RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID.

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.

Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations.

It is important to study the genetics of complex traits in diverse populations. Here, we introduce covariate-adjusted linkage disequilibrium (LD) score regression (cov-LDSC), a method to estimate SNP-heritability (${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}})$ and its enrichment in homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the genome-wide association studies samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ by 10-60% in admixed populations; in contrast, cov-LDSC is robustly accurate. We apply cov-LDSC to genotyping data from 8124 individuals, mostly of admixed ancestry, from the Slim Initiative in Genomic Medicine for the Americas study, and to approximately 161 000 Latino-ancestry individuals, 47 000 African American-ancestry individuals and 135 000 European-ancestry individuals, as classified by 23andMe. We estimate ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and detect heritability enrichment in three quantitative and five dichotomous phenotypes, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals to date. Most traits have high concordance of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$. We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size $ \tau $* is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in the Latino-, African American- and European-ancestry populations, respectively. Our approach is a powerful way to analyze genetic data for complex traits from admixed populations.

HBV Genotype: A Significant Risk Factor in Determining Which Patients With Chronic HBV Infection Should Undergo Surveillance for HCC: The Hepatitis B Alaska Study.

BACKGROUND AND AIMS: Information is limited regarding HBV genotype and the outcome of chronic HBV (CHB) infection. We examined the effect of HBV genotype on HCC occurrence in Alaska Native (AN) persons with CHB, where five HBV genotypes are found: A2, B6, C2, D, and F1. APPROACH AND RESULTS: We calculated HCC incidence per 1,000 person-years of follow-up to determine which groups by age, sex, and genotype met current American Association for the Study of Liver Diseases (AASLD) HCC surveillance criteria. We used Poisson regression to compare HCC risk by genotype, age, sex, and Alaska region. Incidence of HCC was calculated using the sex-specific AASLD cutoff recommended for the Asian population of 50 years for women and 40 years for men. HCC screening was conducted semiannually using alpha-fetoprotein levels and abdominal ultrasound. Among 1,185 AN persons, median follow-up was 35.1 years; 667 (63%) were male. The HBV genotype distribution was 49% D, 18% F, 13% A, 6% C, 3% B, 0.1% H, and 12% undetermined. Sixty-three cases of HCC occurred. HCC incidence for genotype F was 5.73 per 1,000 person-years of follow-up, followed by 4.77 for C, 1.28 for A, 0.47 for D, and 0.00 for B. The HCC risk was higher for genotypes F (relative rate [RR], 12.7; 95% CI, 6.1-26.4), C (RR, 10.6; 95% CI, 4.3-26.0), and A (RR, 2.9; 95% CI, 1.0-8.0) compared to genotypes B and D. Among men < 40 years of age and women < 50 years of age, genotype F had the highest incidence (4.79/1,000 person-years). CONCLUSIONS: HBV genotype was strongly associated with HCC. HBV genotype should be considered in risk factor stratification.

Marker genotyping error effects on genomic predictions under different genetic architectures.

This study aimed to determine the effect of different rates of marker genotyping error on the accuracy of genomic prediction that was examined under distinct marker and quantitative trait loci (QTL) densities and different heritability estimates using a stochastic simulation approach. For each scenario of simulation, a reference population with phenotypic and genotypic records and a validation population with only genotypic records were considered. Marker effects were estimated in the reference population, and then their genotypic records were used to predict genomic breeding values in the validation population. The prediction accuracy was calculated as the correlation between estimated and true breeding values. The prediction bias was examined by computing the regression of true genomic breeding value on estimated genomic breeding value. The accuracy of the genomic evaluation was the highest in a scenario with no marker genotyping error and varied from 0.731 to 0.934. The accuracy of the genomic evaluation was the lowest in a scenario with marker genotyping error equal to 20% and changed from 0.517 to 0.762. The unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value were obtained in the reference and validation populations when the rate of marker genotyping error was equal to zero. The results showed that marker genotyping error can reduce the accuracy of genomic evaluations. Moreover, marker genotyping error can provide biased estimates of genomic breeding values. Therefore, for obtaining accurate results it is recommended to minimize the marker genotyping errors to zero in genomic evaluation programs.

