A method to estimate the contribution of rare coding variants to complex trait heritability.

It has been postulated that rare coding variants (RVs; MAF < 0.01) contribute to the "missing" heritability of complex traits. We developed a framework, the Rare variant heritability (RARity) estimator, to assess RV heritability (h2RV) without assuming a particular genetic architecture. We applied RARity to 31 complex traits in the UK Biobank (n = 167,348) and showed that gene-level RV aggregation suffers from 79% (95% CI: 68-93%) loss of h2RV. Using unaggregated variants, 27 traits had h2RV > 5%, with height having the highest h2RV at 21.9% (95% CI: 19.0-24.8%). The total heritability, including common and rare variants, recovered pedigree-based estimates for 11 traits. RARity can estimate gene-level h2RV, enabling the assessment of gene-level characteristics and revealing 11, previously unreported, gene-phenotype relationships. Finally, we demonstrated that in silico pathogenicity prediction (variant-level) and gene-level annotations do not generally enrich for RVs that over-contribute to complex trait variance, and thus, innovative methods are needed to predict RV functionality.

B-Type Fumonisins in Post-Fermented Tea: Occurrence and Consumer Dietary Exposure in Guangxi, China.

Post-fermented tea (PFT), a commonly consumed beverage worldwide, is characterized by the rapid growth of its microbial groups and the substantial changes they undergo. Consequently, PFT may contain mycotoxins such as B-type fumonisins (FBs). This study aimed to assess the intake of FBs through the consumption of PFT among consumers in Guangxi, China. A novel quantitative method using high-performance liquid chromatography-mass spectrometry was used to determine the FB concentration in PFT products. Additionally, a PFT consumption survey was conducted using a face-to-face questionnaire, recording their body weight and PFT consumption patterns based on a three-day dietary recall method. Finally, hazard index was calculated to estimate the health risk of FBs from the consumption of PFT products in Guangxi. The results revealed that the occurrence of FBs in PFT was 20% (24/120), with a concentration ranging from 2.14 to 18.28 µg/kg. The results of the survey showed that the average daily consumption of PFT by consumers was 9.19 ± 11.14 g. The deterministic risk assessment revealed that only 0.026% of the provisional maximum tolerable daily intake of FBs was consumed through PFT, indicating that FB contamination in PFT is not a public health risk.

Surrogate Adiposity Markers and Mortality.

Importance: Body mass index (BMI) is an easily obtained adiposity surrogate. However, there is variability in body composition and adipose tissue distribution between individuals with the same BMI, and there is controversy regarding the BMI associated with the lowest mortality risk. Objective: To evaluate which of BMI, fat mass index (FMI), and waist-to-hip (WHR) has the strongest and most consistent association with mortality. Design, Setting, and Participant: This cohort study used incident deaths from the UK Biobank (UKB; 2006-2022), which includes data from 22 clinical assessment centers across the United Kingdom. UKB British participants of British White ancestry (N = 387 672) were partitioned into a discovery cohort (n = 337 078) and validation cohort (n = 50 594), with the latter consisting of 25 297 deaths and 25 297 controls. The discovery cohort was used to derive genetically determined adiposity measures while the validation cohort was used for analyses. Exposure-outcome associations were analyzed through observational and mendelian randomization (MR) analyses. Exposures: BMI, FMI, and WHR. Main Outcomes and Measures: All-cause and cause-specific (cancer, cardiovascular disease [CVD], respiratory disease, or other causes) mortality. Results: There were 387 672 and 50 594 participants in our observational (mean [SD] age, 56.9 [8.0] years; 177 340 [45.9%] male, 210 332 [54.2%], female), and MR (mean [SD] age, 61.6 [6.2] years; 30 031 [59.3%] male, 20 563 [40.6%], female) analyses, respectively. Associations between measured BMI and FMI with all-cause mortality were J-shaped, whereas the association of WHR with all-cause mortality was linear using the hazard ratio (HR) scale (HR per SD increase of WHR, 1.41 [95% CI, 1.38-1.43]). Genetically determined WHR had a stronger association with all-cause mortality than BMI (odds ratio [OR] per SD increase of WHR, 1.51 [95% CI, 1.32-1.72]; OR per SD increase of BMI, 1.29 [95% CI, 1.20-1.38]; P for heterogeneity = .02). This association was stronger in male than female participants (OR, 1.89 [95% CI, 1.54-2.32]; P for heterogeneity = .01). Unlike BMI or FMI, the genetically determined WHR-all-cause mortality association was consistent irrespective of observed BMI. Conclusions and Relevance: In this cohort study, WHR had the strongest and most consistent association with mortality irrespective of BMI. Clinical recommendations should consider focusing on adiposity distribution compared with mass.

