Phenotypes In Association Studies Research Articles

A novel method for multiple phenotype association studies based on genotype and phenotype network.

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.

The trichome pattern diversity of Cardamine shares genetic mechanisms with Arabidopsis but differs in environmental drivers.

Natural variation in trichome pattern (amount and distribution) is prominent among populations of many angiosperms. However, the degree of parallelism in the genetic mechanisms underlying this diversity and its environmental drivers in different species remain unclear. To address these questions, we analyzed the genomic and environmental bases of leaf trichome pattern diversity in Cardamine hirsuta, a relative of Arabidopsis (Arabidopsis thaliana). We characterized 123 wild accessions for their genomic diversity, leaf trichome patterns at different temperatures, and environmental adjustments. Nucleotide diversities and biogeographical distribution models identified two major genetic lineages with distinct demographic and adaptive histories. Additionally, C. hirsuta showed substantial variation in trichome pattern and plasticity to temperature. Trichome amount in C. hirsuta correlated positively with spring precipitation but negatively with temperature, which is opposite to climatic patterns in A. thaliana. Contrastingly, genetic analysis of C. hirsuta glabrous accessions indicated that, like for A. thaliana, glabrousness is caused by null mutations in ChGLABRA1 (ChGL1). Phenotypic genome-wide association studies (GWAS) further identified a ChGL1 haplogroup associated with low trichome density and ChGL1 expression. Therefore, a ChGL1 series of null and partial loss-of-function alleles accounts for the parallel evolution of leaf trichome pattern in C. hirsuta and A. thaliana. Finally, GWAS also detected other candidate genes (e.g. ChETC3, ChCLE17) that might affect trichome pattern. Accordingly, the evolution of this trait in C. hirsuta and A. thaliana shows partially conserved genetic mechanisms but is likely involved in adaptation to different environments.

Decoding and reconstructing disease relations between dry eye and depression: a multimodal investigation comprising meta-analysis, genetic pathways and Mendelian randomization

IntroductionThe clinical presentations of dry eye disease (DED) and depression (DEP) often comanifest. However, the robustness and the mechanisms underlying this association were undetermined. ObjectivesTo this end, we set up a three-segment study that employed multimodality results (meta-analysis, genome-wide association study [GWAS] and Mendelian randomization [MR]) to elucidate the association, common pathways and causality between DED and DEP. MethodsA meta-analysis comprising 26 case−control studies was first conducted to confirm the DED-DEP association. Next, we performed a linkage disequilibrium (LD)-adjusted GWAS and targeted phenotype association study (PheWAS) in East Asian TW Biobank (TWB) and European UK Biobank (UKB) populations. Single-nucleotide polymorphisms (SNPs) were further screened for molecular interactions and common pathways at the functional gene level. To further elucidate the activated pathways in DED and DEP, a systemic transcriptome review was conducted on RNA sequencing samples from the Gene Expression Omnibus. Finally, 48 MR experiments were implemented to examine the bidirectional causation between DED and DEP. ResultsOur meta-analysis showed that DED patients are associated with an increased DEP prevalence (OR = 1.83), while DEP patients have a concurrent higher risk of DED (OR = 2.34). Notably, cross-disease GWAS analysis revealed that similar genetic architecture (rG = 0.19) and pleiotropic functional genes contributed to phenotypes in both diseases. Through protein−protein interaction and ontology convergence, we summarized the pleiotropic functional genes under the ontology of immune activation, which was further validated by a transcriptome systemic review. Importantly, the inverse variance-weighted (IVW)-MR experiments in both TWB and UKB populations (p value <0.001) supported the bidirectional exposure-outcome causation for DED-to-DEP and DEP-to-DED. Despite stringent LD-corrected instrumental variable re-selection, the bidirectional causation between DED and DEP remained. ConclusionWith the multi-modal evidence combined, we consolidated the association and causation between DED and DEP.

