Genome Wide Scans(全基因组扫描)研究综述
Genome Wide Scans 全基因组扫描 - CONCLUSION We confirm five associations with multiple sclerosis previously reported in genome-wide scans in Europeans in three ethnic groups from the Volga-Ural region of Russia. [1] Genome-wide scans for trans-species polymorphisms revealed ancient balancing selection at the loci. [2] Computational tools based on machine learning have the potential to aid discovery of genes encoding novel AMPs but existing approaches are not designed for genome-wide scans. [3] Overall, this study lays out the guidelines for genome-wide scans of sORFs and sPeptides in plants by integrating Ribo-seq and MS data and provides a more comprehensive resource of functional sPeptides in maize and gives a new perspective on the complex biological systems of plants. [4] Using genome-wide scans, we identified signatures of extreme differentiation among hares from distinct geographic areas that overlap with area-specific selective sweeps, suggesting targets for local adaptation. [5] Summary Statistics of BRCA were obtained from the latest and largest GWAS meta-analysis comprising of 82 studies from Breast Cancer Association Consortium (BCAC) studies, including women of European ancestry (133,384 cases and 113,789 controls); we obtained summary-level data from the GWAS meta-analysis of PrCa comprising 79,148 cases and 61,106 controls of European ancestry, and the dataset of RCC was a sex-specific GWAS meta-analysis comprising of two kidney cancer genome-wide scans for men (3,227 cases and 4,916 controls) and women (1,992 cases and 3,095 controls) of European ancestry. [6] To uncover these relationships, we performed genome-wide scans on odor-perception phenotypes for ten odors in 1003 Han Chinese and validated results for six of these odors in an ethnically diverse population (n=364). [7] It was detected as a sheep reproductive candidate gene by genome-wide scans, and related studies also showed its significance in female reproductive process. [8] Genome-wide scans of selection showed that several of the GWAS mutations for fungicide resistance resided in regions that have recently undergone a selective sweep. [9] Genome-wide scans revealed 31 regions with outlier SNPs differentiating lowland versus alpine ecotypes in Lug Creek, and 37 regions with outliers differentiating ecotypes in Six Mile Creek. [10] We then conduct genome-wide scans for selection and a genome-wide association study to identify targets of selection and candidate genes for body weight. [11] Having established an advantage in TALE target predicting considering epigenetic features, we use these data for promoterome and genome-wide scans by our new tool EpiTALE, leading to several novel putative virulence targets. [12] Using two overwintering phenotypic data collected at high latitudes of 40°N and one bioclimatic variable, genome-phenotype and genome-environment associations, and genome-wide scans were performed. [13] However, such genome wide scans can lack coverage in certain regions where it is difficult to , thus, it is possible that other loci with practical effect sizes remain to be uncovered through whole genome sequencing approaches. [14]结论 我们确认了以前在俄罗斯伏尔加 - 乌拉尔地区三个种族的欧洲人的全基因组扫描中报告的五种与多发性硬化症的关联。 [1] 跨物种多态性的全基因组扫描揭示了基因座的古老平衡选择。 [2] 基于机器学习的计算工具有可能帮助发现编码新型 AMP 的基因，但现有方法并非为全基因组扫描而设计。 [3] 总体而言，本研究通过整合 Ribo-seq 和 MS 数据，为植物中 sORF 和多肽的全基因组扫描制定了指南，并提供了更全面的玉米功能多肽资源，并为植物复杂的生物系统提供了新的视角。 . [4] 使用全基因组扫描，我们确定了来自不同地理区域的野兔之间的极端分化特征，这些特征与特定区域的选择性扫描重叠，表明了局部适应的目标。 [5] BRCA 的汇总统计数据来自最新和最大的 GWAS 荟萃分析，其中包括来自乳腺癌协会联盟 (BCAC) 研究的 82 项研究，包括欧洲血统的女性（133,384 例病例和 113,789 例对照）；我们从 PrCa 的 GWAS 荟萃分析中获得了汇总级数据，其中包括 79,148 例病例和 61,106 名欧洲血统对照，RCC 的数据集是性别特异性 GWAS 荟萃分析，包括男性的两次肾癌全基因组扫描（ 3,227 例和 4,916 名对照）和欧洲血统的女性（1,992 例和 3,095 名对照）。 [6] 为了揭示这些关系，我们对 1003 名汉族人的 10 种气味的气味感知表型进行了全基因组扫描，并在不同种族的人群中验证了其中 6 种气味的结果（n = 364）。 [7] 它通过全基因组扫描被检测为绵羊生殖候选基因，相关研究也表明其在女性生殖过程中的意义。 [8] 全基因组选择扫描显示，几个 GWAS 对杀菌剂抗性的突变位于最近经历过选择性扫描的区域。 [9] 全基因组扫描显示，在 Lug Creek 中有 31 个具有异常 SNP 区分低地和高山生态型的区域，以及在六英里溪中有 37 个具有异常值区分生态型的区域。 [10] 然后，我们进行全基因组扫描以进行选择和全基因组关联研究，以确定选择目标和体重的候选基因。 [11] 考虑到表观遗传特征，我们在 TALE 目标预测方面建立了优势，我们使用这些数据通过我们的新工具 EpiTALE 进行启动子组和全基因组扫描，从而产生了几个新的推定毒力目标。 [12] 使用在 40°N 高纬度地区收集的两个越冬表型数据和一个生物气候变量、基因组-表型和基因组-环境关联以及全基因组扫描进行。 [13] 然而，这种全基因组扫描在某些难以覆盖的区域可能缺乏覆盖率，因此，通过全基因组测序方法可能仍然发现具有实际效应大小的其他基因座。 [14]