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets.

Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 - 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.

Cost-Effectiveness of Polygenic Risk Scores to Guide Statin Therapy for Cardiovascular Disease Prevention.

BACKGROUND: Atherosclerotic cardiovascular diseases (CVDs) are leading causes of death despite effective therapies and result in unnecessary morbidity and mortality throughout the world. We aimed to investigate the cost-effectiveness of polygenic risk scores (PRS) to guide statin therapy for Canadians with intermediate CVD risk and model its economic outlook. METHODS: This cost-utility analysis was conducted using UK Biobank prospective cohort study participants, with recruitment from 2006 to 2010, and at least 10 years of follow-up. We included nonrelated white British-descent participants (n=96 116) at intermediate CVD risk with no prior lipid lowering medication or statin-indicated conditions. A coronary artery disease PRS was used to inform decision to use statins. The effects of statin therapy with and without PRS, as well as CVD events were modelled to determine the incremental cost-effectiveness ratio from a Canadian public health care perspective. We discounted future costs and quality-adjusted life-years by 1.5% annually. RESULTS: The optimal economic strategy was when intermediate risk individuals with a PRS in the top 70% are eligible for statins while the lowest 1% are excluded. Base-case analysis at a genotyping cost of $70 produced an incremental cost-effectiveness ratio of $172 906 (143 685 USD) per quality-adjusted life-year. In the probabilistic sensitivity analysis, the intervention has approximately a 50% probability of being cost-effective at $179 100 (148 749 USD) per quality-adjusted life-year. At a $0 genotyping cost, representing individuals with existing genotyping information, PRS-guided strategies dominated standard care when 12% of the lowest PRS individuals were withheld from statins. With improved PRS predictive performance and lower genotyping costs, the incremental cost-effectiveness ratio demonstrates possible cost-effectiveness under thresholds of $150 000 and possibly $50 000 per quality-adjusted life-year. CONCLUSIONS: This study suggests that using PRS alongside existing guidelines might be cost-effective for CVD. Stronger predictiveness combined with decreased cost of PRS could further improve cost-effectiveness, providing an economic basis for its inclusion into clinical care.

Fine-tuning of Genome-Wide Polygenic Risk Scores and Prediction of Gestational Diabetes in South Asian Women.

Gestational diabetes Mellitus (GDM) affects 1 in 7 births and is associated with numerous adverse health outcomes for both mother and child. GDM is suspected to share a large common genetic background with type 2 diabetes (T2D). The aim of our study was to characterize different GDM polygenic risk scores (PRSs) and test their association with GDM using data from the South Asian Birth Cohort (START). PRSs were derived for 832 South Asian women from START using the pruning and thresholding (P + T), LDpred, and GraBLD methods. Weights were derived from a multi-ethnic and a white Caucasian study of the DIAGRAM consortium. GDM status was defined using South Asian-specific glucose values in response to an oral glucose tolerance test. Association with GDM was tested using logistic regression. Results were replicated in South Asian women from the UK Biobank (UKB) study. The top ranking P + T, LDpred and GraBLD PRSs were all based on DIAGRAM's multi-ethnic study. The best PRS was highly associated with GDM in START (AUC = 0.62, OR = 1.60 [95% CI = 1.44-1.69]), and in South Asian women from UKB (AUC = 0.65, OR = 1.69 [95% CI = 1.28-2.24]). Our results highlight the importance of combining genome-wide genotypes and summary statistics from large multi-ethnic studies to optimize PRSs in South Asians.

Effects of withering on the main physical properties of withered tea leaves and the sensory quality of congou black tea.

