Bost Darius M, Bizon Chris, Tilson Jeffrey L, Filer Dayne L, Gizer Ian R, Wilhelmsen Kirk C
Department of Genetics, School of Medicine, UNC-Chapel Hill, Chapel Hill, North Carolina, USA.
Renaissance Computing Institute, Chapel Hill, North Carolina, USA.
Complex Psychiatry. 2022 Sep;8(1-2):35-46. doi: 10.1159/000523748. Epub 2022 Feb 28.
Genome-wide association studies (GWAS) have played a critical role in identifying many thousands of loci associated with complex phenotypes and diseases. This has led to several translations of novel disease susceptibility genes into drug targets and care. This however has not been the case for analyses where sample sizes are small, which suffer from multiple comparisons testing. The present study examined the statistical impact of combining a burden test methodology, PrediXcan, with a multimodel meta-analysis, cross phenotype association (CPASSOC).
The analysis was conducted on 5 addiction traits: family alcoholism, cannabis craving, alcohol, nicotine, and cannabis dependence and 10 brain tissues: anterior cingulate cortex BA24, cerebellar hemisphere, cortex, hippocampus, nucleus accumbens basal ganglia, caudate basal ganglia, cerebellum, frontal cortex BA9, hypothalamus, and putamen basal ganglia. Our sample consisted of 1,640 participants from the University of California, San Francisco (UCSF) Family Alcoholism Study. Genotypes were obtained through low pass whole genome sequencing and the use of Thunder, a linkage disequilibrium variant caller.
The post-PrediXcan, gene-phenotype association without aggregation resulted in 2 significant results, and . Aggregating across phenotypes resulted no significant findings. Aggregating across tissues resulted in 15 significant and 5 suggestive associations: and ; , and respectively.
Given the relatively small size of the cohort, this multimodel approach was able to find over a dozen significant associations between predicted gene expression and addiction traits. Of our findings, 8 had prior associations with similar phenotypes through investigation of the GWAS Atlas. With the onset of improved transcriptome data, this approach should increase in efficacy.
全基因组关联研究（GWAS）在识别与复杂表型和疾病相关的数千个基因座方面发挥了关键作用。这已导致将多个新的疾病易感基因转化为药物靶点并应用于治疗。然而，对于样本量较小的分析情况并非如此，这类分析会受到多重比较检验的影响。本研究考察了将负担检验方法PrediXcan与多模型荟萃分析、交叉表型关联（CPASSOC）相结合的统计影响。
对5种成瘾性状进行分析：家族性酒精中毒、大麻渴望、酒精、尼古丁和大麻依赖，以及10种脑组织：前扣带回皮质BA24、小脑半球、皮质、海马体、伏隔核基底神经节、尾状核基底神经节、小脑、额叶皮质BA9、下丘脑和壳核基底神经节。我们的样本包括来自加利福尼亚大学旧金山分校（UCSF）家族性酒精中毒研究的1640名参与者。通过低通量全基因组测序以及使用连锁不平衡变异调用器Thunder获得基因型。
PrediXcan后，未进行汇总的基因-表型关联产生了2个显著结果，即 和 。跨表型汇总未得到显著结果。跨组织汇总产生了15个显著关联和5个提示性关联：分别为 和 ； ，以及 。
鉴于队列规模相对较小，这种多模型方法能够在预测基因表达与成瘾性状之间找到十几个显著关联。在我们的研究结果中，通过对GWAS图谱的调查，有8个与相似表型存在先前关联。随着转录组数据的改进，这种方法的效力应该会提高。