The cost-effectiveness of genotyping versus sequencing for prenatal cystic fibrosis carrier screening.

OBJECTIVE: We investigated the cost-effectiveness of three sequential prenatal cystic fibrosis (CF) carrier screening strategies: genotyping both partners, genotyping one partner then sequencing the second, and sequencing both partners. METHOD: A decision-analytic model compared the strategies in a theoretical cohort of four million pregnant couples in the US population and five racial/ethnic sub-populations. Inputs were obtained from literature and varied in sensitivity analysis. Outcomes included cost per quality-adjusted life year (QALY), missed carrier couples, affected newborns, missed prenatal diagnoses, terminations, and procedure-related losses. The cost-effectiveness threshold was $100,000/QALY. RESULTS: Sequencing both partners identified 1099 carrier couples that were missed by genotyping both partners, leading to 273 fewer missed prenatal diagnoses, 152 more terminations, and 152 fewer affected newborns. A similar trend was observed in the genotyping followed by sequencing strategy. The incremental cost-effectiveness ratio of genotyping followed by sequencing compared to genotyping both partners was $180,004/QALY and the incremental cost-effectiveness ratio of sequencing both partners compared to genotyping followed by sequencing was $17.6 million/QALY. Sequencing both partners was cost-effective below $339 per test, genotyping/sequencing between $340 and $1837, and genotyping both partners above $1838. Sequencing was not cost-effective among five racial/ethnic sub-populations. CONCLUSION: Despite improved outcomes, sequencing for prenatal CF carrier screening was not cost-effective compared to genotyping. The clinical significance of the incremental cost-effectiveness of CF carrier screening is a matter of deliberation for public policy debate.

Strategies for processing and quality control of Illumina genotyping arrays.

Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data on > 100 000 subjects from >10 major Illumina genotyping arrays, we have accumulated extensive experience in handling some of the most peculiar scenarios related to the processing and QC of Illumina genotyping data. Here, we describe strategies for processing Illumina genotyping data from the raw data to an analysis ready format, and we elaborate on the necessary QC procedures required at each processing step. High-quality Illumina genotyping data sets can be obtained by following our detailed QC strategies.

Noninvasive Fetal Genotyping by Droplet Digital PCR to Identify Maternally Inherited Monogenic Diabetes Variants.

BACKGROUND: Babies of women with heterozygous pathogenic glucokinase (GCK) variants causing mild fasting hyperglycemia are at risk of macrosomia if they do not inherit the variant. Conversely, babies who inherit a pathogenic hepatocyte nuclear factor 4α (HNF4A) diabetes variant are at increased risk of high birth weight. Noninvasive fetal genotyping for maternal pathogenic variants would inform pregnancy management. METHODS: Droplet digital PCR was used to quantify reference and variant alleles in cell-free DNA extracted from blood from 38 pregnant women heterozygous for a GCK or HNF4A variant and to determine fetal fraction by measurement of informative maternal and paternal variants. Droplet numbers positive for the reference/alternate allele together with the fetal fraction were used in a Bayesian analysis to derive probability for the fetal genotype. The babies' genotypes were ascertained postnatally by Sanger sequencing. RESULTS: Droplet digital PCR assays for GCK or HNF4A variants were validated for testing in all 38 pregnancies. Fetal fraction of ≥2% was demonstrated in at least 1 cell-free DNA sample from 33 pregnancies. A threshold of ≥0.95 for calling homozygous reference genotypes and ≤0.05 for heterozygous fetal genotypes allowed correct genotype calls for all 33 pregnancies with no false-positive results. In 30 of 33 pregnancies, a result was obtained from a single blood sample. CONCLUSIONS: This assay can be used to identify pregnancies at risk of macrosomia due to maternal monogenic diabetes variants.

Outcomes of prior cervical cytology and HR-HPV testing in women subsequently diagnosed with CIN1, CIN2/3, and invasive cervical cancer: a 4-year routine clinical experience after implementation of systematic training and quality control programs.

