Objective: Nutcracker syndrome is a rare condition that involves mechanical compression of the left renal vein, leading to chronic and debilitating left fl ank pain. The etiology of the pain is misdiagnosed frequently, and patients usually require long-term opioid use to manage their pain. Multiple therapeutic options for nutcracker syndrome have been described in the literature but the reports are limited by small numbers of patients, and the lack of convincing data demonstrating consistently improved outcomes. Here we report the largest series to date of patients undergoing renal autotransplantation for the treatment of nutcracker syndrome. Methods: We performed a multicenter retrospective cohort review of patients 105 patients with nutcracker syndrome who underwent renal autotransplantation as a primary or salvage therapy. Results: During the overall study period, 93.1% of patients treated with autotransplantation had durable, complete fl ank pain relief at 12 months with both open and robotic surgical approach. After autotransplantation, a statistically significant decrease in the percentage of patients using opioids from 48.6% to 17.0% was demonstrated at 12 months. In those patients using opioids before autotransplantation, a statistically significant decrease in morphine milligram equivalents was demonstrated from an alarming 68.9 +/- 15.0 per day to 25.0 +/- 11.02 morphine milligram equivalents per day. Conclusions: Our fi ndings suggest that renal autotransplantation, as a primary treatment or a salvage treatment, in patients with nutcracker syndrome provides durable pain relief and a marked decrease in chronic opioid use regardless of surgical approach. (J Vasc Surg Venous Lymphat Disord 2025;13:101983.)

Budhiraja, Pooja MD; Schold, Jesse D. PhD, MStat, MEd; Heilman, Raymond L. MD; Malamon, John MD; Kaplan, Bruce MD Author Information

In 1948, Claude Shannon published a mathematical system describing the probabilistic relationships between the letters of a natural language and their subsequent order or syntax structure. By counting unique, reoccurring sequences of letters called N-grams, this language model was used to generate recognizable English sentences from N-gram frequency probability tables. More recently, N-gram analysis methodologies have been successfully applied to address many complex problems in a variety of domains, from language processing to genomics. One such example is the common use of N-gram frequency patterns and supervised classification models to determine authorship and plagiarism. In this paradigm, DNA is a language model where nucleotides are analogous to the letters of a word and nucleotide N-grams are analogous to the words of a sentence. Because DNA contains highly conserved and identifiable nucleotide sequence frequency patterns, this approach can be applied to a variety of classification and data reduction problems, such as identifying species based on unknown DNA segments. Other useful applications of this methodology include the identification of functional gene elements, microorganisms, sequence contamination, and sequencing artifacts. To this end, I present DNAnamer, a generalized and extensible methodological framework and analysis toolkit for the supervised classification of DNA sequences based on their N-gram frequency patterns.

BackgroundThe cytomegalovirus (CMV) mismatch rate in deceased donor kidney transplant (DDKT) recipients in the US remains above 40%. Since CMV mismatching is common in DDKT recipients, the cumulative effects may be significant in the context of overall patient and graft survival. Our primary objective was to describe the short- and long-term risks associated with high-risk CMV donor positive/recipient negative (D+/R-) mismatching among DDKT recipients with the explicit goal of deriving a mathematical mismatching penalty.MethodsWe conducted a retrospective, secondary analysis of the Scientific Registry of Transplant Recipients (SRTR) database using donor-matched DDKT recipient pairs (N=105,608) transplanted between 2011-2022. All-cause mortality and graft failure hazard ratios were calculated from one year to ten years post-DDKT. All-cause graft failure included death events. Survival curves were calculated using the Kaplan-Meier estimation at 10 years post-DDKT and extrapolated to 20 years to provide the average graft days lost (aGDL) and average patient days lost (aPDL) due to CMV D+/R- serostatus mismatching. We also performed an age-based stratification analysis to compare the relative risk of CMV D+ mismatching by age.ResultsAmong 31,518 CMV D+/R- recipients, at 1 year post-DDKT, the relative risk of death increased by 29% (p<0.001), and graft failure increased by 17% (p<0.001) as compared to matched CMV D+/R+ group (N=31,518). Age stratification demonstrated a significant increase in the risk associated with CMV mismatching in patients 40 years of age and greater. The aGDL per patient due to mismatching was 125 days and the aPDL per patient was 100 days.ConclusionThe risks of CMV D+/R- mismatching are seen both at 1 year post-DDKT period and accumulated throughout the lifespan of the patient, with the average CMV D+/R- recipient losing more than three months of post-DDKT survival time. CMV D+/R- mismatching poses a more significant risk and a greater health burden than previously reported, thus obviating the need for better preventive strategies including CMV serodirected organ allocation to prolong lifespans and graft survival in high-risk patients.