Transcription factor encoding gene OsC1 regulates leaf sheath color through anthocyanidin metabolism in Oryza rufipogon and Oryza sativa

Carbohydrates, proteins, lipids, minerals and vitamins are nutrient substances commonly seen in rice grains, but anthocyanidin, with benefit for plant growth and animal health, exists mainly in the common wild rice but hardly in the cultivated rice. To screen the rice germplasm with high intensity of anthocyanidins and identify the variations, we used metabolomics technique and detected significant different accumulation of anthocyanidins in common wild rice (Oryza rufipogon, with purple leaf sheath) and cultivated rice (Oryza sativa, with green leaf sheath). In this study, we identified and characterized a well-known MYB transcription factor, OsC1, through phenotypic (leaf sheath color) and metabolic (metabolite profiling) genome-wide association studies (pGWAS and mGWAS) in 160 common wild rice (O. rufipogon) and 151 cultivated (O. sativa) rice varieties. Transgenic experiments demonstrated that biosynthesis and accumulation of cyanidin-3-Galc, cyanidin 3-O-rutinoside and cyanidin O-syringic acid, as well as purple pigmentation in leaf sheath were regulated by OsC1. A total of 25 sequence variations of OsC1 constructed 16 functional haplotypes (higher accumulation of the three anthocyanidin types within purple leaf sheath) and 9 non-functional haplotypes (less accumulation of anthocyanidins within green leaf sheath). Three haplotypes of OsC1 were newly identified in our germplasm, which have potential values in functional genomics and molecular breeding of rice. Gene-to-metabolite analysis by mGWAS and pGWAS provides a useful and efficient tool for functional gene identification and omics-based crop genetic improvement.

Unraveling shared susceptibility loci and Mendelian genetic associations linking educational attainment with multiple neuropsychiatric disorders.

Empirical studies have demonstrated that educational attainment (EA) is associated with neuropsychiatric disorders (NPDs), suggesting a shared etiological basis between them. However, little is known about the shared genetic mechanisms and causality behind such associations. This study explored the shared genetic basis and causal relationships between EA and NPDs using the high-definition likelihood (HDL) method, cross phenotype association study (CPASSOC), transcriptome-wide association study (TWAS), and bidirectional Mendelian randomization (MR) with summary-level data for EA (N = 293,723) and NPDs (N range = 9,725 to 455,258). Significant genetic correlations between EA and 12 NPDs (rg range - 0.49 to 0.35; all p < 3.85 × 10-3) were observed. CPASSOC identified 37 independent loci shared between EA and NPDs, one of which was novel (rs71351952, mapped gene: ARFGEF2). Functional analyses and TWAS found shared genes were enriched in brain tissue, especially in the cerebellum and highlighted the regulatory role of neuronal signaling, purine nucleotide metabolic process, and cAMP-mediated signaling pathways. CPASSOC and TWAS supported the role of three regions of 6q16.1, 3p21.31, and 17q21.31 might account for the shared causes between EA and NPDs. MR confirmed higher genetically predicted EA lower the risk of ADHD (ORIVW: 0.50; 95% CI: 0.39 to 0.63) and genetically predicted ADHD decreased the risk of EA (Causal effect: -2.8 months; 95% CI: -3.9 to -1.8). These findings provided evidence of shared genetics and causation between EA and NPDs, advanced our understanding of EA, and implicated potential biological pathways that might underlie both EA and NPDs.

Metabolic GWAS-based dissection of genetic basis underlying nutrient quality variation and domestication of cassava storage root

