scholarly journals Analysis of Hematological Traits in Polled Yak by Genome-Wide Association Studies Using Individual SNPs and Haplotypes

Genes ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 463 ◽  
Author(s):  
Xiaoming Ma ◽  
Congjun Jia ◽  
Donghai Fu ◽  
Min Chu ◽  
Xuezhi Ding ◽  
...  
Keyword(s):  
Association Studies ◽  
Significant Snps ◽  
Domestic Animal ◽  
Genome Wide Association ◽  
Economic Losses ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Individual Snps ◽  
Causal Genes ◽  
Single Marker

Yak (Bos grunniens) is an important domestic animal living in high-altitude plateaus. Due to inadequate disease prevention, each year, the yak industry suffers significant economic losses. The identification of causal genes that affect blood- and immunity-related cells could provide preliminary reference guidelines for the prevention of diseases in the population of yaks. The genome-wide association studies (GWASs) utilizing a single-marker or haplotype method were employed to analyze 15 hematological traits in the genome of 315 unrelated yaks. Single-marker GWASs identified a total of 43 significant SNPs, including 35 suggestive and eight genome-wide significant SNPs, associated with nine traits. Haplotype analysis detected nine significant haplotype blocks, including two genome-wide and seven suggestive blocks, associated with seven traits. The study provides data on the genetic variability of hematological traits in the yak. Five essential genes (GPLD1, EDNRA, APOB, HIST1H1E, and HIST1H2BI) were identified, which affect the HCT, HGB, RBC, PDW, PLT, and RDWSD traits and can serve as candidate genes for regulating hematological traits. The results provide a valuable reference to be used in the analysis of blood properties and immune diseases in the yak.

Genome-Wide Association Studies for Immunoglobulin Concentrations in Colostrum and Serum in Chinese Holstein

2020 ◽  
Author(s):  
Shan Lin ◽  
Cuncun Ke ◽  
Lin Liu ◽  
Yahui Gao ◽  
Lingna Xu ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Association Studies ◽  
Early Death ◽  
Significant Snps ◽  
Genome Wide Association ◽  
Economic Losses ◽  
Genome Wide Association Studies ◽  
Chinese Holstein ◽  
Genome Wide ◽  
Nf Kappab

Abstract BackgroundThe early death and health problems of calves caused substantial economic losses in dairy industry. As the immune system has not been fully developed in the neonates, transport of passive immune substance such as immunoglobulins (Ig) from mothers to newborn calves is essential in protecting neonates from infections in their early life. Therefore, concentrations of immunoglobulins in the colostrum and serum of dairy cows are critical traits when estimating potential disease resistance of its offspring. ResultsColostrum, blood and hair follicle samples were collected from the 620 Chinese Holstein cows within 24 hours after calving. The concentration of total IgG, IgG1, IgG2, IgA and IgM in both colostrum and serum were detected via ELISA methods. With GCTA software, genome-wide association studies (GWASs) were performed with 88,934 SNPs genotyped by using Illumina 50K (54,609 SNPs) and GeneSeek 150K (140,668 SNPs) chips in which 50K chip were imputed to 150K SNPs with BEAGLE. As a result, 20, 1 and 4 significant SNPs were detected associated with the concentrations of IgG2, IgA and IgM at genome-wide level (P < 3.16E–6); 11, 11, 35, 11 and 10 significant SNPs were identified associated with total IgG, IgG1, IgG2, IgA and IgM at suggestive level (P < 6.32E–5). Such SNPs were located in or proximate to (±1 Mb) 1,083 genes, which were functionally implicated in biological processes and pathways, such as immune response, negative regulation of immunoglobulin secretion, Fc-epsilon receptor and NF-kappaB signaling pathways. By combining the biological functions and the known QTL data for immune traits in bovine, 21 promising candidate functional genes were identified for immunoglobulins concentrations in colostrum and serum in dairy cattle, they were ABR, TIMM22, CRK, MYO1C, RILP, SERPINF2, AKT1, BCL11B, HHIPL1, DYNC1H1, HSP90AA1, TRAF3, KLC1, IL6, PYCARD, ITGAM, TGFB1I1, GUSB, CRCP, RABGEF1 and SBDS.ConclusionsIn this study, we identified 21 candidate genes related to concentrations of immunoglobulins in colostrum and serum in dairy cattle by performing GWASs. Our findings provide a groundwork for unraveling the key genes and causal mutations affecting immunoglobulins levels in colostrum and important information for genetic improvement of such traits in dairy cattle.