Detecting structural variants (SVs) in whole-genome sequencing poses significant challenges. We present a protocol for variant calling, merging, genotyping, sensitivity analysis, and laboratory validation for generating a high-quality SV call set in whole-genome sequencing from the Alzheimer’s Disease Sequencing Project comprising 578 individuals from 111 families. Employing two complementary pipelines, Scalpel and Parliament, for SV/indel calling, we assessed sensitivity through sample replicates (N = 9) with in silico variant spike-ins. We developed a novel metric, D-score, to evaluate caller specificity for deletions. The accuracy of deletions was evaluated by Sanger sequencing. We generated a high-quality call set of 152,301 deletions of diverse sizes. Sanger sequencing validated 114 of 146 detected deletions (78.1%). Scalpel excelled in accuracy for deletions ≤100 bp, whereas Parliament was optimal for deletions >900 bp. Overall, 83.0% and 72.5% of calls by Scalpel and Parliament were validated, respectively, including all 11 deletions called by both Parliament and Scalpel between 101 and 900 bp. Our flexible protocol successfully generated a high-quality deletion call set and a truth set of Sanger sequencing–validated deletions with precise breakpoints spanning 1–17,000 bp.

Goncalves, Carlos PhD; Bhagwandin, Bryon PhD; Malamon, John PhD; Kaplan, Bruce MD Author Information

Background:Currently, there is no mathematical model used nationally to determine the medical urgency of patients on the heart transplant waitlist in the United States. While the current organ distribution system accounts for many patient factors, a truly objective model is needed to more reliably stratify patients by their medical acuity. Objectives:The aim of the study was to develop risk scores (Colorado Heart failure Acuity Risk Model [CHARM] score) to predict mortality in adults waitlisted for heart transplant. Methods:Risk scores were based on multivariable logistic regression models with mortality endpoints at 90 days, 180 days, 1 year, and 2 years. The models included serology data and patient history variables from waitlisted patients (N = 4,176) within the Scientific Registry of Transplant Recipients database from January 1, 2017, to September 2, 2023. Results:The CHARM score included serum markers (brain natriuretic peptide, creatinine, sodium, aspartate aminotransferase, albumin, total bilirubin) and clinical variables (history of cardiac surgery, prior transplant, willingness to accept an hepatitis C virus positive heart, use of extracorporeal membrane oxygenation, use of mechanical life support, implantation of a cardiac defibrillator, and ventilator support prior to transplant). Sample holdout-validation for the models yielded average area under the curves of 0.825 (90-day), 0.805 (180-day), 0.779 (1-year), and 0.766 (2-year). Risk indices for all models were 99% correlated with observed mortality rates. Conclusions:The CHARM score provides reliable calibration and prediction, offering an objective system for identifying critically ill patients on the heart transplant waitlist. The CHARM score will be useful in the era of continuous distribution to standardize organ allocation.

