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Bortezomib (Btz) shows robust efficacy in patients with multiple myeloma (MM); however, some patients experience suboptimal responses and show specific toxicities. Therefore, we attempted to identify specific HLA alleles associated with Btz‐related toxicities and response to treatment. Eighty‐two transplant‐ineligible patients with newly diagnosed MM enrolled in a phase II study (JCOG1105) comparing two less intensive melphalan, prednisolone, plus Btz (MPB) regimens were subjected to HLA typing. The frequency of each allele was compared between the groups, categorized based on toxicity grades and responses to MPB therapy. Among 82 patients, the numbers of patients with severe peripheral neuropathy (PN; grade 2 or higher), skin disorders (SD; grade 2 or higher), and pneumonitis were 16 (19.5%), 15 (18.3%), and 6 (7.3%), respectively. Complete response was achieved in 10 (12.2%) patients. Although no significant HLA allele was identified by multiple comparisons, several candidates were identified. HLA‐B*40:06 was more prevalent in patients with severe PN than in those with less severe PN (odds ratio [OR] = 6.76). HLA‐B*40:06 and HLA‐DRB1*12:01 were more prevalent in patients with SD than in those with less severe SD (OR = 7.47 and OR = 5.55, respectively). HLA‐DRB1*08:02 clustered in the group of patients with pneumonitis (OR = 11.34). Complete response was achieved in patients carrying HLA‐DQB1*03:02, HLA‐DQB1*05:01, and HLA‐DRB1*01:01 class II alleles. HLA genotyping could help predict Btz‐induced toxicity and treatment efficacy in patients with MM, although this needs further validation. Four factors, HLA‐B4006, female (sex), Arm A (treatment course), and bone pain, were chosen as explanatory variables using the stepwise method. The carriers of HLA‐B4006 showed the highest odds ratio for the risk of bortezomib‐induced peripheral neuropathy.

Objective This study was performed to evaluate the use of high-risk HPV (hrHPV) viral load in screening tests for cervical cancer to predict persistent infection and presence of cervical intraepithelial neoplasia grade 2 or worse (CIN2+). Methods We followed women between 30 and 60 years of age who performed self-sampling of vaginal fluid and subsequently a hrHPV test. Women who were hrHPV positive in their screening test repeated the hrHPV test 3–6 months later and were included in the present study. Results Our results show that women with a persistent HPV16 infection had higher HPV viral load in their primary screening test than women with transient infections ( p  = 5.33e-03). This was also true for sum of viral load for all hrHPV types in the primary screening test ( p  = 3.88e-07). 48% of women with persistent HPV16 infection and CIN2+ had an increase in HPV16 titer in the follow-up test, as compared to only 20% of women with persistent infection but without CIN2+ lesions. For the sum of all hrHPV types, 41% of women with persistent infection and CIN2+ had an increase in titer as compared to 26% of women without CIN2 + . Conclusions The results show that hrHPV viral load in the primary screening HPV test is associated with the presence of CIN2+ and could be used in triaging hrHPV positive women for different follow-up strategies or recall times. Serial testing of hrHPV viral load has the potential to distinguish women with CIN2+ lesions from women with persistent infection but without CIN2+ lesions.

Genetic polymorphism of genes encoding the drug metabolizing enzymes, cytochrome P450 2D6 and 2C19 (CYP2D6 and CYP2C19), is associated with treatment failure of and adverse reactions to psychotropic drugs. The clinical utility of routine CYP2D6 and CYP2C19 genotyping (CYP testing) is unclear. To estimate whether routine CYP testing effects the persistence of antipsychotic drug treatment. This single-masked, 3-group randomized clinical trial included patients aged 18 years or older who had been diagnosed within the schizophrenic spectrum (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes, F20-F29) and not previously genotyped. A total of 669 of 1406 potentially eligible patients from 12 psychiatric outpatient clinics in Denmark were approached between July 2008 and December 2009. Overall, 528 patients were genotyped and randomly allocated to 1 of 3 study groups or exclusion in a sequence of 1:1:1:3 using a predictive enrichment design, aiming to double the proportion of poor or ultrarapid metabolizers for CYP2D6 or CYP2C19. Outcome measurements were recorded at baseline and 1-year follow-up. Data analysis was performed in December 2012 and updated March 2019. The trial included 2 intervention groups, where antipsychotic drug treatment was guided by either CYP test (CYP test-guided [CTG]) or structured clinical monitoring (SCM), in which adverse effects and factors influencing compliance were systematically recorded at least once quarterly, and 1 control group. Primary outcome was antipsychotic drug persistence, ie, days to first modification of the initial treatment. Secondary outcomes were number of drug and dose changes, adverse effects, and psychotic symptoms, ie, hallucinations and delusions. A total of 528 participants were genotyped, and 311 (median [interquartile range {IQR} age, 41 [30-50] years; 139 [45%] women; median [IQR] duration of illness, 6 [3-13] years) were randomly allocated to 1 of 3 study groups. Overall, 61 participants (20%) were extreme metabolizers. There was no difference in antipsychotic drug persistence between the CTG group and the control group (hazard ratio [HR], 1.02; 95% CI, 0.71-1.45) or SCM and the control group (HR, 0.88; 95% CI, 0.61-1.26). Subanalyses among extreme metabolizers showed similar results (CTG: HR, 0.99; 95% CI, 0.48-2.03; SCM: HR, 0.93; 95% CI, 0.44-1.96). The results of this randomized clinical trial do not support routine CYP testing in patients with schizophrenia. ClinicalTrials.gov Identifier: NCT00707382.