scholarly journals Meta-analyses of genome wide association studies in lines of laying hens divergently selected for feather pecking using imputed sequence level genotypes

BMC Genetics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Clemens Falker-Gieske ◽  
Hanna Iffland ◽  
Siegfried Preuß ◽  
Werner Bessei ◽  
Cord Drögemüller ◽  
...  
Keyword(s):  
Brain Structure ◽  
Laying Hens ◽  
Association Studies ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Economic Losses ◽  
Genome Wide Association Studies ◽  
Feather Pecking ◽  
Genome Wide ◽  
Meta Analyses

Abstract Background Feather pecking (FP) is damaging behavior in laying hens leading to global economic losses in the layer industry and massive impairments of animal welfare. The objective of the study was to discover genetic variants and affected genes that lead to FP behavior. To achieve that we imputed low-density genotypes from two different populations of layers divergently selected for FP to sequence level by performing whole genome sequencing on founder and half-sib individuals. In order to decipher the genetic structure of FP, genome wide association studies and meta-analyses of two resource populations were carried out by focusing on the traits ‘feather pecks delivered’ (FPD) and the ‘posterior probability of a hen to belong to the extreme feather pecking subgroup’ (pEFP). Results In this meta-analysis, we discovered numerous genes that are affected by polymorphisms significantly associated with the trait FPD. Among them SPATS2L, ZEB2, KCHN8, and MRPL13 which have been previously connected to psychiatric disorders with the latter two being responsive to nicotine treatment. Gene set enrichment analysis revealed that phosphatidylinositol signaling is affected by genes identified in the GWAS and that the Golgi apparatus as well as brain structure may be involved in the development of a FP phenotype. Further, we were able to validate a previously discovered QTL for the trait pEFP on GGA1, which contains variants affecting NIPA1, KIAA1211L, AFF3, and TSGA10. Conclusions We provide evidence for the involvement of numerous genes in the propensity to exhibit FP behavior that could aid in the selection against this unwanted trait. Furthermore, we identified variants that are involved in phosphatidylinositol signaling, Golgi metabolism and cell structure and therefore propose changes in brain structure to be an influential factor in FP, as already described in human neuropsychiatric disorders.

scholarly journals Use of the Multivariate Discriminant Analysis for Genome-Wide Association Studies in Cattle

Animals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1300 ◽  
Author(s):  
Elisabetta Manca ◽  
Alberto Cesarani ◽  
Giustino Gaspa ◽  
Silvia Sorbolini ◽  
Nicolò P.P. Macciotta ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Association Studies ◽  
Real Data ◽  
Genome Wide Association ◽  
Stepwise Discriminant Analysis ◽  
Genome Wide Association Studies ◽  
Multivariate Method ◽  
Genome Wide ◽  
Single Marker ◽  
Multivariate Gwas

