Changes

| Whole genome sequencing || TruSeq PCR-free prep, Illumina HiSeq X (150bp paired-end, 30x mean coverage), BWA-MEM (GRCh37 reference genome), Picard (mark PCR duplicates), GATK 3.4, bcftools (normalization and decomposition), Genome STRiP (CNV calls for CYP2D6, 2269 patients), Astrolabe (allele matching for CYP2D6, for comparison) || Quality filtering parameters are given in the article. The WGS samples (with some modifications) were also merged into a reference panel used for imputation (total 2279 Estonians and 1856 Finns). Cf. [https://www.nature.com/articles/ejhg201751 Mitt ''et al.'']

| [[Allele definition]] || Pruning of allele definitions (removing variants from allele definitions (i.e. only keeping variants that destroys the protein), removing [[Unknown function|alleles with unknown function]]) || The allele pruning also makes it more likely that patients are indeed have normalphenotype (instead of unknown phenotype), removing most sources to [[Unknown function|alleles with unknown function]]

| [[NGS|HLA-typing]] || [https://www.ncbi.nlm.nih.gov/pubmed/23762245 SNP2HLA ] tool (WGS only) || SNP2HLA is a fast and reasonably accurate tool, but A review of HLA-typing methods [https://www.ncbi.nlm.nih.gov/pubmed/27802932 from another articleBauer ''et al.''] does not mention this tool, but SNP2HLA is provided by the Broad Institute, so it seems that in a clinical setting, other tools may should be consideredgood.

| [[Allele definition|Multiple allele matches]] || Made hierarchy of alleles based on the biochemical function (No function > Decreased Function > Other functional statuses) || Probably this can be seen as a variant of the best solution to the [[Unknown function|unknown function problem]]: Look for the most serious consequence, and if not no allele with serious consequence was found, assume Normal function.In case there were more than one star allele match per haplotype, they matched all possible star allele diplotypes, and picked the diplotype with the most serious clinical consequence

| Haplotype calling || (Eagle2, most likely) In case there were more than one star allele match per haplotype, they matched all possible star allele diplotypes, and picked the diplotype with the most serious clinical consequence || Haplotype estimation for WGS was performed, but it is unclear which method was used . Probably the methodology is similar to that used in [https://www.nature.com/articles/ejhg201751 ''Mitt et al.''], in which case they used SHAPEIT2. Otherwise Eagle2 (Eagle2 as for microarraysmicroarray data) which is 6 times faster. || In general, probablythe difference between haplotyping and PGx allele matching it not clear (maybe right to say that PGx allele matching is a subset of general haplotyping?).

* Rare variants (< 1% mean minor allele frequency) account for 89% of all (different kinds of) deleterious mutations(affect 30-40% of patients with non-normal allele function according to [[NGS|''Lauschke et al.'']])