scholarly journals Genetic Variability of Methane Production and Concentration Measured in the Breath of Polish Holstein-Friesian Cattle

Animals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 3175
Author(s):  
Mateusz Sypniewski ◽  
Tomasz Strabel ◽  
Marcin Pszczola
Keyword(s):  
Genetic Variance ◽  
Association Studies ◽  
Single Step ◽  
Candidate Snps ◽  
Genome Wide Association Studies ◽  
Potential Candidate ◽  
Ch4 Production ◽  
Holstein Friesian ◽  
Commercial Farms ◽  
Carbon Dioxide Co2

The genetic architecture of methane (CH4) production remains largely unknown. We aimed to estimate its heritability and to perform genome-wide association studies (GWAS) for the identification of candidate genes associated with two phenotypes: CH4 in parts per million/day (CH4 ppm/d) and CH4 in grams/day (CH4 g/d). We studied 483 Polish Holstein-Friesian cows kept on two commercial farms in Poland. Measurements of CH4 and carbon dioxide (CO2) concentrations exhaled by cows during milking were obtained using gas analyzers installed in the automated milking system on the farms. Genomic analyses were performed using a single-step BLUP approach. The percentage of genetic variance explained by SNPs was calculated for each SNP separately and then for the windows of neighbouring SNPs. The heritability of CH4 ppm/d ranged from 0 to 0.14, with an average of 0.085. The heritability of CH4 g/d ranged from 0.13 to 0.26, with an average of 0.22. The GWAS detected potential candidate SNPs on BTA 14 which explained ~0.9% of genetic variance for CH4 ppm/d and ~1% of genetic variance for CH4 g/d. All identified SNPs were located in the TRPS1 gene. We showed that methane traits are partially controlled by genes; however, the detected SNPs explained only a small part of genetic variation—implying that both CH4 ppm/d and CH4 g/d are highly polygenic traits.

Genome-Wide Association Studies (GWAS) for Traits Related to Fodder Quality and Biofuel in Sorghum: Progress and Prospects

2021 ◽  
Vol 28 ◽  
Author(s):  
Vinutha Kanuganahalli Somegowda ◽  
Laavanya Rayaprolu ◽  
Abhishek Rathore ◽  
Santosh Pandurang Deshpande ◽  
Rajeev Gupta
Keyword(s):  
Genetic Architecture ◽  
Association Studies ◽  
Current Status ◽  
Genome Wide Association Studies ◽  
Potential Candidate ◽  
Crop Species ◽  
Fodder Quality ◽  
Genome Wide ◽  
Genomic Regions ◽  
Model Crop

: The main focus of this review is to discuss the current status of the use of GWAS for fodder quality and biofuel owing to its similarity of traits. Sorghum is a potential multipurpose crop, popularly cultivated for various uses as food, feed fodder, and biomass for ethanol. Production of a huge quantity of biomass and genetic variation for complex sugars are the main motivation not only to use sorghum as fodder for livestock nutritionists but also a potential candidate for biofuel generation. Few studies have been reported on the knowledge transfer that can be used from the development of biofuel technologies to complement improved fodder quality and vice versa. With recent advances in genotyping technologies, GWAS became one of the primary tools used to identify the genes/genomic regions associated with the phenotype. These modern tools and technologies accelerate the genomic assisted breeding process to enhance the rate of genetic gains. Hence, this mini-review focuses on GWAS studies on genetic architecture and dissection of traits underpinning fodder quality and biofuel traits and their limited comparison with other related model crop species.

scholarly journals Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Abiskar Gyawali ◽  
Vivek Shrestha ◽  
Katherine E. Guill ◽  
Sherry Flint-Garcia ◽  
Timothy M. Beissinger
Keyword(s):  
Plant Height ◽  
Bulk Segregant Analysis ◽  
Association Studies ◽  
Significant Snps ◽  
Rapid Identification ◽  
Candidate Snps ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Plant ◽  
Crop Species

