# Methods¶

Import / Export

 export_elasticsearch(t, host, port, index, …) Export a Table to Elasticsearch. export_gen(dataset, output[, precision, gp, …]) Export a MatrixTable as GEN and SAMPLE files. export_plink(dataset, output[, call, …]) Export a MatrixTable as PLINK2 BED, BIM and FAM files. export_vcf(dataset, output[, …]) Export a MatrixTable as a VCF file. get_vcf_metadata(path) Extract metadata from VCF header. import_bed(path[, reference_genome, …]) Import a UCSC BED file as a Table. import_bgen(path, entry_fields[, …]) Import BGEN file(s) as a MatrixTable. import_fam(path[, quant_pheno, delimiter, …]) Import a PLINK FAM file into a Table. import_gen(path[, sample_file, tolerance, …]) Import GEN file(s) as a MatrixTable. import_locus_intervals(path[, …]) Import a locus interval list as a Table. import_matrix_table(paths[, row_fields, …]) Import tab-delimited file(s) as a MatrixTable. import_plink(bed, bim, fam[, …]) Import a PLINK dataset (BED, BIM, FAM) as a MatrixTable. import_table(paths[, key, min_partitions, …]) Import delimited text file (text table) as Table. import_vcf(path[, force, force_bgz, …]) Import VCF file(s) as a MatrixTable. index_bgen(path) Index BGEN files as required by import_bgen(). read_matrix_table(path[, _drop_cols, _drop_rows]) Read in a MatrixTable written with written with MatrixTable.write(). read_table(path[, _row_fields]) Read in a Table written with Table.write().

Statistics

 linear_regression(y, x[, covariates, root, …]) For each row, test an input variable for association with response variables using linear regression. logistic_regression(test, y, x[, …]) For each row, test an input variable for association with a binary response variable using logistic regression. linear_mixed_regression(kinship_matrix, y, x) Use a kinship-based linear mixed model to estimate the genetic component of phenotypic variance (narrow-sense heritability) and optionally test each variant for association. pca(entry_expr[, k, compute_loadings]) Run principal component analysis (PCA) on numeric columns derived from a matrix table.

Genetics

 balding_nichols_model(n_populations, …[, …]) Generate a matrix table of variants, samples, and genotypes using the Balding-Nichols model. concordance(left, right) Calculate call concordance with another dataset. filter_intervals(ds, intervals[, keep]) Filter rows with a list of intervals. filter_alleles(mt, f) Filter alternate alleles. filter_alleles_hts(mt, f, subset) Filter alternate alleles and update standard GATK entry fields. genetic_relatedness_matrix(call_expr) Compute the genetic relatedness matrix (GRM). hwe_normalized_pca(call_expr[, k, …]) Run principal component analysis (PCA) on the Hardy-Weinberg-normalized genotype call matrix. identity_by_descent(dataset[, maf, bounded, …]) Compute matrix of identity-by-descent estimates. impute_sex(call[, aaf_threshold, …]) Impute sex of samples by calculating inbreeding coefficient on the X chromosome. ld_prune(call_expr[, r2, bp_window_size, …]) Returns a maximal subset of nearly-uncorrelated variants. mendel_errors(call, pedigree) Find Mendel errors; count per variant, individual and nuclear family. de_novo(mt, pedigree, pop_frequency_prior, …) Call putative de novo events from trio data. nirvana(dataset, config[, block_size, name]) Annotate variants using Nirvana. pc_relate(call_expr, min_individual_maf, *) Compute relatedness estimates between individuals using a variant of the PC-Relate method. realized_relationship_matrix(call_expr) Computes the realized relationship matrix (RRM). sample_qc(dataset[, name]) Compute per-sample metrics useful for quality control. skat(key_expr, weight_expr, y, x[, …]) Test each keyed group of rows for association by linear or logistic SKAT test. split_multi_hts(ds[, keep_star, left_aligned]) Split multiallelic variants for datasets that contain one or more fields from a standard high-throughput sequencing entry schema. SplitMulti(ds[, keep_star, left_aligned]) Split multiallelic variants. transmission_disequilibrium_test(dataset, …) Performs the transmission disequilibrium test on trios. trio_matrix(dataset, pedigree[, complete_trios]) Builds and returns a matrix where columns correspond to trios and entries contain genotypes for the trio. variant_qc(mt[, name]) Compute common variant statistics (quality control metrics). vep(dataset, config[, block_size, name, csq]) Annotate variants with VEP. window_by_locus(mt, bp_window_size) Collect arrays of row and entry values from preceding loci.

Miscellaneous

 grep(regex, path[, max_count]) Searches given paths for all lines containing regex matches. maximal_independent_set(i, j[, keep, …]) Return a table containing the vertices in a near maximal independent set of an undirected graph whose edges are given by a two-column table. rename_duplicates(dataset[, name]) Rename duplicate column keys.