MADCaP_AndrogeneticAlopecia_SummaryStatistics.tsv
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MADCaP_AndrogeneticAlopecia_SummaryStatistics.tsv contains the summary statistics for the ordinal GWAS of androgenetic alopecia phenotyped on the 1-4 UK Biobank scale after pooling 2,136 samples from Senegal, Ghana, Nigeria, and South Africa. These summary statistics correspond to the manuscript by Janivara et al: "Uncovering the genetic architecture and evolutionary roots of androgenetic alopecia in African men." This is a product of the MADCaP Network (https://www.madcapnetwork.org).<br><br>Column headers are as follows:<b>chromosome</b>: Chromosome of the SNP<b>base_pair_location</b>: hg38 coordinates of the SNP<b>effect_allele</b>: Allele for which effect size is reported<b>other_allele</b>: Other allele<b>beta</b>: Effect size with direction referenced with the effect allele<b>standard_error</b>: Standard Error of Effect Size Estimate in the MADCaP male pattern baldness GWAS<b>effect_allele_frequenc</b>y: Frequency of the effect allele<b>p_value</b>: P-value of GWAS association<b>variant_ID</b>: SNP ID
MADCaP_AndrogeneticAlopecia_SummaryStatistics.tsv 包含了合并塞内加尔、加纳、尼日利亚及南非共2136例样本后,针对基于1-4级英国生物样本库(UK Biobank)表型分型的雄激素性脱发(androgenetic alopecia)开展的有序全基因组关联研究(Genome-Wide Association Study, GWAS)的汇总统计量。该汇总统计量对应Janivara等人发表的研究论文:《解析非洲男性雄激素性脱发的遗传架构与进化根源》(Uncovering the genetic architecture and evolutionary roots of androgenetic alopecia in African men)。本数据集由MADCaP联盟产出,其官方网址为https://www.madcapnetwork.org。<br><br>列名说明如下:<b>chromosome</b>:单核苷酸多态性(Single Nucleotide Polymorphism, SNP)所在染色体;<b>base_pair_location</b>:SNP的hg38基因组坐标;<b>effect_allele</b>:报告效应量的效应等位基因;<b>other_allele</b>:其他等位基因;<b>beta</b>:以效应等位基因为参照的带方向效应量;<b>standard_error</b>:MADCaP男性型秃发GWAS中效应量估计值的标准误;<b>effect_allele_frequency</b>:效应等位基因频率;<b>p_value</b>:GWAS关联分析的P值;<b>variant_ID</b>:SNP位点ID
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figshare创建时间:
2024-07-12
搜集汇总
数据集介绍

背景与挑战
背景概述
该数据集是雄性激素性脱发(男性型秃发)的GWAS汇总统计文件,基于2,136个非洲男性样本(来自塞内加尔、加纳、尼日利亚和南非)的UK Biobank表型数据,用于研究该疾病的遗传结构和进化根源。数据集包含染色体位置、等位基因、效应大小、p值等标准统计列,支持开放获取(CC BY 4.0许可证),适用于基因组学和进化遗传学分析。
以上内容由遇见数据集搜集并总结生成