Background The estimated long-term survival (EPTS) score is used for kidney allocation. A comparable prognostic tool to accurately quantify EPTS benefit in deceased donor liver transplant (DDLT) candidates is nonexistent. Methods Using the Scientific Registry of Transplant Recipients (SRTR) database, we developed, calibrated, and validated a nonlinear regression equation to calculate liver-EPTS (L-EPTS) for 5-and 10-year outcomes in adult DDLT recipients. The population was randomly split (70:30) into two discovery (N = 26,372 and N = 46,329) and validation cohorts (N =11,288 and N =19,859) for 5-and 10-year post-transplant outcomes, respectively. Discovery cohorts were used for variable selection, Cox proportional hazard regression modeling, and nonlinear curve fitting. Eight clinical variables were selected to construct the L-EPTS formula, and a five-tiered ranking system was created. Findings Tier thresholds were defined and the L-EPTS model was calibrated (R-2 = 0.96 [5-year] and 0.99 [10-year]). Patients' median survival probabilities in the discovery cohorts for 5-and 10-year outcomes ranged from 27.94% to 89.22% and 16.27% to 87.97%, respectively. The L-EPTS model was validated via calculation of receiver operating characteristic (ROC) curves using validation cohorts. Area under the ROC curve was 82.4% (5-year) and 86.5% (10-year). Interpretation L-EPTS has high applicability and clinical utility because it uses easily obtained pre-transplant patients characteristics to accurately discriminate between those who are likely to receive a prolonged survival benefit and those who are not. It is important to evaluate medical urgency alongside survival benefit and placement efficiency when considering the allocation of a scarce resource. Copyright (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Structural variations (SVs) are important contributors to the genetics of numerous human diseases. However, their role in Alzheimer's disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. Here, we analyzed whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP, N=16,905 subjects) and identified 400,234 (168,223 high-quality) SVs. We found a significant burden of deletions and duplications in AD cases (OR=1.05, P=0.03), particularly for singletons (OR=1.12, P=0.0002) and homozygous events (OR=1.10, P<0.0004). On AD genes, the ultra-rare SVs, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1, were associated with AD (SKAT-O P=0.004). Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, e.g., a deletion (chr2:105731359-105736864) in complete LD (R2=0.99) with rs143080277 (chr2:105749599) in NCK2. We also identified 16 SVs associated with AD and 13 SVs associated with AD-related pathological/cognitive endophenotypes. Our findings demonstrate the broad impact of SVs on AD genetics.

Biliary anastomotic stricture (BAS) is a frequent complication of liver transplantation and is associated with reduced graft survival and patient morbidity. Existing treatments for BAS involve dilation of the stricture though placement of 1 or more catheters for 6 to 24 months yielding limited effectiveness in transplant patients. In this case series, we present preliminary safety and efficacy of a novel percutaneous laser stricturotomy treatment in a cohort of 5 posttransplant patients with BAS refractory to long-term large bore catheterization. In all patients, holmium or thulium laser was used to excise the stricture and promote biliary re-epithelization. There were no periprocedural complications. Technical success was 100% and at mean follow-up time of 22 months, there have been no recurrences. In conclusion, percutaneous laser stricturotomy demonstrates preliminary safety and efficacy in treatment of refractory BAS following liver transplantation.

Purpose of Review To describe statistical confounding and the impact of observational research in transplantation. Further, to evaluate potential confounding factors that are common in transplant research with particular focus on the impact on evaluating transplant center performance. Recent Findings There are numerous studies documenting evidence of factors that impact outcomes of transplant patients that are not consistently available for risk adjustment in observational transplant research studies. These factors may vary between patient groups and transplant centers and as such, potential confounders affected accurate interpretation of research findings. Summary Researchers and consumers of research studies should be aware of the existence of potential confounding factors and the impact on inferences derived from observational research in transplantation. Confounding factors are common in observational research in this field and strategies to augment existing data, mitigate the effect of confounders in analyses and appropriately interpret findings in the context of confounders are important.

Introduction: Currently in the United States, deceased donor liver transplant (DDLT) allocation priority is based on the model for end-stage liver disease including sodium (MELD-Na) score. The United Network for organ sharing's 'Share-15' policy states that candidates with MELD-Na scores of 15 or greater have priority to receive local organ offers compared to candidates with lower MELD-Na scores. Since the inception of this policy, major changes in the primary etiologies of end-stage liver disease have occurred and previous assumptions need to be recalibrated. Methods: The authors retrospectively analyzed the Scientific Registry of Transplant Recipients database between 2012 and 2021 to determine life years saved by DDLT at each interval of MELD-Na score and the time-to-equal risk and time-to-equal survival versus remaining on the waitlist. The authors stratified our analysis by MELD exception points, primary disease etiology, and MELD score. Results: On aggregate, compared to remaining on the waitlist, a significant 1-year survival advantage of DDLT at MELD-Na scores as low as 12 was found. The median life years saved at this score after a liver transplant was estimated to be greater than 9 years. While the total life years saved were comparable across all MELD-Na scores, the time-to-equal risk and time-to-equal survival decreased exponentially as MELD-Na scores increased. Conclusion: Herein, the authors challenge the perception as to the timing of DDLT and when that benefit occurs. The national liver allocation policy is transitioning to a continuous distribution framework and these data will be instrumental to defining the attributes of the continuos allocation score.