Thiopurine S-methyltransferase (TPMT) plays an important role in the metabolism of thiopurines. Mutations in the TPMT gene can affect drug activity, which may have adverse effects in humans. Thus, genotyping can help elucidate genetic determinants of drug response to thiopurines and optimize the selection of drug therapies for individual patients, effectively avoiding palindromia during maintenance treatment caused by insufficient dosing and the serious side effects caused by excessive doses. The current available detection methods used for TPMT*3B and TPMT*3C are complex, costly and time-consuming. Therefore, innovative detection methods for TPMT genotyping are urgently required. The aim of the present study was to establish and optimize a simple, specific and timesaving TPMT genotyping method. Using the principles of Web-based Allele-Specific PCR and competitive real-time fluorescent allele-specific PCR (CRAS-PCR), two pairs of Scorpion primers were designed for the detection of TPMT*3B and *3C, respectively, and a mutation in TPMT*3A was inferred based on data from TPMT*3B and *3C. In total, 226 samples from volunteers living in Chongqing were used for CRAS-PCR to detect TPMT*3 mutations. Results showed that nine (3.98%) were mutant (MT) heterozygotes and none were MT homozygotes for TPMT*3C, and no TPMT*3A and TPMT*3B mutations were found. Three TPMT*3C MT heterozygotes were randomly selected for DNA sequencing, and CRAS-PCR results were consistent with the sequencing results. In conclusion, in order to improve simplicity, specificity and efficiency, the present study established and optimized CRAS-PCR assays for commonly found mutant alleles of TPMT*3A (G460A and A719G), TPMT*3B (G460A), and TPMT*3C (A719G).

Summary In the last decade, the revolution in sequencing technologies has deeply impacted crop genotyping practice. New methods allowing rapid, high‐throughput genotyping of entire crop populations have proliferated and opened the door to wider use of molecular tools in plant breeding. These new genotyping‐by‐sequencing (GBS) methods include over a dozen reduced‐representation sequencing (RRS) approaches and at least four whole‐genome resequencing (WGR) approaches. The diversity of methods available, each often producing different types of data at different cost, can make selection of the best‐suited method seem a daunting task. We review the most common genotyping methods used today and compare their suitability for linkage mapping, genomewide association studies (GWAS), marker‐assisted and genomic selection and genome assembly and improvement in crops with various genome sizes and complexity. Furthermore, we give an outline of bioinformatics tools for analysis of genotyping data. WGR is well suited to genotyping biparental cross populations with complex, small‐ to moderate‐sized genomes and provides the lowest cost per marker data point. RRS approaches differ in their suitability for various tasks, but demonstrate similar costs per marker data point. These approaches are generally better suited for de novo applications and more cost‐effective when genotyping populations with large genomes or high heterozygosity. We expect that although RRS approaches will remain the most cost‐effective for some time, WGR will become more widespread for crop genotyping as sequencing costs continue to decrease.