BACKGROUND: In 2013, Jinan KingMed Diagnostics (JKD) first established a systematic cervical cytology training and quality control (QC) program in Shandong Province, China. We compared the efficacy of high-risk human papillomavirus (HR-HPV) detection, cytology, and their combination in routine clinical practice after the implementation of the training and QC program to identify the optimal first-line screening method in this region. METHODS: The data of patients histologically diagnosed with cervical intraepithelial neoplasia (CIN) 1, CIN2/3, and invasive cervical cancer (ICC) between January 2014 and December 2017 were retrieved from the JKD database. Cytology and/or HR-HPV testing results within 3 months preceding the CIN1 diagnoses and 6 months preceding the CIN2/3 and ICC diagnoses were analyzed. RESULTS: Prior screening data were available for 1829 CIN1 patients, 2309 CIN2/3 patients, and 680 ICC patients. Cytology alone and HR-HPV testing alone had similar rates of positive results for CIN2/3 (97.2% [854/879] vs. 95.4% [864/906], P = 0.105) and ICC detection (89.1% [205/230] vs. 92.7% [204/220], P = 0.185). Compared with either method alone, co-testing slightly increased the screening sensitivity for CIN2/3 (99.8% [523/524], all P < 0.001) and ICC (99.6% [229/230], all P < 0.001) detection. In the CIN1 group, cervical cytology alone (92.9% [520/560]) was more sensitive than HR-HPV testing alone (79.9% [570/713], P < 0.001), and co-testing (95.3% [530/556]) did not significantly improve the screening sensitivity (P = 0.105). CONCLUSIONS: After the implementation of a systematic training and QC program, both cytology and HR-HPV testing may be adopted for primary cervical cancer screening in Shandong Province.

Performance of pedigree and various forms of marker-derived relationship coefficients in genomic prediction and their correlations.

In recent years, with development and validation of different genotyping panels, several methods have been proposed to build efficient similarity matrices among individuals to be used for genomic selection. Consequently, the estimated genetic parameters from such information may deviate from their counterpart using traditional family information. In this study, we used a pedigree-based numerator relationship matrix (A) and three types of marker-based relationship matrices ( G ) including two identical by descent, that is G K and G M and one identical by state, G V as well as four Gaussian kernel ( GK ) similarity kernels with different smoothing parameters to predict yet to be observed phenotypes. Also, we used different kinship matrices that are a linear combination of marker-derived IBD or IBS matrices with A, constructed as K = λ G + 1 - λ A , where the weight ( λ ) assigned to each source of information varied over a grid of values. A Bayesian multiple-trait Gaussian model was fitted to estimate the genetic parameters and compare the prediction accuracy in terms of predictive correlation, mean square error and unbiasedness. Results show that the estimated genetic parameters (heritability and correlations) are affected by the source of the information used to create kinship or the weight placed on the sources of genomic and pedigree information. The superiority of GK-based model depends on the smoothing parameters (θ) so that with an optimum θ value, the GK-based model statistically yielded better performance (higher predictive correlation, lowest MSE and unbiased estimates) and more stable correlations and heritability than the model with IBD, IBS or A kinship matrices or any of the linear combinations.

Impact of somatic molecular profiling on clinical trial outcomes in rare epithelial gynecologic cancer patients.

OBJECTIVES: Conducting clinical trials in rare malignancies is challenging due to the limited number of patients and differences in biologic behavior. We investigated the feasibility and clinical utility of using genomic profiling for rare gynecologic malignancies. METHODS: Rare epithelial gynecologic cancer patients were analyzed for somatic variants through an institutional molecular profiling program using the Sequenom MassArray platform or the TruSeq Amplicon Cancer Panel on the MiSeq platform. Clinical trial outcomes by RECIST 1.1, and time on treatment were evaluated. RESULTS: From March 2012 to November 2015, 767 gynecologic patients were enrolled and 194 (27%) were classified as rare epithelial malignancies. At least one somatic mutation was identified in 72% of patients, most commonly in TP53 (39%), KRAS (28%) and PIK3CA (27%). A total of 14% of patients were treated on genotype-matched trials. There were no significant differences in overall response rate between genotype-matched versus unmatched trials, nor in median time on treatment between genotype trials and the immediate prior systemic standard treatment. Among 13 evaluable Low Grade Serous ovarian cancer patients treated on genotype-matched trials with MEK inhibitor-based targeted combinations, there were four partial responses. CONCLUSIONS: Somatic molecular profiling is feasible and enables the identification of patients with rare gynecologic cancers who are candidates for genotype-matched clinical trials. Genotype-matched trials, predominantly MEK-based combinations in KRAS and/or NRAS mutant Low Grade Serous ovarian cancer patients, and genotype-unmatched trials, have shown potential clinical activity. Prospective trials with integrated genotyping are warranted to assess the clinical utility of next generation sequencing tests as a standard clinical application in rare malignancies.