Genome-wide association studies (GWAS) are traditionally carried out by using the single marker regression model that, if a small number of individuals is involved, often lead to very few associations. The Bayesian methods, such as BayesR, have obtained encouraging results when they are applied to the GWAS. However, these approaches, require that an a priori posterior inclusion probability threshold be fixed, thus arbitrarily affecting the obtained associations. To partially overcome these problems, a multivariate statistical algorithm was proposed. The basic idea was that animals with different phenotypic values of a specific trait share different allelic combinations for genes involved in its determinism. Three multivariate techniques were used to highlight the differences between the individuals assembled in high and low phenotype groups: the canonical discriminant analysis, the discriminant analysis and the stepwise discriminant analysis. The multivariate method was tested both on simulated and on real data. The results from the simulation study highlighted that the multivariate GWAS detected a greater number of true associated single nucleotide polymorphisms (SNPs) and Quantitative trait loci (QTLs) than the single marker model and the Bayesian approach. For example, with 3000 animals, the traditional GWAS highlighted only 29 significantly associated markers and 13 QTLs, whereas the multivariate method found 127 associated SNPs and 65 QTLs. The gap between the two approaches slowly decreased as the number of animals increased. The Bayesian method gave worse results than the other two. On average, with the real data, the multivariate GWAS found 108 associated markers for each trait under study and among them, around 63% SNPs were also found in the single marker approach. Among the top 118 associated markers, 76 SNPs harbored putative candidate genes.

scholarly journals Genome-Wide Association Studies Using Haplotypes and Individual SNPs in Simmental Cattle

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e109330 ◽  
Author(s):  
Yang Wu ◽  
Huizhong Fan ◽  
Yanhui Wang ◽  
Lupei Zhang ◽  
Xue Gao ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Individual Snps ◽  
Simmental Cattle

Genome-wide association studies in elite varieties of German winter barley using single-marker and haplotype-based methods

2015 ◽  
Vol 134 (1) ◽  
pp. 28-39 ◽  
Author(s):  
Inka Gawenda ◽  
Patrick Thorwarth ◽  
Torsten Günther ◽  
Frank Ordon ◽  
Karl J. Schmid
Keyword(s):  
Association Studies ◽  
Winter Barley ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Single Marker

scholarly journals The power of a multivariate approach to genome-wide association studies: an example with Drosophila melanogaster wing shape

2017 ◽  
Author(s):  
William Pitchers ◽  
Jessica Nye ◽  
Eladio J. Márquez ◽  
Alycia Kowalski ◽  
Ian Dworkin ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Multivariate Analyses ◽  
Association Studies ◽  
Significant Snps ◽  
Wing Shape ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Phenotypic Effect ◽  
Genome Wide ◽  
Univariate Analyses

AbstractDue to the complexity of genotype-phenotype relationships, simultaneous analyses of genomic associations with multiple traits will be more powerful and more informative than a series of univariate analyses. In most cases, however, studies of genotype-phenotype relationships have analyzed only one trait at a time, even as the rapid advances in molecular tools have expanded our view of the genotype to include whole genomes. Here, we report the results of a fully integrated multivariate genome-wide association analysis of the shape of the Drosophila melanogaster wing in the Drosophila Genetic Reference Panel. Genotypic effects on wing shape were highly correlated between two different labs. We found 2,396 significant SNPs using a 5% FDR cutoff in the multivariate analyses, but just 4 significant SNPs in univariate analyses of scores on the first 20 principal component axes. A key advantage of multivariate analysis is that the direction of the estimated phenotypic effect is much more informative than a univariate one. Exploiting this feature, we show that the directions of effects were on average replicable in an unrelated panel of inbred lines. Effects of knockdowns of genes implicated in the initial screen were on average more similar than expected under a null model. Association studies that take a phenomic approach in considering many traits simultaneously are an important complement to the power of genomics. Multivariate analyses of such data are more powerful, more informative, and allow the unbiased study of pleiotropy.

scholarly journals Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fabricio Almeida-Silva ◽  
Thiago M. Venancio
Keyword(s):  
Candidate Genes ◽  
Resistance Genes ◽  
Association Studies ◽  
Fungal Species ◽  
Genome Wide Association ◽  
Economic Losses ◽  
Genome Wide Association Studies ◽  
Link Type ◽  
Physical Defense ◽  
Genome Wide

