Additional file 1 of Trans-ancestral rare variant association study with machine learning-based phenotyping for metabolic dysfunction-associated steatotic liver disease

Additional file 1. Supplementary tables. Table S1: Characteristics of different participant cohorts analyzed in this study. Table S2: Genome-wide significant common variant associations in PNPLA3 with true MASLD and PDFF. Table S3: Definitions of exclusionary diagnoses, Elixhauser comorbidities, MASLD outcomes, and MASLD risk factors. Table S4: Exome-wide significant rare and ultra-rare variant associations with true MASLD and PDFF. Table S5: Single variant allele frequencies in gnomAD v4.1.0. Table S6: Bonferroni-significant gene-level associations with true MASLD and PDFF. Table S7: Previously reported rare variants associated with MASLD. Table S8: Nominally significant rare coding variant associations with true MASLD and PDFF in GCKR, ATG7, MTTP, and PNPLA3. Table S9: Nominally significant associations of identified variants with laboratory and physical measurements. Table S10: Nominally significant associations of identified genes with laboratory and physical measurements. Table S11: Features included in the machine learning model to predict PDFF. Table S12: Performance metrics for PDFF prediction models among participants with different time gaps. Table S13: Performance metrics for identifying steatosisat different thresholds of predicted PDFF. Table S14: Most important features for the PDFF prediction model. Table S15: Power estimates for true and predicted phenotypes. Table S16: Heritability of and genetic correlation between MASLD and PDFF phenotypes in the UK Biobank. Table S17: Replication of 40 previously reported MASLD-associated variants. Table S18: Replication of 40 previously reported MASLD-associated variants in sex-stratified analyses. Table S19: Exome-wide significant rare and ultra-rare variant associations with predicted MASLD and PDFF. Table S20: Bonferroni-significant gene-level associations with predicted MASLD and PDFF. Table S21: Ancestry-stratified single variant and gene-level associations with predicted MASLD and PDFF. Table S22: Sex-stratified single variant and gene-level associations with predicted MASLD and PDFF. Table S23: Functional and clinical annotations for single variants identified in this study.附加文件1：补充表格。 表S1：本研究分析的不同参与者队列的特征。 表S2：PNPLA3基因中与确诊代谢功能障碍相关性脂肪性肝病（Metabolic Dysfunction-Associated Steatotic Liver Disease, MASLD）及质子密度脂肪分数（Proton Density Fat Fraction, PDFF）相关的全基因组显著常见变异关联。 表S3：排除性诊断、埃利克豪泽（Elixhauser）共病、MASLD结局及MASLD危险因素的定义。 表S4：与确诊MASLD及PDFF相关的全外显子组显著罕见及超罕见变异关联。 表S5：基因组聚合数据库（Genome Aggregation Database, gnomAD）v4.1.0中的单变异等位基因频率。 表S6：与确诊MASLD及PDFF相关的经邦费罗尼校正后的显著基因水平关联。 表S7：既往报道的与MASLD相关的罕见变异。 表S8：GCKR、ATG7、MTTP及PNPLA3基因中与确诊MASLD及PDFF相关的名义显著性罕见编码变异关联。 表S9：已鉴定变异与实验室检测及体格测量指标的名义显著性关联。 表S10：已鉴定基因与实验室检测及体格测量指标的名义显著性关联。 表S11：用于预测PDFF的机器学习模型所纳入的特征。 表S12：不同时间间隔亚组中PDFF预测模型的性能指标。 表S13：基于预测PDFF不同阈值的脂肪变性识别性能指标。 表S14：PDFF预测模型中最重要的特征。 表S15：确诊表型与预测表型的效力估计。 表S16：英国生物库（UK Biobank）中MASLD及PDFF表型的遗传力及其遗传相关系数。 表S17：40个既往报道的MASLD相关变异的验证结果。 表S18：40个既往报道的MASLD相关变异在性别分层分析中的验证结果。 表S19：与预测MASLD及PDFF相关的全外显子组显著罕见及超罕见变异关联。 表S20：与预测MASLD及PDFF相关的经邦费罗尼校正后的显著基因水平关联。 表S21：种族分层下与预测MASLD及PDFF相关的单变异及基因水平关联。 表S22：性别分层下与预测MASLD及PDFF相关的单变异及基因水平关联。 表S23：本研究鉴定的单变异的功能与临床注释。