ABSTRACT Importance Although the Organ Procurement and Transplantation network provides structured policies and guidance for waitlisted cardiac transplant patients, the heart transplantation community lacks a mathematical model that can accurately estimate the short-term risk of death associated with being waitlisted. Importantly, the CHARM score provides a risk management and ranking system for patients based on a well-defined and sensitive medical urgency metric. Objective We had three primary objectives in completing this study. First, to increase relevance and applicability, we selected patient attributes that were clinically justified and readily available. Second, we designed and implemented an intuitive, formal system that accurately defined the relative risk of death while being waitlisted at 30-day, 90-day, and 1-year censoring periods. Third, we developed and validated a medical urgency metric that is intuitive, practical, and can be implemented nationally. Design We present a multivariable, prognostic model and risk management strategy for adult waitlisted heart transplant patients (N=1,965) from the Scientific Registry of Transplant Recipients (SRTR) database that were waitlisted from January 1, 2008, to September 2, 2022. To independently validate each model, we randomly split this cohort into a discovery set (N=1,174) and validation set (N=784). Twelve independent patient attributes were selected, and three linear regression formulas were derived to estimate and rank the relative risk of dying while waitlisted. Four independent validation methods were used to measure each model’s performance as a classifier and ranking system. Setting The United States Participants This cohort (N=1,965) consisted of adult heart transplant candidates without missing laboratory data who were placed on the waitlist from January 1, 2008, to September 2, 2022. Patients listed for multi-organ transplantation were excluded. Patients with missing laboratory data were analyzed independently. Exposures The short-term risk of death remaining on the heart transplant waitlist. Main Outcomes and Measures The primary outcome of this study was the design, development, and validation of a formal risk management system for waitlisted heart transplant candidates experiencing end-stage heart failure. We derived three linear regression formulas and calibrated a seven-tiered risk index to accurately rank patients who were more likely to die on the waitlist at 30-day (30D), 90-day (90D), and 1-year (1Y) censoring periods. Four independent validation methods were used to measure each model’s classification and ranking performance. Results Using six interaction terms, we applied the 5-fold cross-validation procedure to the CHARM to discover an area under the ROC curve of 96.4%, 90.4.%, and 78% for the 30D, 90D, and 1Y models, respectively. The mean positive predictive values of the tiered risk system were 99.2% (30D), 94.1% (90D), and 88% (1Y). Risk indices for all three models were >99% correlated to the observed mortality rate across the seven tiers for the 30D, 90D, and 1Y models. Conclusions and Relevance We designed, implemented, and validated an intuitive and formal risk scoring and ranking system which is ideal for prioritizing waitlisted heart failure patients based on a well-defined medical urgency metric. The CHARM score provides extreme sensitivity in predicting short-term mortality outcomes. The CHARM score is extensible to larger patient populations experiencing end-stage heart failure. KEY POINTS Question Can pre-operative patient characteristics be used to develop a formal system to accurately estimate, rank, and predict the relative short-term mortality of waitlisted heart transplant patients? Findings Using twelve patient attributes, we derived three linear regression equations to accurately predict the 30-day, 90-day, and 1-year mortality of waitlisted heart transplant patients. We developed and calibrated a seven-tiered risk index for each model that was 99% correlated to the observed mortality rate. Using several independent validation methods, we achieved extreme sensitivity (>98%) in ordinally ranking patient groups who were more likely to survive 30 days on the waitlist. Model performance was measured using the area under the receiver operating characteristic (ROC) curve. Using six interaction terms, the area under the ROC curve was 96.4% (30-day), 90.4% (90-day), and 78% (1-year). Meaning Our models accurately discriminate among patient subgroups who are more likely to die while waitlisted. Because our tiered ranking system is simple, extremely sensitive, and well calibrated, it is ideal for prioritizing waitlisted heart transplant patients based on a well-defined medical urgency score. These models are generalized and therefore extensible to defining medical urgency in larger patient populations experiencing end-stage heart failure.