The impact of liver transplant recipient and donor genetic variability on tacrolimus exposure and transplant outcome.

This study investigated the effect of recipient and donor genetic variability on dose-adjusted steady-state tacrolimus concentrations (Css ) and clinical outcomes 3 and 6 months after liver transplant. Twenty-nine recipients and matched donor blood samples were genotyped for 27 single nucleotide polymorphisms including CYP3A5*3 (rs776746), ABCB1 haplotype and immune genes. Associations between genetic variability and clinical parameters and Css and the occurrence of rejection and nephrotoxicity were analysed by multivariate and multinomial logistic regression modelling and Jonckheere-Terpstra tests examined the impact of combined donor/recipient CYP3A5 expression on Css . At 3 months post-transplant modelling revealed an association between tacrolimus Css and recipient CASP1 rs580523 genotype (P = 0.005), accounting for 52% Css variance. Jonckheere-Terpstra tests revealed that as combined donor/recipient CYP3A5 expression increased, Css decreased (P = 0.010 [3 months], 0.018 [6 months]). As this is the first report of CASP1 genetic variability influencing tacrolimus Css , further validation in larger cohorts is required.

Analyzing the clinical actionability of germline pharmacogenomic findings in oncology.

BACKGROUND: Germline and tumor pharmacogenomics impact drug responses, but germline markers less commonly guide oncology prescribing. The authors hypothesized that a critical number of clinically actionable germline pharmacogenomic associations exist, representing clinical implementation opportunities. METHODS: In total, 125 oncology drugs were analyzed for positive germline pharmacogenomic associations in journals with impact factors ≥5. Studies were assessed for design and genotyping quality, clinically relevant outcomes, statistical rigor, and evidence of drug-gene effects. Associations from studies of high methodologic quality were deemed potentially clinically actionable, and translational summaries were written as point-of-care clinical decision support (CDS) tools and formally evaluated using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument. RESULTS: The authors identified germline pharmacogenomic results for 56 of 125 oncology drugs (45%) across 173 publications. Actionable associations were detected for 12 drugs, including 6 that had germline pharmacogenomic information within US Food and Drug Administration labels or published guidelines (capecitabine/fluorouracil/dihydropyrimidine dehydrogenase [DPYD], irinotecan/uridine diphosphate glucuronosyltransferase family 1 member A1 [UGT1A1], mercaptopurine/thioguanine/thiopurine S-methyltransferase [TPMT], tamoxifen/cytochrome P450 [CYP] family 2 subfamily D member 6 [CYP2D6]), and 6 others were novel (asparaginase/nuclear factor of activated T-cells 2 [NFATC2]/human leukocyte antigen D-related ß1 [HLA-DRB1], cisplatin/acylphosphatase 2 [ACYP2], doxorubicin/adenosine triphosphate-binding cassette subfamily C member 2/Rac family small guanosine triphosphatase 2/neutrophil cytosolic factor 4 [ABCC2/RAC2/NCF4], lapatinib/human leukocyte antigen DQ α1 [HLA-DQA1], sunitinib/cytochrome P450 family 3 subfamily A member 5 [CYP3A5], vincristine/centrosomal protein 72 [CEP72]). By using AGREE II, the developed CDS summaries had high mean ± standard deviation scores (maximum score, 100) for scope and purpose (92.7 ± 5.1) and rigour of development (87.6 ± 7.4) and moderate yet robust scores for clarity of presentation (58.6 ± 25.1) and applicability (55.9 ± 24.6). The overall mean guideline quality score was 5.2 ± 1.0 (maximum score, 7). Germline pharmacogenomic CDS summaries for these 12 drugs were recommended for implementation. CONCLUSIONS: Several oncology drugs have actionable germline pharmacogenomic information, justifying their delivery through institutional pharmacogenomic implementations to determine clinical utility. Cancer 2018;124:3052-65. © 2018 American Cancer Society.