BackgroundMetabolites play critical roles in regulating nutritional qualities of plants, thereby influencing their consumption and human health. However, the genetic basis underlying the metabolite-based nutrient quality and domestication of root and tuber crops remain largely unknown.ResultsWe report a comprehensive study combining metabolic and phenotypic genome-wide association studies to dissect the genetic basis of metabolites in the storage root (SR) of cassava. We quantify 2,980 metabolic features in 299 cultivated cassava accessions. We detect 18,218 significant marker-metabolite associations via metabolic genome-wide association mapping and identify 12 candidate genes responsible for the levels of metabolites that are of potential nutritional importance. Me3GT, MeMYB4, and UGT85K4/UGT85K5, which are involved in flavone, anthocyanin, and cyanogenic glucoside metabolism, respectively, are functionally validated through in vitro enzyme assays and in vivo gene silencing analyses. We identify a cluster of cyanogenic glucoside biosynthesis genes, among which CYP79D1, CYP71E7b, and UGT85K5 are highly co-expressed and their allelic combination contributes to low linamarin content. We find MeMYB4 is responsible for variations in cyanidin 3-O-glucoside and delphinidin 3-O-rutinoside contents, thus controlling SR endothelium color. We find human selection affects quercetin 3-O-glucoside content and SR weight per plant. The candidate gene MeFLS1 is subject to selection during cassava domestication, leading to decreased quercetin 3-O-glucoside content and thus increased SR weight per plant.ConclusionsThese findings reveal the genetic basis of cassava SR metabolome variation, establish a linkage between metabolites and agronomic traits, and offer useful resources for genetically improving the nutrition of cassava and other root crops.

A high-resolution genotype–phenotype map identifies the TaSPL17 controlling grain number and size in wheat

BackgroundLarge-scale genotype–phenotype association studies of crop germplasm are important for identifying alleles associated with favorable traits. The limited number of single-nucleotide polymorphisms (SNPs) in most wheat genome-wide association studies (GWASs) restricts their power to detect marker-trait associations. Additionally, only a few genes regulating grain number per spikelet have been reported due to sensitivity of this trait to variable environments.ResultsWe perform a large-scale GWAS using approximately 40 million filtered SNPs for 27 spike morphology traits. We detect 132,086 significant marker-trait associations and the associated SNP markers are located within 590 associated peaks. We detect additional and stronger peaks by dividing spike morphology into sub-traits relative to GWAS results of spike morphology traits. We propose that the genetic dissection of spike morphology is a powerful strategy to detect signals for grain yield traits in wheat. The GWAS results reveal that TaSPL17 positively controls grain size and number by regulating spikelet and floret meristem development, which in turn leads to enhanced grain yield per plant. The haplotypes at TaSPL17 indicate geographical differentiation, domestication effects, and breeding selection.ConclusionOur study provides valuable resources for genetic improvement of spike morphology and a fast-forward genetic solution for candidate gene detection and cloning in wheat.

A common variant that alters SUN1 degradation associates with hepatic steatosis and metabolic traits in multiple cohorts

Non-alcoholic fatty liver disease (NAFLD), and its progressive form steatohepatitis (NASH), represent a genetically and phenotypically diverse entity for which there is no approved therapy, making it imperative to define the spectrum of pathways contributing to its pathogenesis. Rare variants in genes encoding nuclear envelope proteins cause lipodystrophy with early-onset NAFLD/NASH; we hypothesized that common variants in nuclear envelope-related genes might also contribute to hepatic steatosis and NAFLD. Using hepatic steatosis as the outcome of interest, we performed an association meta-analysis of nuclear envelope-related coding variants in three large discovery cohorts (N >120,000 participants), followed by phenotype association studies in large validation cohorts (N >600,000) and functional testing of the top steatosis-associated variant in cell culture. A common protein-coding variant, rs6461378 (SUN1 H118Y), was the top steatosis-associated variant in our association meta-analysis (p <0.001). In ancestrally distinct validation cohorts, rs6461378 associated with histologic NAFLD and with NAFLD-related metabolic traits including increased serum fatty acids, type 2 diabetes, hypertension, cardiovascular disease, and decreased HDL. SUN1 H118Y was subject to increased proteasomal degradation relative to wild-type SUN1 in cells, and SUN1 H118Y-expressing cells exhibited insulin resistance and increased lipid accumulation. Collectively, these data support a potential causal role for the common SUN1 variant rs6461378 in NAFLD and metabolic disease. Non-alcoholic fatty liver disease (NAFLD), with an estimated global prevalence of nearly 30%, is a growing cause of morbidity and mortality for which there is no approved pharmacologic therapy. Our data provide a rationale for broadening current concepts of NAFLD genetics and pathophysiology to include the nuclear envelope, and particularly Sad1 and UNC84 domain containing 1 (SUN1), as novel contributors to this common liver disease. Furthermore, if future studies confirm causality of the common SUN1 H118Y variant, it has the potential to become a broadly relevant therapeutic target in NAFLD and metabolic disease.

