Glycoprotein ◽

Membrane Glycoproteins ◽

Membrane Expression ◽

Receptor Complexes ◽

Platelet Membrane Glycoprotein

SummarySpecific membrane glycoproteins (GP) expressed by the megakaryocyte-platelet system, including GPIa-lla, GPIb-V-IX, GPIIb-llla, and GPIV are involved in mediat-ing platelet adhesion to the subendothelial matrix. Among these glycoproteins, GPIIb-llla plays a pivotal role since platelet aggregation is exclusively mediated by this receptor and its interaction with soluble macromolecular proteins. Inherited defects of the GPIIb-llla or GPIb-V-IX receptor complexes are associated with bleeding disorders, known as Glanzmann's thrombasthenia, Bernard-Soulier syndrome, or platelet-type von Willebrand's disease, respectively. Using immuno-chemical and molecular biology techniques, rapid advances in our understanding of the molecular genetic basis of these disorders have been made during the last few years. Moreover, analyses of patients with congenital platelet membrane glycoprotein abnormalities have provided valuable insights into molecular mechanisms that are required for structural and functional integrity, normal biosynthesis of the glycoprotein complexes and coordinated membrane expression of their constituents. The present article reviews the current state of knowledge of the major membrane glycoproteins in health and disease. The spectrum of clinical bleeding manifestations and established diagnostic criteria for each of these dis-orders are summarized. In particular, the variety of molecular defects that have been identified so far and their genetic basis will be discussed.

Download Full-text

Contextualizing genetic risk score for disease screening and rare variant discovery

Nature Communications ◽

10.1038/s41467-021-24387-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Dan Zhou ◽

Dongmei Yu ◽

Jeremiah M. Scharf ◽

Carol A. Mathews ◽

Lauren McGrath ◽

...

Keyword(s):

Complex Traits ◽

Genetic Basis ◽

Clinical Decision Making ◽

Genetic Risk Score ◽

Sufficient Conditions ◽

Clinical Decision ◽

Generative Models ◽

Variant Discovery ◽

Models Of Disease ◽

The Uk

AbstractStudies of the genetic basis of complex traits have demonstrated a substantial role for common, small-effect variant polygenic burden (PB) as well as large-effect variants (LEV, primarily rare). We identify sufficient conditions in which GWAS-derived PB may be used for well-powered rare pathogenic variant discovery or as a sample prioritization tool for whole-genome or exome sequencing. Through extensive simulations of genetic architectures and generative models of disease liability with parameters informed by empirical data, we quantify the power to detect, among cases, a lower PB in LEV carriers than in non-carriers. Furthermore, we uncover clinically useful conditions wherein the risk derived from the PB is comparable to the LEV-derived risk. The resulting summary-statistics-based methodology (with publicly available software, PB-LEV-SCAN) makes predictions on PB-based LEV screening for 36 complex traits, which we confirm in several disease datasets with available LEV information in the UK Biobank, with important implications on clinical decision-making.

Download Full-text

Genetic Influences on the Covariance and Genetic Correlations in a Bivariate Twin Model: An Application to Well-Being

Behavior Genetics ◽

10.1007/s10519-021-10046-y ◽

2021 ◽

Author(s):

Lianne P. de Vries ◽

Toos C. E. M. van Beijsterveldt ◽

Hermine Maes ◽

Lucía Colodro-Conde ◽

Meike Bartels

Keyword(s):

Complex Traits ◽

Educational Achievement ◽

Genetic Correlations ◽

Well Being ◽

Genetic Influences ◽

Phenotypic Correlation ◽

Twin Model ◽

Twin Models ◽

Adolescent Twins ◽

Depressed Symptoms

AbstractThe distinction between genetic influences on the covariance (or bivariate heritability) and genetic correlations in bivariate twin models is often not well-understood or only one is reported while the results show distinctive information about the relation between traits. We applied bivariate twin models in a large sample of adolescent twins, to disentangle the association between well-being (WB) and four complex traits (optimism, anxious-depressed symptoms (AD), aggressive behaviour (AGG), and educational achievement (EA)). Optimism and AD showed respectively a strong positive and negative phenotypic correlation with WB, the negative correlation of WB and AGG is lower and the correlation with EA is nearly zero. All four traits showed a large genetic contribution to the covariance with well-being. The genetic correlations of well-being with optimism and AD are strong and smaller for AGG and EA. We used the results of the models to explain what information is retrieved based on the bivariate heritability versus the genetic correlations and the (clinical) implications.