Avoiding Small Intestinal Biopsies for Diagnosis of Celiac Disease in Children: A Reliable Strategy for All Patients?

BACKGROUND: Current reports applying ESPGHAN exception criteria (EEC) to diagnose celiac disease (CD) without duodenal biopsies indicate that a high percentage of patients with CD may be identified when applied correctly in specialized settings. Application of the EEC, however, in "daily life conditions" at the different levels of medical services is not clear. METHODS: EEC was applied to 130 pediatric patients evaluated for CD at 5 public hospitals in Santiago, Chile, during 2010 to 2015. Clinical presentation, serum anti-tissue transglutaminase 2 and anti-endomysium antibodies (EMA), genotyping, and small intestinal histology were obtained from clinical charts. RESULTS: A total of 78 of 130 patients reviewed had some of the data required for analysis, but EMA was determined in 54% and genotyping in 2.3% of patients, limiting the study. After offering free genotyping, only 12 of 78 (15%) had all data required for EEC application. In this small group, 10 of 12 (83.3%) patients could avoid duodenal biopsies and 2 (16.7%) with potential CD were misdiagnosed. Main reasons for not doing EMA and genotyping were that they are expensive, unavailable in the local health care center, and considered "not necessary" for diagnosis. CONCLUSION: Limited resources in clinical settings reduce availability of EMA and genotyping, making application of EEC criteria difficult and only possible only in 15% of our patients. Within this subgroup, biopsies could be avoided in 83.3%, and 16.7% of patients with potential CD were misdiagnosed. Insufficient studies and incorrect interpretation of EEC contributed to incomplete assessment in 52 of 130 (40%) patients. The Chilean public health system is likely representative of several others present in developing and developed countries.

Utility of Human Papillomavirus Genotyping in the Management of Low-Grade Squamous Intraepithelial Lesions.

OBJECTIVE: The aim of the study was to determine the usefulness of human papillomavirus (HPV) partial genotyping test in the triage of newly diagnosed low-grade squamous intraepithelial lesions (LSILs). MATERIALS AND METHODS: We analyzed 143 patients with LSIL diagnosed de novo. Lesions were classified as positive for HPV 16 or HPV 18, positive for HPV but not HPV 16 or HPV 18 (HPVno16no18) or no HPV detected (HPVneg). Patients were followed for a period of 2 years or until the lesion progressed. We calculated absolute and relative risks for progression and regression according to the HPV result. RESULTS: The mean (SD) age was 33.8 (11.1) years. A total of 19.6% were positive for HPV 16, 4.9% for HPV 18, and 63.6% for HPVno16no18. The absolute risk of HPV 16 for progression to cervical intraepithelial neoplasia grade 2 or more (CIN 2+) was 32.1%, 14.3% for HPV 18, and 5.8% for HPVno16no18. None of the HPVneg cases evolved to CIN 2+. The presence of HPV 16 conferred a 7.4 (95% CI = 2.7-20.3) times greater risk of developing CIN 2+ than its absence. The absolute risks for HPV 16, HPV 18, HPVno16no18, and HPVneg for regression were 53.6%, 57.1%, 75.4%, and 87.5%, respectively. Relative risks for regression were 0.7 (95% CI = 0.5-0.9) for HPV 16 and 1.3 (95% CI = 1.1-1.5) for HPVneg. CONCLUSIONS: The HPV 16 LSILs are more likely to progress to CIN 2+, so tight control and immediate colposcopy are crucial, whereas when HPV 16 is not present, follow-up could be less strict. Low-grade squamous intraepithelial lesions in which high-risk HPV is not detected do not progress to CIN 2+, so its control should be different from other LSIL, and conservative management could be an acceptable strategy.

Variant association tools for quality control and analysis of large-scale sequence and genotyping array data.

