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Challenges of Adjusting Single-Nucleotide Polymorphism Effect Sizes for Linkage Disequilibrium
Human Heredity ( IF 1.8 ) Pub Date : 2021-02-12 , DOI: 10.1159/000513303
Valentina Escott-Price 1, 2 , Karl Michael Schmidt 3
Affiliation  

Background: Genome-wide association studies (GWAS) were successful in identifying SNPs showing association with disease, but their individual effect sizes are small and require large sample sizes to achieve statistical significance. Methods of post-GWAS analysis, including gene-based, gene-set and polygenic risk scores, combine the SNP effect sizes in an attempt to boost the power of the analyses. To avoid giving undue weight to SNPs in linkage disequilibrium (LD), the LD needs to be taken into account in these analyses. Objectives: We review methods that attempt to adjust the effect sizes (β-coefficients) of summary statistics, instead of simple LD pruning. Methods: We subject LD adjustment approaches to a mathematical analysis, recognising Tikhonov regularisation as a framework for comparison. Results: Observing the similarity of the processes involved with the more straightforward Tikhonov-regularised ordinary least squares estimate for multivariate regression coefficients, we note that current methods based on a Bayesian model for the effect sizes effectively provide an implicit choice of the regularisation parameter, which is convenient, but at the price of reduced transparency and, especially in smaller LD blocks, a risk of incomplete LD correction. Conclusions: There is no simple answer to the question which method is best, but where interpretability of the LD adjustment is essential, as in research aiming at identifying the genomic aetiology of disorders, our study suggests that a more direct choice of mild regularisation in the correction of effect sizes may be preferable.
Hum Hered


中文翻译:

调整单核苷酸多态性效应大小以解决连锁不平衡的挑战

背景:全基因组关联研究 (GWAS) 成功地识别了与疾病相关的 SNP,但其个体效应较小,需要大量样本才能达到统计显着性。GWAS 后分析方法,包括基于基因、基因集和多基因风险评分,结合了 SNP 效应大小,试图提高分析的力度。为了避免过度重视连锁不平衡 (LD) 中的 SNP,在这些分析中需要考虑 LD。目标:我们回顾了尝试调整汇总统计的效应大小(β -系数)的方法,而不是简单的 LD 剪枝。方法:我们对 LD 调整方法进行数学分析,将吉洪诺夫正则化视为比较框架。结果:观察与更直接的多元回归系数的吉洪诺夫正则化普通最小二乘估计所涉及的过程的相似性,我们注意到基于效应大小的贝叶斯模型的当前方法有效地提供了正则化参数的隐式选择,其中很方便,但代价是透明度降低,尤其是在较小的 LD 块中,存在 LD 校正不完整的风险。结论:对于哪种方法最好的问题没有简单的答案,但在 LD 调整的可解释性至关重要的情况下,如在旨在确定疾病基因组病因学的研究中,我们的研究表明,在修正效应大小可能更可取。
赫里德
更新日期:2021-02-12
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