AbstractSoybean is one of the most important legume crops worldwide. However, soybean yield is dramatically affected by fungal diseases, leading to economic losses of billions of dollars yearly. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate genes associated with resistance to Cadophora gregata, Fusarium graminearum, Fusarium virguliforme, Macrophomina phaseolina, and Phakopsora pachyrhizi. We identified 188, 56, 11, 8, and 3 high-confidence candidates for resistance to F. virguliforme, F. graminearum, C. gregata, M. phaseolina and P. pachyrhizi, respectively. The prioritized candidate genes are highly conserved in the pangenome of cultivated soybeans and are heavily biased towards fungal species-specific defense responses. The vast majority of the prioritized candidate resistance genes are related to plant immunity processes, such as recognition, signaling, oxidative stress, systemic acquired resistance, and physical defense. Based on the number of resistance alleles, we selected the five most resistant accessions against each fungal species in the soybean USDA germplasm. Interestingly, the most resistant accessions do not reach the maximum theoretical resistance potential. Hence, they can be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression network generated here is available in a user-friendly web application (https://soyfungigcn.venanciogroup.uenf.br/) and an R/Shiny package (https://github.com/almeidasilvaf/SoyFungiGCN) that serve as a public resource to explore soybean-pathogenic fungi interactions at the transcriptional level.

scholarly journals Genome-Wide Association Studies of CKD and Related Traits

2020 ◽  
Vol 15 (11) ◽  
pp. 1643-1656
Author(s):  
Adrienne Tin ◽  
Anna Köttgen
Keyword(s):  
Kidney Function ◽  
Complex Traits ◽  
Kidney Diseases ◽  
Association Studies ◽  
Genome Wide Association ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Genome Wide ◽  
Causal Genes

The past few years have seen major advances in genome-wide association studies (GWAS) of CKD and kidney function–related traits in several areas: increases in sample size from >100,000 to >1 million, enabling the discovery of >250 associated genetic loci that are highly reproducible; the inclusion of participants not only of European but also of non-European ancestries; and the use of advanced computational methods to integrate additional genomic and other unbiased, high-dimensional data to characterize the underlying genetic architecture and prioritize potentially causal genes and variants. Together with other large-scale biobank and genetic association studies of complex traits, these GWAS of kidney function–related traits have also provided novel insight into the relationship of kidney function to other diseases with respect to their genetic associations, genetic correlation, and directional relationships. A number of studies also included functional experiments using model organisms or cell lines to validate prioritized potentially causal genes and/or variants. In this review article, we will summarize these recent GWAS of CKD and kidney function–related traits, explain approaches for downstream characterization of associated genetic loci and the value of such computational follow-up analyses, and discuss related challenges along with potential solutions to ultimately enable improved treatment and prevention of kidney diseases through genetics.

scholarly journals Genome-wide Association Study Of Plasma Proteins Identifies Putatively Causal Genes, Proteins, And Pathways For Cardiovascular Disease

2017 ◽  
Author(s):  
Chen Yao ◽  
George Chen ◽  
Ci Song ◽  
Michael Mendelson ◽  
Tianxiao Huan ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Treatment And Prevention ◽  
Genome Wide ◽  
Causal Genes ◽  
Disease Associations ◽  
Long Term Follow Up

SummaryIdentifying genetic variants associated with circulating protein concentrations (pQTLs) and integrating them with variants from genome-wide association studies (GWAS) may illuminate the proteome’s causal role in disease and bridge a GWAS knowledge gap for hitherto unexplained SNP-disease associations. We conducted GWAS of 71 high-value proteins for cardiovascular disease in 6,861 Framingham Heart Study participants followed by external replication. We comprehensively mapped thousands of pQTLs, including functional annotations and clinical-trait associations, and created an integrated plasma-protein-QTL searchable database. We next identified 15 proteins with pQTLs coinciding with coronary heart disease (CHD)-related variants from GWAS or tested causal for CHD by Mendelian randomization; most of these proteins were associated with new-onset cardiovascular disease events in Framingham participants with long-term follow-up. Identifying pQTLs and integrating them with GWAS results yields insights into genes, proteins, and pathways that may be causally associated with disease and can serve as therapeutic targets for treatment and prevention.

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