Currently there is great interest in detecting associations between complex traits and rare variants. In this report, we describe Variant Association Tools (VAT) and the VAT pipeline, which implements best practices for rare-variant association studies. Highlights of VAT include variant-site and call-level quality control (QC), summary statistics, phenotype- and genotype-based sample selection, variant annotation, selection of variants for association analysis, and a collection of rare-variant association methods for analyzing qualitative and quantitative traits. The association testing framework for VAT is regression based, which readily allows for flexible construction of association models with multiple covariates and weighting themes based on allele frequencies or predicted functionality. Additionally, pathway analyses, conditional analyses, and analyses of gene-gene and gene-environment interactions can be performed. VAT is capable of rapidly scanning through data by using multi-process computation, adaptive permutation, and simultaneously conducting association analysis via multiple methods. Results are available in text or graphic file formats and additionally can be output to relational databases for further annotation and filtering. An interface to R language also facilitates user implementation of novel association methods. The VAT's data QC and association-analysis pipeline can be applied to sequence, imputed, and genotyping array, e.g., "exome chip," data, providing a reliable and reproducible computational environment in which to analyze small- to large-scale studies with data from the latest genotyping and sequencing technologies. Application of the VAT pipeline is demonstrated through analysis of data from the 1000 Genomes project.

Trends in use of genotypic resistance testing and frequency of major drug resistance among antiretroviral-naive persons in the HIV Outpatient Study, 1999-2011.

BACKGROUND: Monitoring antiretroviral drug resistance can inform treatment recommendations; however, there are few such data from US patients before they initiate ART. METHODS: We analysed data from HIV Outpatient Study (HOPS) participants from nine US HIV clinics who were diagnosed with HIV infection during 1999-2011. Using the IAS-USA December 2010 guidelines, we assessed the frequency of major drug resistance mutations (mDRMs) related to antiretroviral agents in viral isolates from patients who underwent commercial genotypic testing (GT) for resistance before initiating ART. We employed general linear regression models to assess factors associated with having undergone GT, and then factors associated with having mDRM. RESULTS: Among 1531 eligible patients, 758 (49.5%) underwent GT before first ART, increasing from 15.5% in 1999-2002 to 75.9% in 2009-11 (P < 0.001). GT was carried out a median of 1.2 months after the diagnosis of HIV. In adjusted regression analyses, patients with pre-ART CD4+ T lymphocyte counts ≥200 cells/mm(3) or with HIV RNA levels >5.0 log10 copies/mL and those with a first HOPS visit in 2006 or later were significantly (P < 0.05) more likely to have undergone GT. Of the 758 patients, 114 (15.0%) had mDRMs; mutations relating to NRTIs, NNRTIs and PIs were present in 8.0%, 7.1% and 2.6%, respectively. There was no temporal change in the frequency of mDRM, and mDRMs were associated with an HIV RNA level <4.0 log10 copies/mL. CONCLUSIONS: During 1999-2011, GT use among antiretroviral-naive patients became more common, but a quarter of patients in recent years remained untested. The frequency of mDRMs remained stable over time at about 15%.

Uptake and utilization of directly acting antiviral medications for hepatitis C infection in U.S. veterans.

New drugs therapies have revolutionized the treatment of hepatitis C virus (HCV) infection. The objectives of this study were to evaluate uptake and utilization of boceprevir and telaprevir in the Department of Veterans Affairs (VA). We evaluated whether therapies conformed to response-guided protocols, whether they replaced standard interferon plus ribavirin treatment, and whether IL-28B was used to guide treatment. We performed an administrative data-based analysis of all patients receiving pharmacologic treatment for HCV in VA from October 2009 to July 2013. There were 12 737 new HCV prescriptions in VA during this time, with 5564 boceprevir or telaprevir prescriptions (44%) and 7173 prescriptions (56%) written for standard interferon plus ribavirin treatment. Prescriptions for the new treatments heavily favoured boceprevir vs telaprevir (83% vs 17%). Sixty-two percent (62%) of boceprevir-treated patients completed their minimum-specified protocol, while 69.2% of telaprevir-treated patients completed their minimum-specified protocol. From October 2010 to July 2012, 4090 patients had an IL-28B test; less than 16% of these tests guided subsequent HCV prescriptions. Uptake of boceprevir and telaprevir was rapid; the number of patients initiating treatment approximately doubled in the period after their introduction. While new prescriptions favor boceprevir or telaprevir over standard interferon plus ribavirin therapy, there appears to still be a strong role of interferon plus ribavirin in treating HCV patients. This work can inform our understanding of how other new effective HCV therapies will be used, their diffusion, and the timing of their diffusion in actual clinical practice.