Abstract Background Genome wide association studies (GWAS) are a powerful tool for identifying quantitative trait loci (QTL) and causal single nucleotide polymorphisms (SNPs)/genes associated with various important traits in crop species. Typically, GWAS in crops are performed using a panel of inbred lines, where multiple replicates of the same inbred are measured and the average phenotype is taken as the response variable. Here we describe and evaluate single plant GWAS (sp-GWAS) for performing a GWAS on individual plants, which does not require an association panel of inbreds. Instead sp-GWAS relies on the phenotypes and genotypes from individual plants sampled from a randomly mating population. Importantly, we demonstrate how sp-GWAS can be efficiently combined with a bulk segregant analysis (BSA) experiment to rapidly corroborate evidence for significant SNPs. Results In this study we used the Shoepeg maize landrace, collected as an open pollinating variety from a farm in Southern Missouri in the 1960’s, to evaluate whether sp-GWAS coupled with BSA can efficiently and powerfully used to detect significant association of SNPs for plant height (PH). Plant were grown in 8 locations across two years and in total 768 individuals were genotyped and phenotyped for sp-GWAS. A total of 306 k polymorphic markers in 768 individuals evaluated via association analysis detected 25 significant SNPs (P ≤ 0.00001) for PH. The results from our single-plant GWAS were further validated by bulk segregant analysis (BSA) for PH. BSA sequencing was performed on the same population by selecting tall and short plants as separate bulks. This approach identified 37 genomic regions for plant height. Of the 25 significant SNPs from GWAS, the three most significant SNPs co-localize with regions identified by BSA. Conclusion Overall, this study demonstrates that sp-GWAS coupled with BSA can be a useful tool for detecting significant SNPs and identifying candidate genes. This result is particularly useful for species/populations where association panels are not readily available.

Association of germ-line single nucleotide polymorphisms (SNPs) with pathologic response to neoadjuvant cisplatin-based chemotherapy in patients with resectable non-small cell lung cancers.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 7558-7558
Author(s):  
Jamie E. Chaft ◽  
Sohela Shah ◽  
Esther N. Drill ◽  
Camelia S. Sima ◽  
Vijai Joseph ◽  
...  
Keyword(s):  
Neoadjuvant Chemotherapy ◽  
Association Studies ◽  
Predictive Biomarkers ◽  
Pathologic Response ◽  
North American Population ◽  
Candidate Snps ◽  
Genome Wide Association Studies ◽  
Lung Cancers ◽  
Study Results ◽  
Response To Neoadjuvant Chemotherapy

7558 Background: Pathologic response to neoadjuvant chemotherapy correlates with survival in resected lung cancers. Predictive biomarkers of response are needed. Tumor-specific biomarkers have been hindered by reproducibility and specimen adequacy. Genome-wide association studies (most in SE Asia) have identified candidate SNPs that correlate with clinical outcomes after cisplatin-based chemotherapy. We evaluated whether these candidate SNPs are predictive of pathologic response to neoadjuvant chemotherapy in a US population. Methods: We aimed to correlate SNPs with near complete PR (ncPR) to neoadjuvant cisplatin + docetaxel in patients with resectable lung cancers. ncPR was defined as 90% necrosis, inflammation and fibrosis across serial 1cm sections of the resected tumor. Germline DNA was extracted and 50 candidate SNPs were genotyped by Sequenom Mass ARRAY iPLEX. SNPs were analyzed for correlation with ncPR using recessive (aa vs AA/aA) , dominant (AA vs aa/aA) and log-additive (linear increase in risk with each additional allele) genetic models. In this exploratory dataset, a p-value <0.1 was considered worthy of further study. Results: 60 patients were treated and had sufficient DNA for analysis. Nine patients had a ncPR. 7 of 50 SNPs (table) correlated with pathologic response in one of the three models. Conclusions: Of the candidate SNPs identified from the literature, 7 showed a promising correlation to ncPR to neoadjuvant chemotherapy. This expands upon the experience evaluating SNPs and chemotherapy sensitivity into a North American population. Study of these 7 SNPs is ongoing in our current multimodality trial as a validation cohort. This study was funded by the Lung Cancer Research Foundation. [Table: see text]

scholarly journals Combined Analysis with Copy Number Variation Identifies Risk Loci in Lung Cancer

