scholarly journals Gene Expression and RNA Splicing Imputation Identifies Novel Candidate Genes Associated with Osteoporosis

2020 ◽  
Vol 105 (12) ◽  
pp. e4742-e4757
Author(s):  
Yong Liu ◽  
Hui Shen ◽  
Jonathan Greenbaum ◽  
Anqi Liu ◽  
Kuan-Jui Su ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Mineral Density ◽  
Mendelian Randomization Analysis ◽  
Trait Loci ◽  
Randomization Analysis

Abstract Context Though genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with osteoporosis related traits, such as bone mineral density (BMD) and fracture, it remains a challenge to interpret their biological functions and underlying biological mechanisms. Objective Integrate diverse expression quantitative trait loci and splicing quantitative trait loci data with several powerful GWAS datasets to identify novel candidate genes associated with osteoporosis. Design, Setting, and Participants Here, we conducted a transcriptome-wide association study (TWAS) for total body BMD (TB-BMD) (n = 66 628 for discovery and 7697 for validation) and fracture (53 184 fracture cases and 373 611 controls for discovery and 37 857 cases and 227 116 controls for validation), respectively. We also conducted multi-SNP-based summarized mendelian randomization analysis to further validate our findings. Results In total, we detected 88 genes significantly associated with TB-BMD or fracture through expression or ribonucleic acid splicing. Summarized mendelian randomization analysis revealed that 78 of the significant genes may have potential causal effects on TB-BMD or fracture in at least 1 specific tissue. Among them, 64 genes have been reported in previous GWASs or TWASs for osteoporosis, such as ING3, CPED1, and WNT16, as well as 14 novel genes, such as DBF4B, GRN, TMUB2, and UNC93B1. Conclusions Overall, our findings provide novel insights into the pathogenesis mechanisms of osteoporosis and highlight the power of a TWAS to identify and prioritize potential causal genes.

scholarly journals ОБЗОР ЗНАЧИМЫХ ЛОКУСОВ КОЛИЧЕСТВЕННЫХ ПРИЗНАКОВ, АССОЦИИРОВАННЫХ С КАЧЕСТВОМ СКОРЛУПЫ ЯИЦ КУР

2021 ◽  
Vol 70 (1) ◽  
Keyword(s):  
Quantitative Trait Loci ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Egg Production ◽  
Egg Quality ◽  
Association Studies ◽  
Published Data ◽  
Genome Wide Association Studies ◽  
Single Nucleotide ◽  
Trait Loci

Баркова О.Ю. КЛЮЧЕВЫЕ СЛОВА: ЛОКУСЫ КОЛИЧЕСТВЕННЫХ ПРИЗНАКОВ (QTL), ОДНОНУКЛЕОТИДНЫЙ ПОЛИМОРФИЗМ (SNP), КУРЫ, КАЧЕСТВО ЯИЦ, МАССА СКОРЛУПЫ, ПРОЧНОСТЬ СКОРЛУПЫ Рассмотрены последние данные исследований молекулярной генетики кур, которые могут способствовать повышению яичной продуктивности птицы отечественной селекции. Современные технологии селекции используют ДНК-маркеры, которые помогают идентифицировать локусы количественных признаков (QTL), связанные с признаками яйценоскости. Маркерная селекция может значительно ускорить процесс селекции птицы по хозяйственно-полезным признакам. Идентификация многочисленных однонуклеотидных полиморфизмов (SNP) в геномах животных, прогресс в области высокопроизводительного секвенирования, разработка вычислительных методов анализа данных SNP, выполняемых с помощью массивов высокой плотности, позволили использовать их в геномном картографировании генов-кандидатов. В данной работе проанализированы литературные данные, полученные при помощи полногеномного поиска ассоциаций (GWAS) для отбора QTL и генов-кандидатов, влияющих на яйценоскость, массу, прочность и толщину яичной скорлупы для дальнейшего создания системы QTL, отвечающей за яичную продуктивность кур-несушек и качество яиц. OVERVIEW OF SIGNIFICANT QUANTITATIVE TRAIT LOCI ASSOCIATED WITH EGGSHELL QUALITY IN CHICKEN BARKOVA O.YU.1 1 Federal Scientific Center for Animal Husbandry of L.K. Ernst The recent data on molecular genetics of chicken are reviewed which can contribute to the improvement of egg productivity in domestically selected chicken. Current selection strategies involve DNA markers to identify quantitative traits loci (QTL) associated with egg productivity; marker assisted techniques can significantly accelerate the selection for economically valuable traits. The identification of numerous single-nucleotide polymorphisms (SNP) in animal genomes, progress in high-performance sequencing, and the development of computational methods for analysis of SNP data using high-density arrays have allowed for the use of SNP in genomic mapping of candidate genes. In the study presented the published data of the genome-wide association studies (GWAS) aimed at the identification of QTL and candidate genes which affect egg production, weight, thickness and strength of the eggshell in chicken are reviewed; these data will be used for further development of the QTL system responsible for egg production and quality traits in laying hens. Keywords: QUANTITATIVE TRAIT LOCI, SINGLE-NUCLEOTIDE POLYMORPHISM, CHICKEN, EGG QUALITY, EGGSHELL WEIGHT, EGGSHELL STRENGTH

