Genetics of swayback in American Saddlebred horses
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Summary Extreme lordosis, also called swayback, lowback or softback, can occur as a congenital trait or as a degenerative trait associated with ageing. In this study, the hereditary aspect of congenital swayback was investigated using whole genome association studies of 20 affected and 20 unaffected American Saddlebred (ASB) Horses for 48 165 single‐nucleotide polymorphisms (SNPs). A statistically significant association was identified on ECA20 (corrected P = 0.017) for SNP BIEC2‐532523. Of the 20 affected horses, 17 were homozygous for this SNP when compared to seven homozygotes among the unaffected horses, suggesting a major gene with a recessive mode of inheritance. The result was confirmed by testing an additional 13 affected horses and 166 unaffected horses using 35 SNPs in this region of ECA20 (corrected P = 0.036). Combined results for 33 affected horses and 287 non‐affected horses allowed identification of a region of homozygosity defined by four SNPs in the region. Based on the haplotype defined by these SNPs, 80% of the 33 affected horses were homozygous, 21% heterozygous and 9% did not possess the haplotype. Among the non‐affected horses, 15% were homozygous, 47% heterozygous and 38% did not possess the haplotype. The differences between the two groups were highly significant ( P < 0.00001). The region defined by this haplotype includes 53 known and predicted genes. Exons from three candidate genes, TRERF1 , RUNX2 and CNPY3 were sequenced without finding distinguishing SNPs. The mutation responsible for swayback may lie in other genes or in regulatory regions outside exons. This information can be used by breeders to reduce the occurrence of swayback among their livestock. This condition may serve as a model for investigation of congenital skeletal deformities in other species.Keywords:
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Genome‐wide association studies (GWAS) are a popular approach for identifying common genetic variants and epistatic effects associated with a disease phenotype. The traditional statistical analysis of such GWAS attempts to assess the association between each individual single‐nucleotide polymorphism (SNP) and the observed phenotype. Recently, kernel machine‐based tests for association between a SNP set (e.g., SNPs in a gene) and the disease phenotype have been proposed as a useful alternative to the traditional individual‐SNP approach, and allow for flexible modeling of the potentially complicated joint SNP effects in a SNP set while adjusting for covariates. We extend the kernel machine framework to accommodate related subjects from multiple independent families, and provide a score‐based variance component test for assessing the association of a given SNP set with a continuous phenotype, while adjusting for additional covariates and accounting for within‐family correlation. We illustrate the proposed method using simulation studies and an application to genetic data from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.
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Genome-wide association studies (GWAS) play a critical role in identifying many loci for common diseases and traits. There has been a rapid increase in the number of GWAS over the past decade. As additional GWAS are being conducted, it is unclear whether a novel signal associated with the trait of interest is independent of single nucleotide polymorphisms (SNPs) in the same region that has been previously associated with the trait of interest. The general approach to determining whether the novel association is independent of previous signals is to examine the association of the novel SNP with the trait of interest conditional on the previously identified SNP and/or calculate linkage disequilibrium (LD) between the two SNPs. However, the role of epistasis and SNP by SNP interactions are rarely considered. Through simulation studies, we examined the role of SNP by SNP interactions when determining the independence of two genetic association signals. We have created an R package on Github called gxgRC to generate these simulation studies based on user input. In genetic association studies of asthma, we considered the role of SNP by SNP interactions when determining independence of signals for SNPs in the ARG1 gene and bronchodilator response.
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Aim: Recent genome‐wide association studies (GWAS) of bipolar disorder (BD) have detected new candidate genes, including DGKH , DFNB31 and SORCS2 . However, the results of these GWAS were not necessarily consistent, indicating the importance of replication studies. In this study, we tested the genetic association of DGKH , DFNB31 and SORCS2 with BD. Methods: We genotyped 18 single‐nucleotide polymorphisms (SNP) in DGKH , DFNB31 and SORCS2 using Japanese samples (366 cases and 370 controls). We also performed a meta‐analysis of four SNP in DGKH , using the previously published allele frequency data of Han‐Chinese case–control samples (1139 cases and 1138 controls). Results: In the association analysis using Japanese samples, a SNP in SORCS2 (rs10937823) showed nominal genotypic association. However, we could not find any association in an additional analysis of tag SNP around rs10937823. In the meta‐analysis of SNP in DGKH , rs9315897, which was not significantly associated with BD in the previous Chinese study, showed nominal association. Conclusion: Although the association was not strong, the result of this study would support the association between DGKH and BD.