To explore the relationship between the moisture content of withered tea leaves and their physical properties (i.e., elasticity, plasticity, flexibility, and texture) during withering, texture analyzer was employed to test the elasticity and flexibility of withered tea leaves with different moisture contents. The texture was evaluated by computer vision technology. The withered tea leaves with different moisture contents were used to process congou black tea, which was then subjected to sensory evaluation. Results showed that good elasticity, optimal flexibility, and plasticity were achieved when the moisture content of the withered tea leaves of Fudingdabai comprising two leaves and one bud varied arranging from 65.51 to 61.48%. The sensory evaluation of congou black tea revealed that moderate withering was better than long-term withering and that both moderate and long-term withering were better than no withering during processing. The moisture content was significantly correlated with the flexibility and plasticity of the withered tea leaves. Fresh tea leaves undergoing moderate withering with moisture content of 65.51-61.48% to process congou black tea, good tea shape and liquor color were achieved. This study provided new evidence that the moisture content of withered tea leaves significantly affected the quality of black tea.

Author Correction: A machine-learning heuristic to improve gene score prediction of polygenic traits.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Analytical strategies to include the X-chromosome in variance heterogeneity analyses: Evidence for trait-specific polygenic variance structure.

Genotype-stratified variance of a quantitative trait could differ in the presence of gene-gene or gene-environment interactions. Genetic markers associated with phenotypic variance are thus considered promising candidates for follow-up interaction or joint location-scale analyses. However, as in studies of main effects, the X-chromosome is routinely excluded from "whole-genome" scans due to analytical challenges. Specifically, as males carry only one copy of the X-chromosome, the inherent sex-genotype dependency could bias the trait-genotype association, through sexual dimorphism in quantitative traits with sex-specific means or variances. Here we investigate phenotypic variance heterogeneity associated with X-chromosome single nucleotide polymorphisms (SNPs) and propose valid and powerful strategies. Among those, a generalized Levene's test has adequate power and remains robust to sexual dimorphism. An alternative approach is a sex-stratified analysis but at the cost of slightly reduced power and modeling flexibility. We applied both methods to an Estonian study of gene expression quantitative trait loci (eQTL; n = 841), and two complex trait studies of height, hip, and waist circumferences, and body mass index from Multi-Ethnic Study of Atherosclerosis (MESA; n = 2,073) and UK Biobank (UKB; n = 327,393). Consistent with previous eQTL findings on mean, we found some but no conclusive evidence for cis regulators being enriched for variance association. SNP rs2681646 is associated with variance of waist circumference (p = 9.5E-07) at X-chromosome-wide significance in UKB, with a suggestive female-specific effect in MESA (p = 0.048). Collectively, an enrichment analysis using permutated UKB (p < 0.1) and MESA (p < 0.01) datasets, suggests a possible polygenic structure for the variance of human height.

Identification of Novel Causal Blood Biomarkers Linking Metabolically Favorable Adiposity With Type 2 Diabetes Risk.

OBJECTIVE: Observations of a metabolically unhealthy normal weight phenotype suggest that a lack of favorable adiposity contributes to an increased risk of type 2 diabetes. We aimed to identify causal blood biomarkers linking favorable adiposity with type 2 diabetes risk for use in cardiometabolic risk assessments. RESEARCH DESIGN AND METHODS: A weighted polygenic risk score (PRS) underpinning metabolically favorable adiposity was validated in the UK Biobank (n = 341,872) and the Outcome Reduction With Initial Glargine Intervention (ORIGIN Trial) (n = 8,197) and tested for association with 238 blood biomarkers. Associated biomarkers were investigated for causation with type 2 diabetes risk using Mendelian randomization and for its performance in predictive models for incident major adverse cardiovascular events (MACE). RESULTS: Of the 238 biomarkers tested, only insulin-like growth factor-binding protein (IGFBP)-3 concentration was associated with the PRS, where a 1 unit increase in PRS predicted a 0.28-SD decrease in IGFBP-3 blood levels (P < 0.05/238). Higher IGFBP-3 levels causally increased type 2 diabetes risk (odds ratio 1.26 per 1 SD genetically determined IGFBP-3 level [95% CI 1.11-1.43]) and predicted a higher incidence of MACE (hazard ratio 1.13 per 1 SD IGFBP-3 concentration [95% CI 1.07-1.20]). Adding IGFBP-3 concentrations to the standard clinical assessment of metabolic health enhanced the prediction of incident MACE, with a net reclassification improvement of 11.5% in normal weight individuals (P = 0.004). CONCLUSIONS: We identified IGFBP-3 as a novel biomarker linking a lack of favorable adiposity with type 2 diabetes risk and a predictive marker for incident cardiovascular events. Using IGFBP-3 blood concentrations may improve the risk assessment of cardiometabolic diseases.