A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics

There is strong evidence showing that joint analysis of multiple phenotypes in genome-wide association studies (GWAS) can increase statistical power when detecting the association between genetic variants and human complex diseases. We previously developed the Clustering Linear Combination (CLC) method and a computationally efficient CLC (ceCLC) method to test the association between multiple phenotypes and a genetic variant, which perform very well. However, both of these methods require individual-level genotypes and phenotypes that are often not easily accessible. In this research, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. We use the LD score regression to estimate the correlation matrix among phenotypes. The test statistic of sCLC is constructed by GWAS summary statistics and has an approximate Cauchy distribution. We perform a variety of simulation studies and compare sCLC with other commonly used methods for multiple phenotype association studies using GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. Moreover, we apply the newly developed method to the UK Biobank GWAS summary statistics from the XIII category with 70 related musculoskeletal system and connective tissue phenotypes. The results demonstrate that sCLC detects the most number of significant SNPs, and most of these identified SNPs can be matched to genes that have been reported in the GWAS catalog to be associated with those phenotypes. Furthermore, sCLC also identifies some novel signals that were missed by standard GWAS, which provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes.

Variable level of genetic dominance controls important agronomic traits in rice populations under water deficit condition.

Plant hybridization is an important breeding technique essential for producing a genotype (hybrid) with favorable traits (e.g., stress tolerance, pest resistance, high yield potential etc.) to increase agronomic, economic and commercial values. Studying of genetic dominance among the population helps to determine gene action, heritability and candidate gene selection for plant breeding program. Therefore, this investigation was aimed to evaluate gene action, heritability, genetic advance and heterosis of rice root, agronomic, and yield component traits under water deficit conditions. In this study, crossing was performed among the four different water-deficit tolerant rice genotypes to produce better hybrid (F1), segregating (F2) and back-cross (BC1 and BC2) populations. The Giza 178, WAB56-204, and Sakha104 × WAB56-104 populations showed the better physiological and agronomical performances, which provided better adaptability of the populations to water deficit condition. Additionally, the estimation of heterosis and heterobeltiosis of some quantitative traits in rice populations were also studied. The inheritance of all studied traits was influenced by additive gene actions. Dominance gene actions played a major role in controlling the genetic variance among studied traits in both crossed populations under well-watered and drought conditions. The additive × additive type of gene interactions was essential for the inheritance of root length, root/shoot ratio, 1,000-grain weight, and sterility % of two crossed populations under both conditions. On the contrary, the additive × dominance type of gene interactions was effective in the inheritance of all studied traits, except duration in Giza178 × Sakha106, and plant height in Sakha104 × WAB56-104 under water deficit condition. In both crosses, the dominance × dominance type of gene interactions was effective in the inheritance of root volume, root/shoot ratio, number of panicles/plant and 1,000-grain weight under both conditions. Moreover, dominance × dominance type of gene interaction played a major role in the inheritance of root length, number of roots/plant, plant height, panicle length, number of filled grain/panicle and grain yield/plant in Giza178 × Sakha106 under both conditions. The studied traits in both crossed populations indicated better genetic advance as they showed advanced qualitative and quantitative characters in rice populations under water deficit condition. Overall, our findings open a new avenue of future phenotypic and genotypic association studies in rice. These insights might be useful to the plant breeders and farmers for developing water deficit tolerant rice cultivars.