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xinlei Li ◽  
Xianfeng Chen ◽  
Guohong Hu ◽  
Yang Liu ◽  
Zhenguo Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Copy Number ◽  
Association Studies ◽  
Copy Number Variations ◽  
Genome Wide Association Studies ◽  
Potential Candidate ◽  
Cancer Etiology ◽  
Combined Analysis ◽  
Recombination Hotspots ◽  
Underlying Mechanisms

Background. Lung cancer is the most important cause of cancer mortality worldwide, but the underlying mechanisms of this disease are not fully understood. Copy number variations (CNVs) are promising genetic variations to study because of their potential effects on cancer.Methodology/Principal Findings. Here we conducted a pilot study in which we systematically analyzed the association of CNVs in two lung cancer datasets: the Environment And Genetics in Lung cancer Etiology (EAGLE) and the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial datasets. We used a preestablished association method to test the datasets separately and conducted a combined analysis to test the association accordance between the two datasets. Finally, we identified 167 risk SNP loci and 22 CNVs associated with lung cancer and linked them with recombination hotspots. Functional annotation and biological relevance analyses implied that some of our predicted risk loci were supported by other studies and might be potential candidate loci for lung cancer studies.Conclusions/Significance. Our results further emphasized the importance of copy number variations in cancer and might be a valuable complement to current genome-wide association studies on cancer.

scholarly journals Genome-wide interaction analysis of quantitative traits in outbred mice

2015 ◽  
Vol 97 ◽  
Author(s):  
WEIJUN MA ◽  
CHAOFENG YUAN ◽  
HAIDONG LIU ◽  
WEI ZHENG ◽  
YING ZHOU
Keyword(s):  
Interaction Analysis ◽  
Association Studies ◽  
Candidate Snps ◽  
Single Pair ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Two Stage ◽  
Gene Pairs ◽  
Genome Wide ◽  
Outbred Mice

SummaryWith a large number of quantitative trait loci being identified in genome-wide association studies, researchers have become more interested in detecting interactions among genes or single nucleotide polymorphisms (SNPs). In this research, we carried out a two-stage model selection procedure to detect interacting gene pairs or SNP pairs associated with four important traits of outbred mice, including glucose, high-density lipoprotein cholesterol, diastolic blood pressure and triglyceride. In the first stage, a variance heterogeneity test was used to screen for candidate SNPs. In the second stage, the Lasso method and single pair analysis were used to select two-way interactions. Moreover, the shared Gene Ontology information about the selected interacting gene pairs was considered to study the interactions auxiliarily. Based on this method, we not only replicated the identification of important SNPs associated with each trait of outbred mice, but also found some SNP pairs and gene pairs with significant interaction effects on each trait. Simulation studies were also conducted to evaluate the performance of the two-stage method in different situations.

scholarly journals Gene action, genetic variation, and GWAS: A user-friendly web tool

PLoS Genetics ◽  
2021 ◽  
Vol 17 (5) ◽  
pp. e1009548
Author(s):  
Valentin Hivert ◽  
Naomi R. Wray ◽  
Peter M. Visscher
Keyword(s):  
Individual Differences ◽  
Genetic Variance ◽  
Gene Action ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association Studies ◽  
Web Tool ◽  
Additive Variance ◽  
Specific Effects ◽  
User Friendly