scholarly journals Identification, deployment, and transferability of quantitative trait loci from genome-wide association studies in plants

2020 ◽  
Vol 24 ◽  
pp. 100145 ◽  
Author(s):  
Mohsen Mohammadi ◽  
Alencar Xavier ◽  
Travis Beckett ◽  
Savannah Beyer ◽  
Liyang Chen ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Trait Loci

ADDO: a comprehensive toolkit to detect, classify and visualize additive and non-additive quantitative trait loci

2019 ◽  
Vol 36 (5) ◽  
pp. 1517-1521
Author(s):  
Leilei Cui ◽  
Bin Yang ◽  
Nikolas Pontikos ◽  
Richard Mott ◽  
Lusheng Huang
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Association Studies ◽  
Genetic Effects ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Additive Effects ◽  
A Genome ◽  
Trait Loci ◽  
Additive Genetic Effects

Abstract Motivation During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked. Results We developed ADDO, a highly efficient tool to detect, classify and visualize QTLs with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single-nucleotide polymorphism effects as either additive, partial dominant, dominant or over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 h with 10 CPUs. Analysis of simulated data confirms ADDO’s performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects. Availability and implementation ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Multi-trait GWAS using high-density genotypes identifies genes associated with body traits in Nile tilapia

2020 ◽  
Author(s):  
Grazyella Yoshida ◽  
José Manuel Yáñez
Keyword(s):  
Quantitative Trait Loci ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Nile Tilapia ◽  
Statistical Power ◽  
Association Studies ◽  
Genotype Imputation ◽  
Genome Wide Association ◽  
Genome Wide ◽  
Trait Loci

Abstract Background: Body traits are generally controlled by several genes in vertebrates (i.e. polygenes), which in turn make them difficult to identify through association mapping. Increasing the power of association studies by combining approaches such as genotype imputation and multi-trait analysis improves the ability to detect quantitative trait loci associated with polygenic traits, such as body traits. Results: A multi-trait genome-wide association study (mtGWAS) was performed to identify quantitative trait loci (QTL) and genes associated with body traits in Nile tilapia (Oreochromos niloticus) using genotypes imputed to whole-genome sequence (WGS). To increase the statistical power of mtGWAS for the detection of genetic associations, summary statistics from single-trait genome-wide association studies (stGWAS) for eight different body traits recorded in 1,309 animals were used. The mtGWAS increased the statistical power from the original sample size from 13% to 44%, depending on the trait analyzed. The better resolution of the WGS data combined with the increased power of the mtGWAS approach, allowed the detection of significant markers not previously found in the stGWAS. Some lead single nucleotide polymorphisms (SNPs) were found within important functional candidate genes previously associated with growth-related traits. For instance, we identified SNP within the α1,6-fucosyltransferase (FUT8), solute carrier family 4 member 2 (SLC4A2), A disintegrin and metalloproteinase with thrombospondin motifs 9 (ADAMTS9) and heart development protein with EGF like domains 1 (HEG1) genes, which have been associated with average daily gain in sheep, osteopetrosis in cattle, chest size in goats, and growth and meat quality in sheep, respectively. Conclusions: The high-resolution mtGWAS presented, allowed identification of significant SNPs, linked to strong functional candidate genes, associated with body traits in Nile tilapia. These results provide further insights about the genetic variants and genes underlying body trait variation in cichlid fish with high accuracy and strong statistical support.