Genetic risk score for common obesity and anthropometry in Spanish schoolchildren

IntroductionCommon or non-syndromic obesity is a complex polygenic trait conditioned by biallelic or single-base polymorphisms called SNPs (Single-Nucleotide Polymorphisms) that present an additive effect and act synergistically. Most genotype-obese phenotype association studies include body mass index (BMI) or waist-to-height ratio (WtHR), and very few introduce a broad anthropometric profile.ObjectiveTo verify whether a genetic risk score (GRS) developed from 10 SNPs is associated with the obesity phenotype assessed from anthropometric measures indicative of excess weight, adiposity and fat distribution.Material and methodsA series of 438 Spanish schoolchildren (6–16 years old) were evaluated anthropometrically (weight, height, waist circumference, skinfold thickness, BMI, WtHR, body fat percentage [%BF]). Ten SNPs were genotyped from saliva samples, generating a GRS for obesity, establishing genotype-phenotype association.ResultsSchoolchildren categorised as obese by BMI, ICT and %BF had higher GRS than their non-obese peers. The prevalence of overweight and adiposity was higher in subjects with a GRS above the median. Similarly, between 11 and 16 years of age, all anthropometric variables presented higher averages.ConclusionsGRS estimated from the 10 SNPs can be a diagnostic tool for the potential risk of obesity in Spanish schoolchildren and could be useful from the preventive perspective.

Phenotypic trait association studies in brinjal upon drought stress

Eggplant is popularly known as poor man’s vegetable. With respect to present situation of climatic challenges, fruit yield of eggplant is reduced due to drought or moisture stresses. In view of this condition, an experiment was aimed to study character association between yield and yield components in eggplant. The resultant outcome from correlation analysis computed among nine eggplant characters indicated that traits like plant height and total plant length at harvesting, fruit length and number of fruits per plant significantly correlated with fruit yield per plant. Whereas, traits like plant height and total plant length observed at harvesting stage, number of days for flower initiation, number of primary branches, fruit length and average fruit weight were significantly associated with fruit yield per plant under moisture stressed condition.

Quantitative Analysis of Isoniazid and Its Four Primary Metabolites in Plasma of Tuberculosis Patients Using LC-MS/MS

Isoniazid and its metabolites are potentially associated with hepatotoxicity and treatment outcomes in patients who receive antituberculosis (TB) therapy. To further understand the pharmacokinetic profiles of these molecules, a method based on LC-MS/MS was developed to determine the concentration of these compounds in human plasma. Isoniazid, acetylisoniazid, and isonicotinic acid were directly analyzed, whereas hydrazine and acetylhydrazine were determined after derivatization using p-tolualdehyde. Chromatographic separation was conducted on reversed-phase C18 columns with gradient elution, and detection was carried out in multiple reaction monitoring mode. The calibration curves were linear with correlation coefficients (r) greater than 0.9947 for all analytes. The intra- and inter-day precision was less than 13.43%, and the accuracy ranged between 91.63 and 114.00%. The recovery and matrix effect of the analytes were also consistent (coefficient of variation was less than 9.36%). The developed method successfully quantified isoniazid and its metabolites in TB patients. The method has broad applications in clinical research, including isoniazid one-point-based therapeutic drug monitoring, genotype-phenotype association studies of isoniazid metabolic profile and isoniazid-induced hepatotoxicity, and the initial dose prediction of isoniazid using population pharmacokinetic modeling.