Fisher’s partitioning of genotypic values and genetic variance is highly relevant in the current era of genome-wide association studies (GWASs). However, despite being more than a century old, a number of persistent misconceptions related to nonadditive genetic effects remain. We developed a user-friendly web tool, the Falconer ShinyApp, to show how the combination of gene action and allele frequencies at causal loci translate to genetic variance and genetic variance components for a complex trait. The app can be used to demonstrate the relationship between a SNP effect size estimated from GWAS and the variation the SNP generates in the population, i.e., how locus-specific effects lead to individual differences in traits. In addition, it can also be used to demonstrate how within and between locus interactions (dominance and epistasis, respectively) usually do not lead to a large amount of nonadditive variance relative to additive variance, and therefore, that these interactions usually do not explain individual differences in a population.

scholarly journals Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Enrico Mancin ◽  
Daniela Lourenco ◽  
Matias Bermann ◽  
Roberto Mantovani ◽  
Ignacy Misztal
Keyword(s):  
Population Structure ◽  
Beef Cattle ◽  
Dairy Cattle ◽  
Genetic Relatedness ◽  
Association Studies ◽  
Single Step ◽  
Structured Populations ◽  
Farm Animals ◽  
Snp Analysis ◽  
Genome Wide Association Studies

Population structure or genetic relatedness should be considered in genome association studies to avoid spurious association. The most used methods for genome-wide association studies (GWAS) account for population structure but are limited to genotyped individuals with phenotypes. Single-step GWAS (ssGWAS) can use phenotypes from non-genotyped relatives; however, its ability to account for population structure has not been explored. Here we investigate the equivalence among ssGWAS, efficient mixed-model association expedited (EMMAX), and genomic best linear unbiased prediction GWAS (GBLUP-GWAS), and how they differ from the single-SNP analysis without correction for population structure (SSA-NoCor). We used simulated, structured populations that mimicked fish, beef cattle, and dairy cattle populations with 1040, 5525, and 1,400 genotyped individuals, respectively. Larger populations were also simulated that had up to 10-fold more genotyped animals. The genomes were composed by 29 chromosomes, each harboring one QTN, and the number of simulated SNPs was 35,000 for the fish and 65,000 for the beef and dairy cattle populations. Males and females were genotyped in the fish and beef cattle populations, whereas only males had genotypes in the dairy population. Phenotypes for a trait with heritability varying from 0.25 to 0.35 were available in both sexes for the fish population, but only for females in the beef and dairy cattle populations. In the latter, phenotypes of daughters were projected into genotyped sires (i.e., deregressed proofs) before applying EMMAX and SSA-NoCor. Although SSA-NoCor had the largest number of true positive SNPs among the four methods, the number of false negatives was two–fivefold that of true positives. GBLUP-GWAS and EMMAX had a similar number of true positives, which was slightly smaller than in ssGWAS, although the difference was not significant. Additionally, no significant differences were observed when deregressed proofs were used as pseudo-phenotypes in EMMAX compared to daughter phenotypes in ssGWAS for the dairy cattle population. Single-step GWAS accounts for population structure and is a straightforward method for association analysis when only a fraction of the population is genotyped and/or when phenotypes are available on non-genotyped relatives.

scholarly journals Association Analysis of Candidate Variants in Admixed Brazilian Patients With Genetic Generalized Epilepsies

2021 ◽  
Vol 12 ◽  
Author(s):  
Felipe S. Kaibara ◽  
Tânia K. de Araujo ◽  
Patricia A. O. R. A. Araujo ◽  
Marina K. M. Alvim ◽  
Clarissa L. Yasuda ◽  
...  
Keyword(s):  
Native Americans ◽  
Statistical Power ◽  
Association Studies ◽  
Snp Array ◽  
Absence Epilepsy ◽  
Candidate Snps ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Clonic Seizures ◽  
Candidate Regions