scholarly journals Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids

2017 ◽  
Author(s):  
Fanny Bonnafous ◽  
Ghislain Fievet ◽  
Nicolas Blanchet ◽  
Marie-Claude Boniface ◽  
Sébastien Carrère ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Flowering Time ◽  
Genetic Control ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Association Studies ◽  
Additive Models ◽  
Genome Wide Association Studies ◽  
Trait Loci ◽  
Genomic Regions

AbstractGenome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

scholarly journals Identification of putative effector genes across the GWAS Catalog using molecular quantitative trait loci from 68 tissues and cell types

2019 ◽  
Author(s):  
Cong Guo ◽  
Karsten B. Sieber ◽  
Jorge Esparza-Gordillo ◽  
Mark R. Hurle ◽  
Kijoung Song ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Effector Genes ◽  
Genome Wide ◽  
Trait Loci ◽  
Gwas Catalog

AbstractIdentifying the effector genes from genome-wide association studies (GWAS) is a crucial step towards understanding the biological mechanisms underlying complex traits and diseases. Colocalization of expression and protein quantitative trait loci (eQTL and pQTL, hereafter collectively called “xQTL”) can be effective for mapping associations to genes in many loci. However, existing colocalization methods require full single-variant summary statistics which are often not readily available for many published GWAS or xQTL studies. Here, we present PICCOLO, a method that uses minimum SNP p-values within a locus to determine if pairs of genetic associations are colocalized. This method greatly expands the number of GWAS and xQTL datasets that can be tested for colocalization. We applied PICCOLO to 10,759 genome-wide significant associations across the NHGRI-EBI GWAS Catalog with xQTLs from 28 studies. We identified at least one colocalized gene-xQTL in at least one tissue for 30% of associations, and we pursued multiple lines of evidence to demonstrate that these mappings are biologically meaningful. PICCOLO genes are significantly enriched for biologically relevant tissues, and 4.3-fold enriched for targets of approved drugs.

scholarly journals Multi-trait GWAS using imputed high-density genotypes from whole-genome sequencing identifies genes associated with body traits in Nile tilapia

2020 ◽  
Author(s):  
Grazyella Yoshida ◽  
José Manuel Yáñez
Keyword(s):  
Quantitative Trait Loci ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Nile Tilapia ◽  
Statistical Power ◽  
Association Studies ◽  
Genome Wide Association ◽  
Whole Genome ◽  
Genome Wide ◽  
Trait Loci

Abstract Background: Body traits are generally controlled by several genes in vertebrates (i.e. polygenes), which in turn make them difficult to identify through association mapping. Increasing the power of association studies by combining approaches such as genotype imputation and multi-trait analysis improves the ability to detect quantitative trait loci associated with polygenic traits, such as body traits. Results: A multi-trait genome-wide association study (mtGWAS) was performed to identify quantitative trait loci (QTL) and genes associated with body traits in Nile tilapia (Oreochromos niloticus) using genotypes imputed to whole-genome sequence (WGS). To increase the statistical power of mtGWAS for the detection of genetic associations, summary statistics from single-trait genome-wide association studies (stGWAS) for eight different body traits recorded in 1,309 animals were used. The mtGWAS increased the statistical power from the original sample size from 13% to 44%, depending on the trait analyzed. The better resolution of the WGS data combined with the increased power of the mtGWAS approach, allowed the detection of significant markers not previously found in the stGWAS. Some lead single nucleotide polymorphisms (SNPs) were found within important functional candidate genes previously associated with growth-related traits. For instance, we identified SNP within the α1,6-fucosyltransferase (FUT8), solute carrier family 4 member 2 (SLC4A2), A disintegrin and metalloproteinase with thrombospondin motifs 9 (ADAMTS9) and heart development protein with EGF like domains 1 (HEG1) genes, which have been associated with average daily gain in sheep, osteopetrosis in cattle, chest size in goats, and growth and meat quality in sheep, respectively. Conclusions: The high-resolution mtGWAS presented, allowed identification of significant SNPs, linked to strong functional candidate genes, associated with body traits in Nile tilapia. These results provide further insights about the genetic variants and genes underlying body trait variation in cichlid fish with high accuracy and strong statistical support.