Two adjustment strategies for imputation across genotyping arrays.

Genotype imputation is a powerful approach in genome-wide association studies (GWAS) because it can provide higher resolution for associated regions and facilitate meta-analysis. However, bias can exist if different genotyping arrays are used and are unbalanced for case versus control subjects. The intersection imputation strategy [imputation based on single nucleotide polymorphisms (SNPs) available on all arrays] is a valid strategy that eliminates the bias caused by unbalanced genotyping, but achieved at the expense of reduced statistical power. In order to improve power in this situation, we introduce two new strategies: the replacement strategy based on the imputation quality score (IQS) ≥0.9 and the correction strategy. The IQS is a score that we have previously introduced based on Cohen's kappa of rater agreement. The replacement strategy with IQS ≥0.9 is a hybrid approach that utilizes measured genotypes for SNPs available on one or more of all arrays whenever the SNP has a high imputation quality (defined by IQS ≥0.9). The correction strategy combines measured genotypes as well as imputed and corrected genotype dosages for SNPs available on one or more of all arrays. The correction strategy yields a valid statistical test, while the replacement strategy with IQS ≥0.9 eliminates most spurious associations. Both strategies maintain statistical power.

The value of regenotyping older linkage data sets with denser marker panels.

OBJECTIVES: Linkage analysis can help determine regions of interest in whole-genome sequence studies. However, many linkage studies rely on older microsatellite (MSAT) panels. We set out to determine whether results would change if we regenotyped families using a dense map of SNPs. METHODS: We selected 47 Hispanic-American families from the NIMH Repository and Genomics Resource (NRGR) schizophrenia data repository. We regenotyped all individuals with DNA available from the NRGR on the Affymetrix Lat Array. After optimizing SNP selection for inclusion on the linkage map, we compared information content (IC) and linkage results using MSAT, SNP and MSAT+SNP maps. RESULTS: As expected, SNP provided a higher average IC (0.78, SD 0.03) than MSAT (0.51, SD 0.10) in a direct 'apples-to-apples' comparison using only individuals genotyped on both platforms; while MSAT+SNP provided only a slightly higher IC (0.82, SD 0.03). However, when utilizing all available individuals, including those who had genotypes available on only one platform, the IC was substantially increased using MSAT+SNP (0.76, SD 0.05) compared to SNP (0.61, SD 0.02). Linkage results changed appreciably between MSAT and MSAT+SNP in terms of magnitude, rank ordering and localization of peaks. CONCLUSIONS: Regenotyping older family data can substantially alter the conclusions of linkage analyses.

Reducing false-positive incidental findings with ensemble genotyping and logistic regression based variant filtering methods.

As whole genome sequencing (WGS) uncovers variants associated with rare and common diseases, an immediate challenge is to minimize false-positive findings due to sequencing and variant calling errors. False positives can be reduced by combining results from orthogonal sequencing methods, but costly. Here, we present variant filtering approaches using logistic regression (LR) and ensemble genotyping to minimize false positives without sacrificing sensitivity. We evaluated the methods using paired WGS datasets of an extended family prepared using two sequencing platforms and a validated set of variants in NA12878. Using LR or ensemble genotyping based filtering, false-negative rates were significantly reduced by 1.1- to 17.8-fold at the same levels of false discovery rates (5.4% for heterozygous and 4.5% for homozygous single nucleotide variants (SNVs); 30.0% for heterozygous and 18.7% for homozygous insertions; 25.2% for heterozygous and 16.6% for homozygous deletions) compared to the filtering based on genotype quality scores. Moreover, ensemble genotyping excluded > 98% (105,080 of 107,167) of false positives while retaining > 95% (897 of 937) of true positives in de novo mutation (DNM) discovery in NA12878, and performed better than a consensus method using two sequencing platforms. Our proposed methods were effective in prioritizing phenotype-associated variants, and an ensemble genotyping would be essential to minimize false-positive DNM candidates.