Puntuación de riesgo genético para la obesidad común y antropometría en escolares españoles

IntroductionCommon or non-syndromic obesity is a complex polygenic trait conditioned by biallelic or single-base polymorphisms called SNPs (Single-Nucleotide Polymorphisms) that present an additive effect and act synergistically. Most genotype-obese phenotype association studies include body mass index (BMI) or waist-to-height ratio (WtHR), and very few introduce a broad anthropometric profile. ObjectiveTo verify whether a genetic risk score (GRS) developed from 10SNPs is associated with the obesity phenotype assessed from anthropometric measures indicative of excess weight, adiposity and fat distribution. Material and methodsA series of 438 Spanish schoolchildren (6 to 16years old) were evaluated anthropometrically (weight, height, waist circumference, skinfold thickness, BMI, WtHR, body fat percentage [%BF]). Ten SNPs were genotyped from saliva samples, generating a GRS for obesity, establishing genotype-phenotype association. ResultsSchoolchildren categorised as obese by BMI, ICT and %BF had higher GRS than their non-obese peers. The prevalence of overweight and adiposity was higher in subjects with a GRS above the median. Similarly, between 11 and 16years of age, all anthropometric variables presented higher averages. ConclusionsGRS estimated from the 10SNPs can be a diagnostic tool for the potential risk of obesity in Spanish schoolchildren and could be useful from the preventive perspective.

Transcriptome-module phenotype association study implicates extracellular vesicles biogenesis in Plasmodium falciparum artemisinin resistance.

Plasmodium falciparum malaria is still an important disease in sub-Saharan Africa (sSA). Great strides have been made in its control spear-headed by artemisinin (ART)-based combination therapies (ACTs). However, concerns about the imminent spread of ART-resistant (ARTr) malaria parasites to sSA threaten gains already made. Attempts to mitigate this risk have highlighted the need to discover novel P. falciparum drug targets. Therefore, studies to deepen our understanding of the biology of P. falciparum are needed. The role of extracellular vesicles (EVs) in the biology of malaria parasites is not fully understood. Recently, the ART resistance-associated transcriptional profile has been reported to involve several biological processes connected to vesicular trafficking, proteotoxic stress, erythrocyte remodelling, and mitochondrial metabolism. We explored a role for EVs in developing the P. falciparum ARTr phenotype using bulk RNA sequencing of unsynchronized parasite cultures under untreated, 0.1% dimethyl sulfoxide and 700nM dihydroartemisinin treated conditions for six hours. As pathway and gene ontology analysis is limited in its curated knowledge repertoire on EVs biogenesis in P. falciparum, we used a modular (gene set) analysis approach to explore whether an EVs biogenesis module is associated with the ARTr phenotype in P. falciparum. We first generated well-defined EVs modules of interest and used statistical tools to determine differences in their expression among the parasite and treatment conditions. Then we used gene set enrichment analysis to determine the strength of the association between each EVs module of interest and the ARTr phenotype. This transcriptome-module phenotype association study (TMPAS) represents a well-powered approach to making meaningful discoveries out of bulk gene expression data. We identified four EVs module of interest and report that one module representing gene sets with correlated expression to PF3D7_1441800 – involved with EVs biogenesis in P. falciparum - is associated with the ARTr phenotype (R539T_DHA_treated versus R539T_untreated: normalized enrichment score (NES) = 1.1830174, FDR q-value < 0.25; C580R_DHA_treated versus C580R_untreated: NES = 1.2457103, FDR q-value < 0.25). PF3D7_1441800 has been reported to reduce EVs production when knocked out in P. falciparum. Altogether, our findings suggest a role for EVs in developing ART resistance and warrant further studies interrogating this association.

Phenotypic variation and genome-wide association studies of main culm panicle node number, maximum node production rate, and degree-days to heading in rice