A robust method to estimate regional polygenic correlation under misspecified linkage disequilibrium structure.

Complex traits can share a substantial proportion of their polygenic heritability. However, genome-wide polygenic correlations between pairs of traits can mask heterogeneity in their shared polygenic effects across loci. We propose a novel method (weighted maximum likelihood-regional polygenic correlation [RPC]) to evaluate polygenic correlation between two complex traits in small genomic regions using summary association statistics. Our method tests for evidence that the polygenic effect at a given region affects two traits concurrently. We show through simulations that our method is well calibrated, powerful, and more robust to misspecification of linkage disequilibrium than other methods under a polygenic model. As small genomic regions are more likely to harbor specific genetic effects, our method is ideal to identify heterogeneity in shared polygenic correlation across regions. We illustrate the usefulness of our method by addressing two questions related to cardiometabolic traits. First, we explored how RPC can inform on the strong epidemiological association between high-density lipoprotein cholesterol and coronary artery disease (CAD), suggesting a key role for triglycerides metabolism. Second, we investigated the potential role of PPARγ activators in the prevention of CAD. Our results provide a compelling argument that shared heritability between complex traits is highly heterogeneous across loci.

Identification of key aromatic compounds in Congou black tea by partial least-square regression with variable importance of projection scores and gas chromatography-mass spectrometry/gas chromatography-olfactometry.

BACKGROUND: Gas chromatography-olfactometry (GC-O) is the most frequently used method to estimate the sensory contribution of single odorant, but disregards the interactions between volatiles. In order to select the key volatiles responsible for the aroma attributes of Congou black tea (Camellia sinensis), instrumental, sensory and multivariate statistical approaches were applied. RESULTS: Using sensory analysis, nine panellists developed eight descriptors: floral, sweet, fruity, green, roasted, oil, spicy, and off-odour. Linalool, (E)-furan linalool oxide, (Z)-pyran linalool oxide, methyl salicylate, ß-myrcene, and phenylethyl alcohol, which were identified from the most representative samples by the GC-O procedure, were the essential aroma-active compounds in the formation of basic Congou black tea aroma. In addition, 136 volatiles were identified by gas chromatography-mass spectrometry (GC-MS), among which 55 compounds were determined as the key factors for six sensory attributes by partial least-square regression (PLSR) with variable importance of projection scores. CONCLUSION: Our results demonstrated that headspace solid-phase microextraction/GC-MS/GC-O was a fast approach for isolation and quantification aroma-active compounds. The PLSR method was also considered to be a useful tool in selecting important variables for sensory attributes. These two strategies, which allowed us to comprehensively evaluate the sensorial contribution of a single volatile from different perspectives, can be applied to related products for comprehensive quality control. © 2018 Society of Chemical Industry.

A machine-learning heuristic to improve gene score prediction of polygenic traits.

Machine-learning techniques have helped solve a broad range of prediction problems, yet are not widely used to build polygenic risk scores for the prediction of complex traits. We propose a novel heuristic based on machine-learning techniques (GraBLD) to boost the predictive performance of polygenic risk scores. Gradient boosted regression trees were first used to optimize the weights of SNPs included in the score, followed by a novel regional adjustment for linkage disequilibrium. A calibration set with sample size of ~200 individuals was sufficient for optimal performance. GraBLD yielded prediction R 2 of 0.239 and 0.082 using GIANT summary association statistics for height and BMI in the UK Biobank study (N = 130 K; 1.98 M SNPs), explaining 46.9% and 32.7% of the overall polygenic variance, respectively. For diabetes status, the area under the receiver operating characteristic curve was 0.602 in the UK Biobank study using summary-level association statistics from the DIAGRAM consortium. GraBLD outperformed other polygenic score heuristics for the prediction of height (p < 2.2 × 10-16) and BMI (p < 1.57 × 10-4), and was equivalent to LDpred for diabetes. Results were independently validated in the Health and Retirement Study (N = 8,292; 688,398 SNPs). Our report demonstrates the use of machine-learning techniques, coupled with summary-level data from large genome-wide meta-analyses to improve the prediction of polygenic traits.