On July 14, 2022, the Organ Procurement and Transplantation Network's (OPTN) Membership and Professional Standards Committee (MPSC) approved bylaws including two new post-transplant performance evaluation metrics, the 90-day (90D) and 1-year conditional on the 90-day (1YC90D) graft survival hazard ratio (HR). These metrics have replaced the previous 1-year (1Y) unconditional, post-transplant graft survival HR and are used to nationally rank and identify programs for MPSC review. The MPSC's policies have major implications for all transplant programs, providers, and patients across the United States. Herein we show two significant limitations with the new evaluation criteria, arbitrary censoring periods and interdependence in the new performance metrics. We have demonstrated a strong and consistent inverse correlation between the new evaluation metrics, thus proving a lack of independence. Moreover, these two evaluation criteria are interdependent even at nominal HRs. Thus, the 90D cohort can be used to accurately predict whether the 1YC90D is above or below a given HR threshold. This could alter practice behaviors and the timing of patient event reporting, which may result in many unintended consequences related to clinical practice. Here we provide the first evidence that this new evaluation system will lead to a significant increase in the number of programs flagged for MPSC review. When this occurs, the cost of operating a transplant program will increase without a clear demonstration of an increased accuracy in identifying problematic programs.

ABSTRACT Background Reliable detection and accurate genotyping of structural variants (SVs) and insertion/deletions (indels) from whole-genome sequence (WGS) data is a significant challenge. We present a protocol for variant calling, quality control, call merging, sensitivity analysis, in silico genotyping, and laboratory validation protocols for generating a high-quality deletion call set from whole genome sequences as part of the Alzheimer’s Disease Sequencing Project (ADSP). This dataset contains 578 individuals from 111 families. Methods We applied two complementary pipelines (Scalpel and Parliament) for SV/indel calling, break-point refinement, genotyping, and local reassembly to produce a high-quality annotated call set. Sensitivity was measured in sample replicates (N=9) for all callers using in silico variant spike-in for a wide range of event sizes. We focused on deletions because these events were more reliably called. To evaluate caller specificity, we developed a novel metric called the D-score that leverages deletion sharing frequencies within and outside of families to rank recurring deletions. Assessment of overall quality across size bins was measured with the kinship coefficient. Individual callers were evaluated for computational cost, performance, sensitivity, and specificity. Quality of calls were evaluated by Sanger sequencing of predicted loss-of-function (LOF) variants, variants near AD candidate genes, and randomly selected genome-wide deletions ranging from 2 to 17,000 bp. Results We generated a high-quality deletion call set across a wide range of event sizes consisting of 152,301 deletions with an average of 263 per genome. A total of 114 of 146 predicted deletions (78.1%) were validated by Sanger sequencing. Scalpel was more accurate in calling deletions ≤100 bp, whereas for Parliament, sensitivity was improved for deletions > 900 bp. We validated 83.0% (88/106) and 72.5% (37/51) of calls made by Scalpel and Parliament, respectively. Eleven deletions called by both Parliament and Scalpel in the 101-900 bin were tested and all were confirmed by Sanger sequencing. Conclusions We developed a flexible protocol to assess the quality of deletion detection across a wide range of sizes. We also generated a truth set of Sanger sequencing validated deletions with precise breakpoints covering a wide spectrum of sizes between 1 and 17,000 bp.

ABSTRACTTo extract biological meaning from transcriptomics analysis, investigators almost exclusively rely on biological annotation databases and the subsequent associations made between gene products and ontology terms (i.e., gene ontology analysis). Although there is ease and utility to this approach, multiple hypothesis testing methods such as differential expression analysis are performed in the absence ofin silicovalidation, and downstream gene ontology analysis methods lack precision and propagate Type I error. Therefore, we present a novel R package, Functional Association Vectors (FACTORs), that begins to address some of the common pitfalls associated with functional association studies in transcriptomics research. FACTORs are vectorized containers for directly comparing and testing congruent functional association statistics at the molecular level. Our goal was to develop novel methodology an R software package to allow for the experimental validation of differentially expressed genes that are conserved across studies and to reduce Type I error in the down-stream functional analysis of these signals. FACTORs are generalizable and flexible, allowing for any association statistic such as log fold change (logFC), t-statistic, p-value, or other functional significance score. To demonstrate utility of FACTORs in the cross-study validation of global mRNA expression profiling, we used differential expression analysis summary statistics obtained from two studies with publicly available transcriptomic data. Through this demonstration we show FACTORs provide a more precise and generalizable functional hypothesis testing methodology and data reduction approach that directly tests functional association statistics at the molecular level, across experiments.AUTHOR SUMMARYWe present a novel statistical methodology and software tool that can be used to validate transcriptomic data across studies. FACTORs is available as an R package and will aid in transcriptomic data reduction, identifying gene expression profiles that are conserved across studies, and improving the precision and generalizability of functional association studies. We propose FACTORs as a generalizable methodology for reducing Type I error and increasing the biological relevance of functional associations in transcriptomics studies.