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This chapter contains sections titled: Introduction DNA sequence variants and linkage disequilibrium Genetic architecture of human traits Principles of genetic association studies Candidate gene association studies Genome-wide association studies Utility of candidate gene and genome-wide approaches Sampling designs Challenges in association studies Outlook for the future References
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Genetic association studies are used to find candidate genes or genome regions that contribute to a specific disease by testing for a correlation between disease status and genetic variation. This article provides a broad outline of the design and analysis of such studies, focusing on case–control studies in candidate genes or regions.
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Atopic dermatitis (AD) has been extensively investigated for genetic associations utilizing both candidate gene approaches and genome-wide scans. Here, we comprehensively evaluated the available literature to determine the association of candidate genes in AD to gain additional insight in the etiopathogenesis of the disease. We systematically screened all studies that explored the association between polymorphisms and AD risks in cases of European and Asian ancestry and synthesized the available evidence through random-effects meta-analysis. We identified 97 studies that met our inclusion/exclusion criteria that examined 17 candidate loci in Europeans and 14 candidate genes in Asians. We confirmed the significant associations between FLG variants in both European and Asian populations and AD risk, while additional synthesis of available data revealed novel loci mapped to IL18 and TGFB1 genes in Europeans and IL12RB1 and MIF in Asians, that have not yet been identified by genome-wide association studies. Our findings provide comprehensive evidence for AD risk loci in cases of both European and Asian ancestries, validating previous associations as well as revealing novel loci that could imply previously unexplored biological pathways.
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Background Before the genome-wide association (GWA) era, many hypothesis-driven candidate gene association studies were performed that tested whether DNA variants in genes that had been selected based on prior knowledge about migraine pathophysiology were associated with migraine. Most studies involved small sample sets without robust replication, thereby making the risk of false-positive findings high. Genome-wide marker data of thousands of migraine patients and controls from the International Headache Genetics Consortium provide a unique opportunity to re-evaluate key findings from candidate gene association studies (and other non-GWA genetic studies) in a much larger data set. Methods We selected 21 genes from published candidate gene association studies and six additional genes from other non-GWA genetic studies in migraine. Single nucleotide polymorphisms (SNPs) in these genes, as well as in the regions 500 kb up- and downstream, were inspected in IHGC GWAS data from 5175 clinic-based migraine patients with and without aura and 13,972 controls. Results None of the SNPs in or near the 27 genes, including the SNPs that were previously found to be associated with migraine, reached the Bonferroni-corrected significance threshold; neither when analyzing all migraine patients together, nor when analyzing the migraine with and without aura patients or males and females separately. Conclusion The available migraine GWAS data provide no clear evidence for involvement of the previously reported most promising candidate genes in migraine.
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Atopic dermatitis (AD) has been extensively investigated for genetic associations utilizing both candidate gene approaches and genome-wide scans. Here, we comprehensively evaluated the available literature to determine the association of candidate genes in AD to gain additional insight into the etiopathogenesis of the disease. We systematically screened all studies that explored the association between polymorphisms and AD risks in cases of European and Asian ancestry and synthesized the available evidence through a random-effects meta-analysis. We identified 99 studies that met our inclusion/exclusion criteria that examined 17 candidate loci in Europeans and 14 candidate genes in Asians. We confirmed the significant associations between FLG variants in both European and Asian populations and AD risk, while synthesis of the available data revealed novel loci mapped to IL18 and TGFB1 genes in Europeans and IL12RB1 and MIF in Asians that have not yet been identified by genome-wide association studies. Our findings provide comprehensive evidence for AD risk loci in cases of both European and Asian ancestries, validating previous associations as well as revealing novel loci that could imply previously unexplored biological pathways.
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Clinical & Investigative Medicine (CIM) is receiving an increasing number of reports of candidate-based association studies. The track record of such studies in the past has been poor: numerous genetic associations reported from candidate gene studies have not been replicated in later studies. The rise of the genome-wide association study (GWAS) is changing this situation. A well-designed GWAS may identify a number of candidate loci without bias by screening the whole human genome. Validating and fine-mapping the candidate loci from GWAS are required to clarify the genetic mechanisms. Thus, a candidate-based association study has become a well-directed effort, instead of searching for a needle in a haystack. In the post-GWAS era, exponential growth of candidate-based genetic association studies is expected. A pressing issue accompanying this new trend is the assessment of the validity of an association study. In this editorial, we illustrate the major cause of false positive association from random sampling bias by an empirical example, and emphasize the application of the probability theory in assessing the validity of a genetic association study.
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