A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics.

Despite considerable efforts, known genetic associations only explain a small fraction of predicted heritability. Regional associations combine information from multiple contiguous genetic variants and can improve variance explained at established association loci. However, regional associations are not easily amenable to estimation using summary association statistics because of sensitivity to linkage disequilibrium (LD). We now propose a novel method, LD Adjusted Regional Genetic Variance (LARGV), to estimate phenotypic variance explained by regional associations using summary statistics while accounting for LD. Our method is asymptotically equivalent to a multiple linear regression model when no interaction or haplotype effects are present. It has several applications, such as ranking of genetic regions according to variance explained or comparison of variance explained by two or more regions. Using height and BMI data from the Health Retirement Study (N = 7,776), we show that most genetic variance lies in a small proportion of the genome and that previously identified linkage peaks have higher than expected regional variance.

A comparison of sperm RNA-seq methods.

A significant challenge to the effective application of RNA-seq to the complete transcript analysis of low quantity and/or degraded samples is the amplification of minimal input RNA to enable sequencing library construction. Several strategies have been commercialized in order to facilitate this goal. However, each strategy has its own specific protocols and methodology, and each may introduce unique bias and in some cases show specific preference for a collection of sequences. Our wider investigation of human spermatozoal RNAs was able to reveal their complexity despite being generally characterized by low quantity and high fragmentation. In this study, the following four commercially available RNA-seq amplification and library protocols for the preparation of low quantity/highly fragmented samples, SMARTer™ Ultra Low RNA (SU) for Illumina® Sequencing, SeqPlex RNA Amplification (SP), Ovation® RNA-Seq System V2 (OR), and Ovation® RNA-Seq Formalin Fixed Paraffin Embedded System (FFPES) were assessed using human sperm RNAs. Further investigation analyzed the effects on the end results of two different library preparation methods, Encore NGS Multiplex System I (Enc) and Ovation Ultralow Library Systems (UL), that appeared best suited to this type of RNA, along with other potential confounding factors such as FFPE preservation. Our results indicate that for each library preparation protocol, the differences in the initial amount of input RNA and choice of RNA purification step do not generate marked differences in terms of RNA profiling. However, substantial disparity is introduced by individual amplification methods prior to library construction. These significant differences may be caused by the different priming methods or amplification strategies used in each of the four different protocols examined. The observation of intra-sample variation introduced by the choice of protocol highlights the role that external factors play in planning and subsequent reliable interpretation of results of any RNA-seq experiment.

Evaluation of the effectiveness of semen storage and sperm purification methods for spermatozoa transcript profiling.

Different semen storage and sperm purification methods may affect the integrity of isolated spermatozoal RNA. RNA-Seq was applied to determine whether semen storage methods (pelleted vs. liquefied) and somatic cell lysis buffer (SCLB) vs. PureSperm (PS) purification methods affect the quantity and quality of sperm RNA. The results indicate that the method of semen storage does not markedly impact RNA profiling whereas the choice of purification can yield significant differences. RNA-Seq showed that the majority of mitochondrial and mid-piece associated transcripts were lost after SCLB purification, which indicated that the mid-piece of spermatozoa may have been compromised. In addition, the number of stable transcript pairs from SCLB-samples was less than that from the PS samples. This study supports the view that PS purification better maintains the integrity of spermatozoal RNAs.

Next-generation sequencing: a powerful tool for the discovery of molecular markers in breast ductal carcinoma in situ.

Mammographic screening leads to frequent biopsies and concomitant overdiagnosis of breast cancer, particularly ductal carcinoma in situ (DCIS). Some DCIS lesions rapidly progress to invasive carcinoma, whereas others remain indolent. Because we cannot yet predict which lesions will not progress, all DCIS is regarded as malignant, and many women are overtreated. Thus, there is a pressing need for a panel of molecular markers in addition to the current clinical and pathological factors to provide prognostic information. Genomic technologies such as microarrays have made major contributions to defining subtypes of breast cancer. Next-generation sequencing (NGS) modalities offer unprecedented depth of expression analysis through revealing transcriptional boundaries, mutations, rare transcripts and alternative splice variants. NGS approaches are just beginning to be applied to DCIS. Here, the authors review the applications and challenges of NGS in discovering novel potential therapeutic targets and candidate biomarkers in the premalignant progression of breast cancer.

