How to use the gnomad.utils.reference_genome.get_reference_genome function in gnomad

- `n_trans_singletons` - count of transmitted singletons
        - `n_untrans_singletons` - count of untransmitted singletons
        - `n_omni` - count of omni truth variants
        - `n_mills` - count of mills truth variants
        - `n_hapmap` - count of hapmap truth variants
        - `n_kgp_phase1_hc` - count of 1000 genomes phase 1 high confidence truth variants

    :param ht: Table that aggregation will be performed on
    :param fam_stats_ht: Path to family statistics HT
    :return: a dictionary containing aggregations to perform on ht
    """
    # Annotate binned table with the evaluation data
    ht = ht._parent
    indel_length = hl.abs(ht.alleles[0].length() - ht.alleles[1].length())
    # Load external evaluation data
    build = get_reference_genome(ht.locus).name
    clinvar = (
        grch37_resources.reference_data.clinvar
        if build == "GRCh37"
        else grch38_resources.reference_data.clinvar
    ).ht()[ht.key]
    truth_data = (
        grch37_resources.reference_data.get_truth_ht()
        if build == "GRCh37"
        else grch38_resources.reference_data.get_truth_ht()
    )[ht.key]
    fam = fam_stats_ht[ht.key]

    return dict(
        min_score=hl.agg.min(ht.score),
        max_score=hl.agg.max(ht.score),
        n=hl.agg.count(),

:param gt_expr: Name of entry field storing the genotype. Default: 'GT'
    :param f_stat_cutoff: f-stat to roughly divide 'XX' from 'XY' samples. Assumes XX samples are below cutoff and XY are above cutoff.
    :param float aaf_threshold: Minimum alternate allele frequency to be used in f-stat calculations.
    :return: Table of samples and their imputed sex karyotypes.
    """
    logger.info("Imputing sex chromosome ploidies...")
    if is_sparse:
        ploidy_ht = impute_sex_ploidy(
            mt, excluded_intervals, included_intervals, normalization_contig
        )
    else:
        raise NotImplementedError(
            "Imputing sex ploidy does not exist yet for dense data."
        )

    x_contigs = get_reference_genome(mt.locus).x_contigs
    logger.info(f"Filtering mt to biallelic SNPs in X contigs: {x_contigs}")
    if "was_split" in list(mt.row):
        mt = mt.filter_rows((~mt.was_split) & hl.is_snp(mt.alleles[0], mt.alleles[1]))
    else:
        mt = mt.filter_rows(
            (hl.len(mt.alleles) == 2) & hl.is_snp(mt.alleles[0], mt.alleles[1])
        )
    mt = hl.filter_intervals(
        mt, [hl.parse_locus_interval(contig) for contig in x_contigs]
    )

    if sites_ht is not None:
        if aaf_expr == None:
            logger.warning(
                "sites_ht was provided, but aaf_expr is missing. Assuming name of field with alternate allele frequency is 'AF'."
            )

def filter_to_autosomes(
    t: Union[hl.MatrixTable, hl.Table]
) -> Union[hl.MatrixTable, hl.Table]:
    """
    Filters the Table or MatrixTable to autosomes only.
    This assumes that the input contains a field named `locus` of type Locus

    :param t: Input MT/HT
    :return:  MT/HT autosomes
    """
    reference = get_reference_genome(t.locus)
    autosomes = hl.parse_locus_interval(
        f"{reference.contigs[0]}-{reference.contigs[21]}", reference_genome=reference
    )
    return hl.filter_intervals(t, [autosomes])

the coverage of an autosomal chromosome (by default chr20).

    Coverage is computed using the median block coverage (summed over the block size) and the non-ref coverage at non-ref genotypes.

    :param mt: Input sparse Matrix Table
    :param excluded_calling_intervals: Optional table of intervals to exclude from the computation. 
        Used only when determining contig size (not used when computing chromosome depth).
    :param included_calling_intervals: Optional table of intervals to use in the computation. 
        Used only when determining contig size (not used when computing chromosome depth).
    :param normalization_contig: Which chromosome to normalize by
    :param chr_x: Optional X Chromosome contig name (by default uses the X contig in the reference)
    :param chr_y: Optional Y Chromosome contig name (by default uses the Y contig in the reference)
    :return: Table with mean coverage over chromosomes 20, X and Y and sex chromosomes ploidy based on normalized coverage.
    """

    ref = get_reference_genome(mt.locus, add_sequence=True)
    if chr_x is None:
        if len(ref.x_contigs) != 1:
            raise NotImplementedError(
                "Found {0} X chromosome contigs ({1}) in Genome reference. sparse_impute_sex_ploidy currently only supports a single X chromosome contig. Please use the `chr_x` argument to  specify which X chromosome contig to use ".format(
                    len(ref.x_contigs), ",".join(ref.x_contigs)
                )
            )
        chr_x = ref.x_contigs[0]
    if chr_y is None:
        if len(ref.y_contigs) != 1:
            raise NotImplementedError(
                "Found {0} Y chromosome contigs ({1}) in Genome reference. sparse_impute_sex_ploidy currently only supports a single Y chromosome contig. Please use the `chr_y` argument to  specify which Y chromosome contig to use ".format(
                    len(ref.y_contigs), ",".join(ref.y_contigs)
                )
            )
        chr_y = ref.y_contigs[0]

filter_segdup: bool = True,
    filter_exome_low_coverage_regions: bool = False,
    high_conf_regions: Optional[List[str]] = None,
) -> Union[hl.MatrixTable, hl.Table]:
    """
    Filters low-confidence regions

    :param mt: MatrixTable or Table to filter
    :param filter_lcr: Whether to filter LCR regions
    :param filter_decoy: Whether to filter decoy regions
    :param filter_segdup: Whether to filter Segdup regions
    :param filter_exome_low_coverage_regions: Whether to filter exome low confidence regions
    :param high_conf_regions: Paths to set of high confidence regions to restrict to (union of regions)
    :return: MatrixTable or Table with low confidence regions removed
    """
    build = get_reference_genome(mt.locus).name
    if build == "GRCh37":
        import gnomad.resources.grch37.reference_data as resources
    elif build == "GRCh38":
        import gnomad.resources.grch38.reference_data as resources

    criteria = []
    if filter_lcr:
        lcr = resources.lcr_intervals.ht()
        criteria.append(hl.is_missing(lcr[mt.locus]))

    if filter_decoy:
        decoy = resources.decoy_intervals.ht()
        criteria.append(hl.is_missing(decoy[mt.locus]))

    if filter_segdup:
        segdup = resources.seg_dup_intervals.ht()