scholarly journals Multi-trait GWAS using imputed high-density genotypes from whole-genome sequencing identifies genes associated with body traits in Nile tilapia

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Grazyella M. Yoshida ◽  
José M. Yáñez
Keyword(s):  
Quantitative Trait Loci ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Nile Tilapia ◽  
Statistical Power ◽  
Association Studies ◽  
Genome Wide Association ◽  
Whole Genome ◽  
Genome Wide ◽  
Trait Loci

Abstract Background Body traits are generally controlled by several genes in vertebrates (i.e. polygenes), which in turn make them difficult to identify through association mapping. Increasing the power of association studies by combining approaches such as genotype imputation and multi-trait analysis improves the ability to detect quantitative trait loci associated with polygenic traits, such as body traits. Results A multi-trait genome-wide association study (mtGWAS) was performed to identify quantitative trait loci (QTL) and genes associated with body traits in Nile tilapia (Oreochromis niloticus) using genotypes imputed to whole-genome sequences (WGS). To increase the statistical power of mtGWAS for the detection of genetic associations, summary statistics from single-trait genome-wide association studies (stGWAS) for eight different body traits recorded in 1309 animals were used. The mtGWAS increased the statistical power from the original sample size from 13 to 44%, depending on the trait analyzed. The better resolution of the WGS data, combined with the increased power of the mtGWAS approach, allowed the detection of significant markers which were not previously found in the stGWAS. Some of the lead single nucleotide polymorphisms (SNPs) were found within important functional candidate genes previously associated with growth-related traits in other terrestrial species. For instance, we identified SNP within the α1,6-fucosyltransferase (FUT8), solute carrier family 4 member 2 (SLC4A2), A disintegrin and metalloproteinase with thrombospondin motifs 9 (ADAMTS9) and heart development protein with EGF like domains 1 (HEG1) genes, which have been associated with average daily gain in sheep, osteopetrosis in cattle, chest size in goats, and growth and meat quality in sheep, respectively. Conclusions The high-resolution mtGWAS presented here allowed the identification of significant SNPs, linked to strong functional candidate genes, associated with body traits in Nile tilapia. These results provide further insights about the genetic variants and genes underlying body trait variation in cichlid fish with high accuracy and strong statistical support.

scholarly journals Causal Association of Trauma With Subsequent Psychiatric Disorder: A Mendelian Randomization Study

2021 ◽  
Author(s):  
Dongqing Gu ◽  
Shan Ou ◽  
Guodong Liu
Keyword(s):  
Psychiatric Disorder ◽  
Mood Disorder ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Causal Association ◽  
Post Traumatic Stress ◽  
Mendelian Randomization Analysis ◽  
Increased Risk ◽  
Randomization Analysis

Abstract Objective Trauma has been proposed as a risk factor for the development of psychiatric disorder. This study aimed to determine the causal association between them. Methods Two-sample Mendelian randomization analyses were performed to estimate the causal association between trauma and psychiatric disorder. We obtained summary-level data for genetic variants associated with trauma and the corresponding association with psychiatric disorder from previous genome-wide association studies, and inverse variance weighted was used as the main method in our Mendelian randomization analysis. Results Genetically predisposed trauma was associated with an increased risk of psychiatric disorder (odds ratio [OR] = 1.02, 95% confidence interval [CI], 1.01–1.02,), mood disorder (OR = 1.01, 95% CI, 1.00-1.01) and depression (OR = 1.02, 95% CI, 1.01–1.02) in UK Biobank, as well as increased risk of mood disorder (OR = 1.23, 95% CI, 1.03–1.48), depression (OR = 1.10, 95% CI, 1.04–1.17), bipolar disorder (OR = 1.24, 95% CI, 1.04–1.49) and schizophrenia (OR = 1.47, 95% CI, 1.21–1.78) in data source from MR Base. However, Mendelian randomization evidence did not support an association between trauma and risk of post-traumatic stress disorder, anxiety disorder, sleep disorder, and eating disorder. Conclusions Findings from our Mendelian randomization analysis suggested that trauma might be causally associated with an increased risk of some common psychiatric disorder such as depression.

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