trumpet_plot(), forest_plot() and effect_compare_plot() match column
names case-insensitively, like as_gwas_data()pos, #chrom, alt, ...) are recognized-log10(p) columns (LOG10P, neg_log_pvalue, ...) are auto-detected and
back-transformedas_granges() exports results to a
Bioconductor GRanges, and as_gwas_data() now accepts GRanges inputtrumpet_plot(): effect size versus minor allele frequency with
statistical-power contours showing which variants a study can detectforest_plot() for effect estimates with confidence intervals across
cohorts or lead variantseffect_compare_plot() comparing variant effects between two studies
on their shared variantsgene_annotation() with bundled protein-coding genes for GRCh37 and
GRCh38, so regional and gene-labelled plots work without a GTFgene_track() now renders full exon structure when given exon_datasmart_downsample() with exponential-key weighted samplingy_truncate parameter for Manhattan plots with broken y-axis,
showing extreme p-values in a compressed zone above the breaksnp_density() with heatmap and points styles for SNP density
karyograms with centromere markersdensity_signal_plot() dual-track comparison of genotyping
density vs association signalchr_info_human(), chr_info_mouse(), chr_info_cattle() for
built-in chromosome data, and chr_info_ucsc() for any UCSC assemblypvalue_heatmap() to eliminate gapsfilter_region(), maf_filter(), merge_gwas(),
get_loci()highlight_regions()Initial Bioconductor pre-release.
manhattan_genes() for labeling peaks with gene namesannotate_genes() for nearest-gene mappingtop_hits() with clumping and cytoband estimationhighlight_regions() for marking genomic regionsgwas_preset()as_gwas_data() constructor with validation