Robust inference of population structure from next-generation sequencing data with systematic differences in sequencing.

Motivation: Inferring population structure is important for both population genetics and genetic epidemiology. Principal components analysis (PCA) has been effective in ascertaining population structure with array genotype data but can be difficult to use with sequencing data, especially when low depth leads to uncertainty in called genotypes. Because PCA is sensitive to differences in variability, PCA using sequencing data can result in components that correspond to differences in sequencing quality (read depth and error rate), rather than differences in population structure. We demonstrate that even existing methods for PCA specifically designed for sequencing data can still yield biased conclusions when used with data having sequencing properties that are systematically different across different groups of samples (i.e. sequencing groups). This situation can arise in population genetics when combining sequencing data from different studies, or in genetic epidemiology when using historical controls such as samples from the 1000 Genomes Project. Results: To allow inference on population structure using PCA in these situations, we provide an approach that is based on using sequencing reads directly without calling genotypes. Our approach is to adjust the data from different sequencing groups to have the same read depth and error rate so that PCA does not generate spurious components representing sequencing quality. To accomplish this, we have developed a subsampling procedure to match the depth distributions in different sequencing groups, and a read-flipping procedure to match the error rates. We average over subsamples and read flips to minimize loss of information. We demonstrate the utility of our approach using two datasets from 1000 Genomes, and further evaluate it using simulation studies. Availability and implementation: TASER-PC software is publicly available at http://web1.sph.emory.edu/users/yhu30/software.html. Contact: yijuan.hu@emory.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between Cases and Controls.

Next-generation sequencing of DNA provides an unprecedented opportunity to discover rare genetic variants associated with complex diseases and traits. However, the common practice of first calling underlying genotypes and then treating the called values as known is prone to false positive findings, especially when genotyping errors are systematically different between cases and controls. This happens whenever cases and controls are sequenced at different depths, on different platforms, or in different batches. In this article, we provide a likelihood-based approach to testing rare variant associations that directly models sequencing reads without calling genotypes. We consider the (weighted) burden test statistic, which is the (weighted) sum of the score statistic for assessing effects of individual variants on the trait of interest. Because variant locations are unknown, we develop a simple, computationally efficient screening algorithm to estimate the loci that are variants. Because our burden statistic may not have mean zero after screening, we develop a novel bootstrap procedure for assessing the significance of the burden statistic. We demonstrate through extensive simulation studies that the proposed tests are robust to a wide range of differential sequencing qualities between cases and controls, and are at least as powerful as the standard genotype calling approach when the latter controls type I error. An application to the UK10K data reveals novel rare variants in gene BTBD18 associated with childhood onset obesity. The relevant software is freely available.

PhredEM: a phred-score-informed genotype-calling approach for next-generation sequencing studies.

A fundamental challenge in analyzing next-generation sequencing (NGS) data is to determine an individual's genotype accurately, as the accuracy of the inferred genotype is essential to downstream analyses. Correctly estimating the base-calling error rate is critical to accurate genotype calls. Phred scores that accompany each call can be used to decide which calls are reliable. Some genotype callers, such as GATK and SAMtools, directly calculate the base-calling error rates from phred scores or recalibrated base quality scores. Others, such as SeqEM, estimate error rates from the read data without using any quality scores. It is also a common quality control procedure to filter out reads with low phred scores. However, choosing an appropriate phred score threshold is problematic as a too high threshold may lose data, while a too low threshold may introduce errors. We propose a new likelihood-based genotype-calling approach that exploits all reads and estimates the per-base error rates by incorporating phred scores through a logistic regression model. The approach, which we call PhredEM, uses the expectation-maximization (EM) algorithm to obtain consistent estimates of genotype frequencies and logistic regression parameters. It also includes a simple, computationally efficient screening algorithm to identify loci that are estimated to be monomorphic, so that only loci estimated to be nonmonomorphic require application of the EM algorithm. Like GATK, PhredEM can be used together with a linkage-disequilibrium-based method such as Beagle, which can further improve genotype calling as a refinement step. We evaluate the performance of PhredEM using both simulated data and real sequencing data from the UK10K project and the 1000 Genomes project. The results demonstrate that PhredEM performs better than either GATK or SeqEM, and that PhredEM is an improved, robust, and widely applicable genotype-calling approach for NGS studies. The relevant software is freely available.