Genetic generalized epilepsies (GGEs) include well-established epilepsy syndromes with generalized onset seizures: childhood absence epilepsy, juvenile myoclonic epilepsy (JME), juvenile absence epilepsy (JAE), myoclonic absence epilepsy, epilepsy with eyelid myoclonia (Jeavons syndrome), generalized tonic–clonic seizures, and generalized tonic–clonic seizures alone. Genome-wide association studies (GWASs) and exome sequencing have identified 48 single-nucleotide polymorphisms (SNPs) associated with GGE. However, these studies were mainly based on non-admixed, European, and Asian populations. Thus, it remains unclear whether these results apply to patients of other origins. This study aims to evaluate whether these previous results could be replicated in a cohort of admixed Brazilian patients with GGE. We obtained SNP-array data from 87 patients with GGE, compared with 340 controls from the BIPMed public dataset. We could directly access genotypes of 17 candidate SNPs, available in the SNP array, and the remaining 31 SNPs were imputed using the BEAGLE v5.1 software. We performed an association test by logistic regression analysis, including the first five principal components as covariates. Furthermore, to expand the analysis of the candidate regions, we also interrogated 14,047 SNPs that flank the candidate SNPs (1 Mb). The statistical power was evaluated in terms of odds ratio and minor allele frequency (MAF) by the genpwr package. Differences in SNP frequencies between Brazilian and Europeans, sub-Saharan African, and Native Americans were evaluated by a two-proportion Z-test. We identified nine flanking SNPs, located on eight candidate regions, which presented association signals that passed the Bonferroni correction (rs12726617; rs9428842; rs1915992; rs1464634; rs6459526; rs2510087; rs9551042; rs9888879; and rs8133217; p-values &lt;3.55e–06). In addition, the two-proportion Z-test indicates that the lack of association of the remaining candidate SNPs could be due to different genomic backgrounds observed in admixed Brazilians. This is the first time that candidate SNPs for GGE are analyzed in an admixed Brazilian population, and we could successfully replicate the association signals in eight candidate regions. In addition, our results provide new insights on how we can account for population structure to improve risk stratification estimation in admixed individuals.

scholarly journals Identification of early and intermediate biomarkers for ARDS mortality by multi-omic approaches

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
S. Y. Liao ◽  
N. G. Casanova ◽  
C. Bime ◽  
S. M. Camp ◽  
H. Lynn ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Association Studies ◽  
Unmet Need ◽  
Predictive Biomarkers ◽  
Predictive Biomarker ◽  
Genome Wide Association Studies ◽  
Potential Candidate ◽  
Gene Set ◽  
Protein Levels ◽  
N 93

AbstractThe lack of successful clinical trials in acute respiratory distress syndrome (ARDS) has highlighted the unmet need for biomarkers predicting ARDS mortality and for novel therapeutics to reduce ARDS mortality. We utilized a systems biology multi-“omics” approach to identify predictive biomarkers for ARDS mortality. Integrating analyses were designed to differentiate ARDS non-survivors and survivors (568 subjects, 27% overall 28-day mortality) using datasets derived from multiple ‘omics’ studies in a multi-institution ARDS cohort (54% European descent, 40% African descent). ‘Omics’ data was available for each subject and included genome-wide association studies (GWAS, n = 297), RNA sequencing (n = 93), DNA methylation data (n = 61), and selective proteomic network analysis (n = 240). Integration of available “omic” data identified a 9-gene set (TNPO1, NUP214, HDAC1, HNRNPA1, GATAD2A, FOSB, DDX17, PHF20, CREBBP) that differentiated ARDS survivors/non-survivors, results that were validated utilizing a longitudinal transcription dataset. Pathway analysis identified TP53-, HDAC1-, TGF-β-, and IL-6-signaling pathways to be associated with ARDS mortality. Predictive biomarker discovery identified transcription levels of the 9-gene set (AUC-0.83) and Day 7 angiopoietin 2 protein levels as potential candidate predictors of ARDS mortality (AUC-0.70). These results underscore the value of utilizing integrated “multi-omics” approaches in underpowered datasets from racially diverse ARDS subjects.

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