Stability, delivery and functions of human sperm RNAs at fertilization.

Increasing attention has focused on the significance of RNA in sperm, in light of its contribution to the birth and long-term health of a child, role in sperm function and diagnostic potential. As the composition of sperm RNA is in flux, assigning specific roles to individual RNAs presents a significant challenge. For the first time RNA-seq was used to characterize the population of coding and non-coding transcripts in human sperm. Examining RNA representation as a function of multiple methods of library preparation revealed unique features indicative of very specific and stage-dependent maturation and regulation of sperm RNA, illuminating their various transitional roles. Correlation of sperm transcript abundance with epigenetic marks suggested roles for these elements in the pre- and post-fertilization genome. Several classes of non-coding RNAs including lncRNAs, CARs, pri-miRNAs, novel elements and mRNAs have been identified which, based on factors including relative abundance, integrity in sperm, available knockout data of embryonic effect and presence or absence in the unfertilized human oocyte, are likely to be essential male factors critical to early post-fertilization development. The diverse and unique attributes of sperm transcripts that were revealed provides the first detailed analysis of the biology and anticipated clinical significance of spermatozoal RNAs.

RNA-Seq of human breast ductal carcinoma in situ models reveals aldehyde dehydrogenase isoform 5A1 as a novel potential target.

Breast ductal carcinoma in situ (DCIS) is being found in great numbers of women due to the widespread use of mammography. To increase knowledge of DCIS, we determined the expression changes that are common among three DCIS models (MCF10.DCIS, SUM102 and SUM225) compared to the MCF10A model of non-tumorigenic mammary epithelial cells in three dimensional (3D) overlay culture with reconstituted basement membrane (rBM). Extracted mRNA was subjected to 76 cycles of deep sequencing (RNA-Seq) using Illumina Genome Analyzer GAIIx. Analysis of RNA-Seq results showed 295 consistently differentially expressed transcripts in the DCIS models. These differentially expressed genes encode proteins that are associated with a number of signaling pathways such as integrin, fibroblast growth factor and TGFß signaling, show association with cell-cell signaling, cell-cell adhesion and cell proliferation, and have a notable bias toward localization in the extracellular and plasma membrane compartments. RNA-Seq data was validated by quantitative real-time PCR of selected differentially expressed genes. Aldehyde dehydrogenase 5A1 (ALDH5A1) which is an enzyme that is involved in mitochondrial glutamate metabolism, was over-expressed in all three DCIS models at both the mRNA and protein levels. Disulfiram and valproic acid are known to inhibit ALDH5A1 and are safe for chronic use in humans for other disorders. Both of these drugs significantly inhibited net proliferation of the DCIS 3D rBM overlay models, but had minimal effect on MCF10A 3D rBM overlay models. These results suggest that ALDH5A1 may play an important role in DCIS and potentially serve as a novel molecular therapeutic target.

Identification of artifactual microarray probe signals constantly present in multiple sample types.

The detection, identification, and quantitation of transcripts have evolved from simple Northern analysis, cDNA cloning, and sequencing to RT-PCR, microarrays, and now digital gene expression using ultra-high-throughput RNA sequencing (RNA-Seq). During the course of our studies we observed that some microarray probes show very high signal intensity values yet are discordant when compared with RNA-Seq. A total of 99 probes from approximately 30,000 were identified as consistently discordant in four human tissues or cell lines. Interestingly, this set of discordant probes appears array-dependent. Among the 99 probes identified, 70 constantly exhibited a high signal in all 713 available samples surveyed using the Illumina HumanHT-12v4 platform. Some were discordant with additional probes that annotated the same genes. Absence of a number of these transcripts was confirmed by quantitative RT-PCR (qRT-PCR). Our findings suggest that one must be cautious, as some array probes do not capture the level of the target.