Baroreflex dysfunction and augmented sympathetic nerve responses during mental stress in veterans with post-traumatic stress disorder.

KEY POINTS: Patients with post-traumatic stress disorder (PTSD) are at a significantly higher risk of developing hypertension and cardiovascular disease. The mechanisms underlying this increased risk are not known. Studies have suggested that PTSD patients have an overactive sympathetic nervous system (SNS) that could contribute to cardiovascular risk; however, sympathetic function has not previously been rigorously evaluated in PTSD patients. Using direct measurements of sympathetic nerve activity and pharmacological manipulation of blood pressure, we show that veterans with PTSD have augmented SNS and haemodynamic reactivity during both combat-related and non-combat related mental stress, impaired sympathetic and cardiovagal baroreflex sensitivity, and increased inflammation. Identifying the mechanisms contributing to increased cardiovascular (CV) risk in PTSD will pave the way for developing interventions to improve sympathetic function and reduce CV risk in these patients. ABSTRACT: Post-traumatic stress disorder (PTSD) is associated with increased cardiovascular (CV) risk. We tested the hypothesis that PTSD patients have augmented sympathetic nervous system (SNS) and haemodynamic reactivity during mental stress, as well as impaired arterial baroreflex sensitivity (BRS). Fourteen otherwise healthy Veterans with combat-related PTSD were compared with 14 matched Controls without PTSD.  Muscle sympathetic nerve activity (MSNA), continuous blood pressure (BP) and electrocardiography were measured at baseline, as well as during two types of mental stress:  combat-related mental stress using virtual reality combat exposure (VRCE) and non-combat related stress using mental arithmetic (MA). A cold pressor test (CPT) was administered for comparison. BRS was tested using pharmacological manipulation of BP via the Modified Oxford technique at rest and during VRCE. Blood samples were analysed for inflammatory biomarkers. Baseline characteristics, MSNA and haemodynamics were similar between the groups. In PTSD vs. Controls, MSNA (+8.2 ± 1.0 vs. +1.2 ± 1.3 bursts min-1 , P < 0.001) and heart rate responses (+3.2 ± 1.1 vs. -2.3 ± 1.0 beats min-1 , P = 0.003) were significantly augmented during VRCE.  Similarly, in PTSD vs. Controls, MSNA (+21.0 ± 2.6 vs. +6.7 ± 1.5 bursts min-1 , P < 0.001) and diastolic BP responses (+6.3 ± 1.0 vs. +3.5 ± 1.0 mmHg, P = 0.011) were significantly augmented during MA but not during CPT (P = not significant). In the PTSD group, sympathetic BRS (-1.2 ± 0.2 vs. -2.0 ± 0.3 burst incidence mmHg-1 , P = 0.026) and cardiovagal BRS (9.5 ± 1.4 vs. 23.6 ± 4.3 ms mmHg-1 , P = 0.008) were significantly blunted at rest. PTSD patients had significantly higher highly sensitive-C-reactive protein levels compared to Controls (2.1 ± 0.4 vs. 1.0 ± 0.3 mg L-1 , P = 0.047). Augmented SNS and haemodynamic responses to mental stress, blunted BRS and inflammation may contribute to an increased CV risk in PTSD.

Endothelial dysfunction correlates with exaggerated exercise pressor response during whole body maximal exercise in chronic kidney disease.