Download Full-text

Meta-QTL analysis and identification of candidate genes for quality, abiotic and biotic stress in durum wheat

Scientific Reports ◽

10.1038/s41598-021-91446-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jose Miguel Soriano ◽

Pasqualina Colasuonno ◽

Ilaria Marcotuli ◽

Agata Gadaleta

Keyword(s):

Molecular Markers ◽

Abiotic Stress ◽

Durum Wheat ◽

Candidate Genes ◽

Biotic Stress ◽

Qtl Analysis ◽

Complex Traits ◽

Genetic Basis ◽

Agronomic Traits ◽

Plant Performance

AbstractThe genetic improvement of durum wheat and enhancement of plant performance often depend on the identification of stable quantitative trait loci (QTL) and closely linked molecular markers. This is essential for better understanding the genetic basis of important agronomic traits and identifying an effective method for improving selection efficiency in breeding programmes. Meta-QTL analysis is a useful approach for dissecting the genetic basis of complex traits, providing broader allelic coverage and higher mapping resolution for the identification of putative molecular markers to be used in marker-assisted selection. In the present study, extensive QTL meta-analysis was conducted on 45 traits of durum wheat, including quality and biotic and abiotic stress-related traits. A total of 368 QTL distributed on all 14 chromosomes of genomes A and B were projected: 171 corresponded to quality-related traits, 127 to abiotic stress and 71 to biotic stress, of which 318 were grouped in 85 meta-QTL (MQTL), 24 remained as single QTL and 26 were not assigned to any MQTL. The number of MQTL per chromosome ranged from 4 in chromosomes 1A and 6A to 9 in chromosome 7B; chromosomes 3A and 7A showed the highest number of individual QTL (4), and chromosome 7B the highest number of undefined QTL (4). The recently published genome sequence of durum wheat was used to search for candidate genes within the MQTL peaks. This work will facilitate cloning and pyramiding of QTL to develop new cultivars with specific quantitative traits and speed up breeding programs.

Download Full-text

Epistasis for Three Grain Yield Components in Rice (Oryxa sativa L.)

Genetics ◽

10.1093/genetics/145.2.453 ◽

1997 ◽

Vol 145 (2) ◽

pp. 453-465 ◽

Cited By ~ 6

Author(s):

Zhikang Li ◽

Shannon R M Pinson ◽

William D Park ◽

Andrew H Paterson ◽

James W Stansel

Keyword(s):

Grain Yield ◽

Complex Traits ◽

Yield Components ◽

Genetic Basis ◽

Kernel Weight ◽

Progeny Testing ◽

Grain Yield Components ◽

Main Effects ◽

Grain Number Per Panicle

The genetic basis for three grain yield components of rice, 1000 kernel weight (KW), grain number per panicle (GN), and grain weight per panicle (GWP), was investigated using restriction fragment length polymorphism markers and F4 progeny testing from a cross between rice subspecies japonica (cultivar Lemont from USA) and indica (cv. Teqing from China). Following identification of 19 QTL affecting these traits, we investigated the role of epistasis in genetic control of these phenotypes. Among 63 markers distributed throughout the genome that appeared to be involved in 79 highly significant (P < 0.001) interactions, most (46 or 73%) did not appear to have “main” effects on the relevant traits, but influenced the trait(s) predominantly through interactions. These results indicate that epistasis is an important genetic basis for complex traits such as yield components, especially traits of low heritability such as GN and GWP. The identification of epistatic loci is an important step toward resolution of discrepancies between quantitative trait loci mapping and classical genetic dogma, contributes to better understanding of the persistence of quantitative genetic variation in populations, and impels reconsideration of optimal mapping methodology and marker-assisted breeding strategies for improvement of complex traits.

Download Full-text

Plant evolution driven by interactions with symbiotic and pathogenic microbes

Science ◽

10.1126/science.aba6605 ◽

2021 ◽

Vol 371 (6531) ◽

pp. eaba6605 ◽

Cited By ~ 4

Author(s):

Pierre-Marc Delaux ◽

Sebastian Schornack

Keyword(s):

Cell Biology ◽

Genetic Basis ◽

Reverse Genetics ◽

Molecular Mechanisms ◽

Plant Evolution ◽

Symbiotic Microorganisms ◽

Protection Mechanisms ◽

Evolutionary Trajectories ◽

Microbe Interactions ◽

Pathogenic Microbes

During 450 million years of diversification on land, plants and microbes have evolved together. This is reflected in today’s continuum of associations, ranging from parasitism to mutualism. Through phylogenetics, cell biology, and reverse genetics extending beyond flowering plants into bryophytes, scientists have started to unravel the genetic basis and evolutionary trajectories of plant-microbe associations. Protection against pathogens and support of beneficial, symbiotic, microorganisms are sustained by a blend of conserved and clade-specific plant mechanisms evolving at different speeds. We propose that symbiosis consistently emerges from the co-option of protection mechanisms and general cell biology principles. Exploring and harnessing the diversity of molecular mechanisms used in nonflowering plant-microbe interactions may extend the possibilities for engineering symbiosis-competent and pathogen-resilient crops.

Download Full-text