BackgroundGrain yield is a complex trait that results from interaction between underlying phenotypic traits and climatic, edaphic, and biotic variables. In rice, main culm panicle node number (MCPNN; the node number on which the panicle is borne) and maximum node production rate (MNPR; the number of leaves that emerge per degree-day > 10°C) are primary phenotypic plant traits that have significant positive direct effects on yield-related traits. Degree-days to heading (DDTH), which has a significant positive effect on grain yield, is influenced by the interaction between MCPNN and MNPR. The objective of this research is to assess the phenotypic variation of MCPNN, MNPR, and DDTH in a panel of diverse rice accessions, determine regions in the rice genome associated with these traits using genome-wide association studies (GWAS), and identify putative candidate genes that control these traits.ResultsConsiderable variation was observed for the three traits in a 220-genotype diverse rice population. MCPNN ranged from 8.1 to 20.9 nodes in 2018 and from 9.9 to 21.0 nodes in 2019. MNPR ranged from 0.0097 to 0.0214 nodes/degree day > 10°C in 2018 and from 0.0108 to 0.0193 nodes/degree-day > 10°C in 2019. DDTH ranged from 713 to 2,345 degree-days > 10°C in 2018 and from 778 to 2,404 degree-days > 10°C in 2019.Thirteen significant (P < 2.91 x 10-7) trait-single nucleotide polymorphism (SNP) associations were identified using the multilocus mixed linear model for GWAS. Significant associations between MCPNN and three SNPs in chromosome 2 (S02_12032235, S02_11971745, and S02_12030176) were detected with both the 2018 and best linear unbiased prediction (BLUP) datasets. Nine SNPs in chromosome 6 (S06_1970442, S06_2310856, S06_2550351, S06_1968653, S06_2296852, S06_1968680, S06_1968681, S06_1970597, and S06_1970602) were significantly associated with MNPR in the 2019 dataset. One SNP in chromosome 11 (S11_29358169) was significantly associated with the DDTH in the BLUP dataset.ConclusionsThis study identifies SNP markers that are putatively associated with MCPNN, MNPR, and DDTH. Some of these SNPs were located within or near gene models, which identify possible candidate genes involved in these traits. Validation of the putative candidate genes through expression and gene editing analyses are necessary to confirm their roles in regulating MCPNN, MNPR, and DDTH. Identifying the underlying genetic basis for primary phenotypic traits MCPNN and MNPR could lead to the development of fast and efficient approaches for their estimation, such as marker-assisted selection and gene editing, which is essential in increasing breeding efficiency and enhancing grain yield in rice. On the other hand, DDTH is a resultant variable that is highly affected by nitrogen and water management, plant density, and several other factors.

A novel Synthetic phenotype association study approach reveals the landscape of association for genomic variants and phenotypes

IntroductionGenome-Wide Association Studies (GWAS) identify tagging variants in the genome that are statistically associated with the phenotype because of their linkage disequilibrium (LD) relationship with the causative mutation (CM). When both low-density genotyped accession panels with phenotypes and resequenced data accession panels are available, tagging variants can assist with post-GWAS challenges in CM discovery. ObjectivesOur objective was to identify additional GWAS evaluation criteria to assess correspondence between genomic variants and phenotypes, as well as enable deeper analysis of the localized landscape of association. MethodsWe used genomic variant positions as Synthetic phenotypes in GWAS that we named “Synthetic phenotype association study” (SPAS). The extreme case of SPAS is what we call an “Inverse GWAS” where we used CM positions of cloned soybean genes. We developed and validated the Accuracy concept as a measure of the correspondence between variant positions and phenotypes. ResultsThe SPAS approach demonstrated that the genotype status of an associated variant used as a Synthetic phenotype enabled us to explore the relationships between tagging variants and CMs, and further, that utilizing CMs as Synthetic phenotypes in Inverse GWAS illuminated the landscape of association. We implemented the Accuracy calculation for a curated accession panel to an online Accuracy calculation tool (AccuTool) as a resource for gene identification in soybean. We demonstrated our concepts on three examples of soybean cloned genes. As a result of our findings, we devised an enhanced “GWAS to Genes” analysis (Synthetic phenotype to CM strategy, SP2CM). Using SP2CM, we identified a CM for a novel gene. ConclusionThe SP2CM strategy utilizing Synthetic phenotypes and the Accuracy calculation of correspondence provides crucial information to assist researchers in CM discovery. The impact of this work is a more effective evaluation of landscapes of GWAS associations.