Chronic kidney disease (CKD) patients have exercise intolerance associated with increased cardiovascular mortality. Previous studies demonstrate that blood pressure (BP) and sympathetic nerve responses to handgrip exercise are exaggerated in CKD. These patients also have decreased nitric oxide (NO) bioavailability and endothelial dysfunction, which could potentially lead to an impaired ability to vasodilate during exercise. We hypothesized that CKD patients have exaggerated BP responses during maximal whole body exercise and that endothelial dysfunction correlates with greater exercise pressor responses in these patients. Brachial artery flow-mediated dilation (FMD) was assessed before maximal treadmill exercise in 56 participants: 38 CKD (56.7 ± 1.2 yr old, 38 men) and 21 controls (52.8 ± 1.8 yr old, 20 men). During maximal treadmill exercise, the slope-of-rise in systolic BP (+10.32 vs. +7.75 mmHg/stage, P < 0.001), mean arterial pressure (+3.50 vs. +2.63 mmHg/stage, P = 0.004), and heart rate (+11.87 vs. +10.69 beats·min-1·stage-1, P = 0.031) was significantly greater in CKD compared with controls. Baseline FMD was significantly lower in CKD (2.76 ± 0.42% vs. 5.84 ± 0.97%, P = 0.008). Lower FMD values were significantly associated with a higher slope-of-rise in systolic BP (+11.05 vs. 8.71 mmHg/stage, P = 0.003) during exercise in CKD, as well as poorer exercise capacity measured as peak oxygen uptake (V̇o2peak; 19.47 ± 1.47 vs. 24.57 ± 1.51 ml·min-1·kg-1, P < 0.001). These findings demonstrate that low FMD in CKD correlates with augmented BP responses during exercise and lower V̇o2peak, suggesting that endothelial dysfunction may contribute to exaggerated exercise pressor responses and poor exercise capacity in CKD patients.

Tetrahydrobiopterin ameliorates the exaggerated exercise pressor response in patients with chronic kidney disease: a randomized controlled trial.

Chronic kidney disease (CKD) patients have an exaggerated increase in blood pressure (BP) during rhythmic handgrip exercise (RHG 20%) and static handgrip exercise (SHG 30%). Nitric oxide levels increase during exercise and help prevent excessive hypertension by both increasing vasodilation and reducing sympathetic nerve activity (SNA). Therefore, we hypothesized that tetrahydrobiopterin (BH4), an essential cofactor for nitric oxide synthase, would ameliorate the exaggerated exercise pressor response in CKD patients. In a randomized, double-blinded, placebo-controlled trial, we tested the effects of 12 wk of sapropterin dihydrochloride (6R-BH4; n = 18) versus placebo (n = 14) treatement on BP and muscle SNA (MSNA) responses during RHG 20% and SHG 30% in CKD patients. The 6R-BH4-treated group had a significantly lower systolic BP (+6 ± 1 vs. +13 ± 2 mmHg, P = 0.002) and mean arterial pressure response (+5 ± 1 vs. +10 ± 2 mmHg, P = 0.020) during RHG 20% and a significantly lower systolic BP response (+19 ± 3 vs. +28 ± 3 mmHg, P = 0.043) during SHG 30%. Under baseline conditions, there was no significant difference in MSNA responses between the groups; however, when the BP response during exercise was equalized between the groups using nitroprusside, the 6R-BH4-treated group had a significantly lower MSNA response during RHG 20% (6R-BH4 vs. placebo, +12 ± 1 vs. +21 ± 2 bursts/min, P = 0.004) but not during SHG 30%. These findings suggest that 6R-BH4 ameliorates the augmented BP response during RHG 20% and SHG 30% in CKD patients. A reduction in reflex activation of SNA may contribute to the decreased exercise pressor response during RHG 20% but not during SHG 30% in CKD patients.

Tetrahydrobiopterin lowers muscle sympathetic nerve activity and improves augmentation index in patients with chronic kidney disease.