IMPORTANCE Despite the acceptance of living-donor liver transplant (LDLT) as a lifesaving procedure for end-stage liver disease, it remains underused in the United States. Quantification of lifetime survival benefit and the Model for End-stage Liver Disease incorporating sodium levels (MELD-Na) score range at which benefit outweighs risk in LDLT is necessary to demonstrate its safety and effectiveness. OBJECTIVE To assess the survival benefit, life-years saved, and the MELD-Na score at which that survival benefit was obtained for individuals who received an LDLT compared with that for individuals who remained on the wait list. DESIGN, SETTING, AND PARTICIPANTS This case-control study was a retrospective, secondary analysis of the Scientific Registry of Transplant Recipients database of 119 275 US liver transplant candidates and recipients from January 1, 2012, to September 2, 2021. Liver transplant candidates aged 18 years or older who were assigned to the wait list (N = 116 455) or received LDLT (N = 2820) were included. Patients listed for retransplant or multiorgan transplant and those with prior kidney or liver transplants were excluded. EXPOSURES Living-donor liver transplant vs remaining on the wait list. MAIN OUTCOMES AND MEASURES The primary outcome of this study was life-years saved from receiving an LDLT. Secondary outcomes included 1-year relative mortality and risk, time to equal risk, time to equal survival, and the MELD-Na score at which that survival benefit was obtained for individuals who received an LDLT compared with that for individuals who remained on the wait list. MELD-Na score ranges from 6 to 40 and is well correlated with short-term survival. Higher MELD-Na scores (>20) are associated with an increased risk of death. RESULTS The mean (SD) age of the 119 275 study participants was 55.1 (11.2) years, 63% were male, 0.9% were American Indian or Alaska Native, 4.3% were Asian, 8.2% were Black or African American, 15.8% were Hispanic or Latino, 0.2% were Native Hawaiian or Other Pacific Islander, and 70.2% were White. Mortality risk and survival models confirmed a significant survival benefit for patients receiving an LDLT who had a MELD-Na score of 11 or higher (adjusted hazard ratio, 0.64 [95% CI, 0.47-0.88]; P = .006). Living-donor liver transplant recipients gained an additional 13 to 17 life-years compared with patients who never received an LDLT. CONCLUSIONS AND RELEVANCE An LDLT is associated with a substantial survival benefit to patients with end-stage liver disease even at MELD-Na scores as low as 11. The findings of this study suggest that the life-years gained are comparable to or greater than those conferred by any other lifesaving procedure or by a deceased-donor liver transplant. This study's findings challenge current perceptions regarding when LDLT survival benefit occurs.

The Alzheimer's Disease Sequencing Project (ADSP) performed whole genome sequencing (WGS) of 584 subjects from 111 multiplex families at three sequencing centers. Genotype calling of single nucleotide variants (SNVs) and insertion-deletion variants (indels) was performed centrally using GATK-HaplotypeCaller and Atlas V2. The ADSP Quality Control (QC) Working Group applied QC protocols to project-level variant call format files (VCFs) from each pipeline, and developed and implemented a novel protocol, termed "consensus calling," to combine genotype calls from both pipelines into a single high-quality set. QC was applied to autosomal bi-allelic SNVs and indels, and included pipeline-recommended QC filters, variant-level QC, and sample-level QC. Low-quality variants or genotypes were excluded, and sample outliers were noted. Quality was assessed by examining Mendelian inconsistencies (MIs) among 67 parent-offspring pairs, and MIs were used to establish additional genotype-specific filters for GATK calls. After QC, 578 subjects remained. Pipeline-specific QC excluded ~12.0% of GATK and 14.5% of Atlas SNVs. Between pipelines, ~91% of SNV genotypes across all QCed variants were concordant; 4.23% and 4.56% of genotypes were exclusive to Atlas or GATK, respectively; the remaining ~0.01% of discordant genotypes were excluded. For indels, variant-level QC excluded ~36.8% of GATK and 35.3% of Atlas indels. Between pipelines, ~55.6% of indel genotypes were concordant; while 10.3% and 28.3% were exclusive to Atlas or GATK, respectively; and ~0.29% of discordant genotypes were. The final WGS consensus dataset contains 27,896,774 SNVs and 3,133,926 indels and is publicly available.