Jeong Raehoon, Bulyk Martha L
Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA.
bioRxiv. 2023 Mar 30:2023.03.29.534582. doi: 10.1101/2023.03.29.534582.
Genome-wide association studies (GWAS) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretations difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell traits GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits.
全基因组关联研究(GWAS)已经在人类基因组中发现了众多与性状相关的基因座,其中大多数位于非编码区域,这使得解释变得困难。此外,由于广泛存在的连锁不平衡,因果变异在许多基因座上很难通过统计方法进行精细定位。为了应对这一挑战,我们提出了一种策略,利用转录因子(TF)结合定量性状基因座(bQTL)进行共定位分析,以识别可能由TF占据变异介导的性状关联,并使用基序分数来确定可能的因果变异。我们将这种方法应用于淋巴母细胞系中的PU.1 bQTL和血细胞性状GWAS数据。共定位分析揭示了69个可能由PU.1占据变异驱动的血细胞性状GWAS基因座。我们将PU.1基序改变变异指定为51个基因座上可能的共享因果变异。TF bQTL数据与其他GWAS数据的这种整合可能揭示额外复杂性状背后的转录调控机制和因果非编码变异。