Chronic kidney disease (CKD) is characterized by overactivation of the sympathetic nervous system (SNS) that contributes to cardiovascular risk. Decreased nitric oxide (NO) bioavailability is a major factor contributing to SNS overactivity in CKD, since reduced neuronal NO leads to increased central SNS activity. Tetrahydrobiopterin (BH4) is an essential cofactor for nitric oxide synthase that increases NO bioavailability in experimental models of CKD. We conducted a randomized, double-blinded, placebo-controlled trial testing the benefits of oral sapropterin dihydrochloride (6R-BH4, a synthetic form of BH4) in CKD. 36 patients with CKD and hypertension were randomized to 12 wk of 1) 200 mg 6R-BH4 twice daily + 1 mg folic acid once daily; vs. 2) placebo + folic acid. The primary endpoint was a change in resting muscle sympathetic nerve activity (MSNA). Secondary endpoints included arterial stiffness using pulse wave velocity (PWV) and augmentation index (AIx), endothelial function using brachial artery flow-mediated dilation and endothelial progenitor cells, endothelium-independent vasodilatation (EID), microalbuminuria, and blood pressure. We observed a significant reduction in MSNA after 12 wk of 6R-BH4 (-7.5 ± 2.1 bursts/min vs. +3.2 ± 1.3 bursts/min; P = 0.003). We also observed a significant improvement in AIx (by -5.8 ± 2.0% vs. +1.8 ± 1.7 in the placebo group, P = 0.007). EID increased significantly (by +2.0 ± 0.59%; P = 0.004) in the 6R-BH4 group, but there was no change in endothelial function. There was a trend toward a reduction in diastolic blood pressure by -4 ± 3 mmHg at 12 wk with 6R-BH4 (P = 0.055). 6R-BH4 treatment may have beneficial effects on SNS activity and central pulse wave reflections in hypertensive patients with CKD.

ROC Curve Analysis in the Presence of Imperfect Reference Standards.

The receiver operating characteristic (ROC) curve is an important tool for the evaluation and comparison of predictive models when the outcome is binary. If the class membership of the outcomes are known, ROC can be constructed for a model, and the ROC with greater area under the curve (AUC) indicates better performance. However in practice, imperfect reference standards often exist, in which class membership of every data point are not fully determined. This situation is especially prevalent in high-throughput biomedical data because obtaining perfect reference standards for all data points is either too costly or technically impractical. To construct ROC curves for these data, the common practice is to either ignore the uncertainties in references, or remove data points with high uncertainties. Such approaches may cause bias to the ROC curves and generate misleading results in method evaluation. Here we present a framework to incorporate membership uncertainties into the construction of ROC curve, termed the expected ROC or "eROC" curve. We develop an efficient procedure for the estimation of eROC curve. The advantages of using eROC are demonstrated using simulated and real data.

Impact of Selection Bias on Estimation of Subsequent Event Risk.

BACKGROUND: Studies of recurrent or subsequent disease events may be susceptible to bias caused by selection of subjects who both experience and survive the primary indexing event. Currently, the magnitude of any selection bias, particularly for subsequent time-to-event analysis in genetic association studies, is unknown. METHODS AND RESULTS: We used empirically inspired simulation studies to explore the impact of selection bias on the marginal hazard ratio for risk of subsequent events among those with established coronary heart disease. The extent of selection bias was determined by the magnitudes of genetic and nongenetic effects on the indexing (first) coronary heart disease event. Unless the genetic hazard ratio was unrealistically large (>1.6 per allele) and assuming the sum of all nongenetic hazard ratios was <10, bias was usually <10% (downward toward the null). Despite the low bias, the probability that a confidence interval included the true effect decreased (undercoverage) with increasing sample size because of increasing precision. Importantly, false-positive rates were not affected by selection bias. CONCLUSIONS: In most empirical settings, selection bias is expected to have a limited impact on genetic effect estimates of subsequent event risk. Nevertheless, because of undercoverage increasing with sample size, most confidence intervals will be over precise (not wide enough). When there is no effect modification by history of coronary heart disease, the false-positive rates of association tests will be close to nominal.