Expression QTLs for NYGC ALS Consortium Paper

The files below contain nominal and permuted quantitative trait loci (QTL) associations between common genetic variants derived from whole genome sequencing and gene expression phenotypes generated from RNA-seq of post-mortem tissue sections. All QTLs were mapped with TensorQTL. Top association files are gzip-compressed tab-separated variable files - *cis_qtl.txt.gz Nominal association files are stored as Parquet files to save space. These can be converted to text files using the following code snippet: pip install pandas pyarrow conda install -c bioconda htslib # provides bgzip python3 -c " import pandas as pd df = pd.read_parquet('your_file.parquet') df.to_csv('your_file.tsv.gz', sep='\t', index=False) " | bgzip > your_file.tsv.gz NYGC_all_common_variants_alleles.tsv.gz - Allele information for all SNPs tested in the eQTL analysis Table columns are formatted as follows: Nominal QTL results include all SNP-gene pairs tested using either a 1Mb window from each side of the transcription start site (TSS) of the gene. phenotype_id - ensembl ID of the gene tested (GENCODE v30) variant_id - SNP tested for association (rsid or chr:position:ref:alt) tss_distance - distance of the SNP to the gene transcription start site (TSS) maf - minor allele frequency in cohort ma_samples - number of samples carrying the minor allele ma_count - total number of minor alleles across individuals pval_nominal - nominal P-value from linear regression slope - slope of the linear regression slope_se - standard error of the slope Top association results include only the top SNP-gene association for each gene. Table columns are formatted as follows: phenotype_id - ensembl ID of the gene tested (GENCODE v30 num_var - total number of variants tested in cis beta_shape1 - first parameter value of the fitted beta distribution beta_shape2 - second parameter value of the fitted beta distribution true_df - effective degrees of freedom the beta distribution approximation pval_true_df - empirical P-value for the beta distribution approximation variant_id - ID of the top variant (rsid or chr:position:ref:alt) tss_distance - distance of the SNP to the gene transcription start site (TSS) ma_samples - number of samples carrying the minor allele ma_count - total number of minor alleles across individuals maf -minor allele frequency in MiGA cohort ref_factor - flag indicating if the alternative allele is the minor allele in the cohort (1 if AF <= 0.5, -1 if not) pval_nominal - nominal P-value from linear regression slope - slope of the linear regression slope_se - standard error of the slope pval_perm - first permutation P-value directly obtained from the permutations with the direct method pval_beta - second permutation P-value obtained via beta approximation. This is the one to use for downstream analysis qval - Storey q-value derived from pval_beta (FDR adjusted) pval_nominal_threshold - nominal P-value threshold for calling a variant-gene pair significant for the gene Allele Information for each variant: CHROM - chromosome position of the variant POS - position of the variant in the chromosome REF - reference allele (GRCh38) ALT - alternative allele (this is the effect allele in the eQTL analysis) ID - variant id (rsid or chr:position:ref:alt)以下文件涵盖了全基因组测序获得的常见遗传变异，与死后组织切片RNA测序所得基因表达表型之间的名义关联，以及置换后的数量性状位点（quantitative trait loci, QTL）关联。所有QTL均采用TensorQTL完成定位。 顶级关联文件为gzip压缩的制表符分隔值文件，文件命名格式为*cis_qtl.txt.gz。 名义关联文件采用Parquet格式存储以节省空间，可通过以下代码片段转换为文本文件： pip install pandas pyarrow conda install -c bioconda htslib # 提供bgzip工具 python3 -c " import pandas as pd df = pd.read_parquet('your_file.parquet') df.to_csv('your_file.tsv.gz', sep=' ', index=False) " | bgzip > your_file.tsv.gz NYGC_all_common_variants_alleles.tsv.gz：表达数量性状位点（expression quantitative trait loci, eQTL）分析中所有检测的单核苷酸多态性（single nucleotide polymorphism, SNP）的等位基因信息。 ### 表列格式说明 #### 名义QTL结果 名义QTL结果涵盖了所有基于基因转录起始位点（transcription start site, TSS）两侧各1Mb窗口所检测的SNP-基因对，其表列格式如下： - phenotype_id：所检测基因的Ensembl ID（对应GENCODE v30版本） - variant_id：用于关联检测的SNP，格式可为rsID或chr:位置:参考等位基因:变异等位基因 - tss_distance：该SNP与基因转录起始位点（TSS）的距离 - maf：研究队列中的次要等位基因频率 - ma_samples：携带次要等位基因的样本数量 - ma_count：所有个体中次要等位基因的总计数 - pval_nominal：线性回归分析得到的名义P值 - slope：线性回归的斜率 - slope_se：斜率的标准误 #### 顶级关联结果 顶级关联结果仅包含每个基因的最优SNP-基因关联，其表列格式如下： - phenotype_id：所检测基因的Ensembl ID（对应GENCODE v30版本） - num_var：顺式区域中检测的变异体总数 - beta_shape1：拟合β分布的第一个参数值 - beta_shape2：拟合β分布的第二个参数值 - true_df：β分布近似的有效自由度 - pval_true_df：β分布近似的经验P值 - variant_id：最优变异体的ID，格式可为rsID或chr:位置:参考等位基因:变异等位基因 - tss_distance：该SNP与基因转录起始位点（TSS）的距离 - ma_samples：携带次要等位基因的样本数量 - ma_count：所有个体中次要等位基因的总计数 - maf：MiGA队列中的次要等位基因频率 - ref_factor：标记该变异等位基因是否为队列中的次要等位基因（等位基因频率≤0.5时为1，否则为-1） - pval_nominal：线性回归分析得到的名义P值 - slope：线性回归的斜率 - slope_se：斜率的标准误 - pval_perm：通过直接置换法直接获得的首次置换P值 - pval_beta：通过β近似法获得的第二次置换P值，为下游分析推荐使用的指标 - qval：由pval_beta推导得到的Storey q值（经错误发现率（false discovery rate, FDR）校正） - pval_nominal_threshold：用于判定该基因的变异体-基因对是否显著的名义P值阈值 #### 变异体等位基因信息 各变异体的等位基因信息如下： - CHROM：变异体所在染色体 - POS：变异体在染色体上的位置 - REF：参考等位基因（对应GRCh38版本） - ALT：变异等位基因（为eQTL分析中的效应等位基因） - ID：变异体ID，格式可为rsID或chr:位置:参考等位基因:变异等位基因