A Novel Hierarchical Clustering Approach for Joint Analysis of Multiple Phenotypes Uncovers Obesity Variants Based on ARIC.

Genome-wide association studies (GWASs) have successfully discovered numerous variants underlying various diseases. Generally, one-phenotype one-variant association study in GWASs is not efficient in identifying variants with weak effects, indicating that more signals have not been identified yet. Nowadays, jointly analyzing multiple phenotypes has been recognized as an important approach to elevate the statistical power for identifying weak genetic variants on complex diseases, shedding new light on potential biological mechanisms. Therefore, hierarchical clustering based on different methods for calculating correlation coefficients (HCDC) is developed to synchronously analyze multiple phenotypes in association studies. There are two steps involved in HCDC. First, a clustering approach based on the similarity matrix between two groups of phenotypes is applied to choose a representative phenotype in each cluster. Then, we use existing methods to estimate the genetic associations with the representative phenotypes rather than the individual phenotypes in every cluster. A variety of simulations are conducted to demonstrate the capacity of HCDC for boosting power. As a consequence, existing methods embedding HCDC are either more powerful or comparable with those of without embedding HCDC in most scenarios. Additionally, the application of obesity-related phenotypes from Atherosclerosis Risk in Communities via existing methods with HCDC uncovered several associated variants. Among these, UQCC1-rs1570004 is reported as a significant obesity signal for the first time, whose differential expression in subcutaneous fat, visceral fat, and muscle tissue is worthy of further functional studies.

Bayesian model comparison for rare-variant association studies

Whole-genome sequencing studies applied to large populations or biobanks with extensive phenotyping raise new analytic challenges. The need to consider many variants at a locus or group of genes simultaneously and the potential to study many correlated phenotypes with shared genetic architecture provide opportunities for discovery not addressed by the traditional one variant, one phenotype association study. Here, we introduce a Bayesian model comparison approach called MRP (multiple rare variants and phenotypes) for rare-variant association studies that considers correlation, scale, and direction of genetic effects across a group of genetic variants, phenotypes, and studies, requiring only summary statistic data. We apply our method to exome sequencing data (n = 184,698) across 2,019 traits from the UK Biobank, aggregating signals in genes. MRP demonstrates an ability to recover signals such as associations between PCSK9 and LDL cholesterol levels. We additionally find MRP effective in conducting meta-analyses in exome data. Non-biomarker findings include associations between MC1R and red hair color and skin color, IL17RA and monocyte count, and IQGAP2 and mean platelet volume. Finally, we apply MRP in a multi-phenotype setting; after clustering the 35 biomarker phenotypes based on genetic correlation estimates, we find that joint analysis of these phenotypes results in substantial power gains for gene-trait associations, such as in TNFRSF13B in one of the clusters containing diabetes- and lipid-related traits. Overall, we show that the MRP model comparison approach improves upon useful features from widely used meta-analysis approaches for rare-variant association analyses and prioritizes protective modifiers of disease risk.

The eigen higher criticism and eigen Berk-Jones tests for multiple trait association studies based on GWAS summary statistics.

In this article, we propose the eigen higher criticism and the eigen Berk-Jones testing procedures to test the association between a single genetic variant and multiple correlated traits based on summary statistics from single-trait genome-wide association studies. Since the association pattern between each genetic variant and multiple traits varies across the whole genome, we further develop an omnibus (OMNI) test using the aggregated Cauchy association test to achieve more robust performance. The p values of our proposed tests can be computed analytically, thus, our methods are appealing in large-scale multiple phenotype association studies. Through extensive simulation studies, we found that all of our proposed tests can maintain the correct type I error rates and our proposed tests have greater power in certain settings. In addition, the OMNI test can always provide robust power performance across a wide range of scenarios. We apply the proposed tests to the Global Lipids Genetics Consortium summary statistics data set and identify additional genetic variants that were missed by the original single-trait analyses. We also develop an R package EBMMT publicly available at https://github.com/Vivian-Liu-Wei64/EBMMT.