Genotyping by sequencing (GBS) is a next generation sequencing based method that takes advantage of reduced representation to enable high throughput genotyping of large numbers of individuals at a large number of SNP markers. The relatively straightforward, robust, and cost-effective GBS protocol is currently being applied in numerous species by a large number of researchers. Herein we describe a bioinformatics pipeline, TASSEL-GBS, designed for the efficient processing of raw GBS sequence data into SNP genotypes. The TASSEL-GBS pipeline successfully fulfills the following key design criteria: (1) Ability to run on the modest computing resources that are typically available to small breeding or ecological research programs, including desktop or laptop machines with only 8-16 GB of RAM, (2) Scalability from small to extremely large studies, where hundreds of thousands or even millions of SNPs can be scored in up to 100,000 individuals (e.g., for large breeding programs or genetic surveys), and (3) Applicability in an accelerated breeding context, requiring rapid turnover from tissue collection to genotypes. Although a reference genome is required, the pipeline can also be run with an unfinished \"pseudo-reference\" consisting of numerous contigs. We describe the TASSEL-GBS pipeline in detail and benchmark it based upon a large scale, species wide analysis in maize (Zea mays), where the average error rate was reduced to 0.0042 through application of population genetic-based SNP filters. Overall, the GBS assay and the TASSEL-GBS pipeline provide robust tools for studying genomic diversity.

R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely-used R/qtl software package to include multiparental populations, better handles modern high-dimensional data... R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely used R/qtl software package to include multiparent populations derived from more than two founder strains, such as the Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations. R/qtl2 is designed to handle modern high-density genotyping data and high-dimensional molecular phenotypes, including gene expression and proteomics. R/qtl2 includes the ability to perform genome scans using a linear mixed model to account for population structure, and also includes features to impute SNPs based on founder strain genomes and to carry out association mapping. The R/qtl2 software provides all of the basic features needed for QTL mapping, including graphical displays and summary reports, and it can be extended through the creation of add-on packages. R/qtl2, which is free and open source software written in the R and C++ programming languages, comes with a test framework.

Analysis of sequence diversity in the human genome is fundamental for genetic studies. Structural variants (SVs) are frequently omitted in sequence analysis studies, although each has a relatively large impact on the genome. Here, we present GraphTyper2, which uses pangenome graphs to genotype SVs and small variants using short-reads. Comparison to the syndip benchmark dataset shows that our SV genotyping is sensitive and variant segregation in families demonstrates the accuracy of our approach. We demonstrate that incorporating public assembly data into our pipeline greatly improves sensitivity, particularly for large insertions. We validate 6,812 SVs on average per genome using long-read data of 41 Icelanders. We show that GraphTyper2 can simultaneously genotype tens of thousands of whole-genomes by characterizing 60 million small variants and half a million SVs in 49,962 Icelanders, including 80 thousand SVs with high-confidence. Structural variants may be omitted in sequence analysis despite their importance in genome variation and phenotypic impact. Here the authors present GraphTyper2, which uses pangenome graphs to genotype structural variants using short-reads and can be applied in large-scale sequencing studies.

Structural variants (SVs) remain challenging to represent and study relative to point mutations despite their demonstrated importance. We show that variation graphs, as implemented in the vg toolkit, provide an effective means for leveraging SV catalogs for short-read SV genotyping experiments. We benchmark vg against state-of-the-art SV genotypers using three sequence-resolved SV catalogs generated by recent long-read sequencing studies. In addition, we use assemblies from 12 yeast strains to show that graphs constructed directly from aligned de novo assemblies improve genotyping compared to graphs built from intermediate SV catalogs in the VCF format.

The success of clinical genomics using next generation sequencing (NGS) requires the accurate and consistent identification of personal genome variants. Assorted variant calling methods have been developed, which show low concordance between their calls. Hence, a systematic comparison of the variant callers could give important guidance to NGS-based clinical genomics. Recently, a set of high-confident variant calls for one individual (NA12878) has been published by the Genome in a Bottle (GIAB) consortium, enabling performance benchmarking of different variant calling pipelines. Based on the gold standard reference variant calls from GIAB, we compared the performance of thirteen variant calling pipelines, testing combinations of three read aligners—BWA-MEM, Bowtie2 and Novoalign—and four variant callers—Genome Analysis Tool Kit HaplotypeCaller (GATK-HC), Samtools mpileup, Freebayes and Ion Proton Variant Caller (TVC), for twelve data sets for the NA12878 genome sequenced by different platforms including Illumina2000, Illumina2500 and Ion Proton, with various exome capture systems and exome coverage. We observed different biases toward specific types of SNP genotyping errors by the different variant callers. The results of our study provide useful guidelines for reliable variant identification from deep sequencing of personal genomes.