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This study investigates the frequency of a clinically reported variant in PMS2, NM_000535.7:c.2523G>A p.(W841∗), from next-generation sequencing studies in 2 racially diverse cohorts. We identified clinical reports of the PMS2 c.2523G>A p.(W841∗) variant in the National Precision Oncology Program’s somatic testing database (n = 25,168). We determined frequency of the variant in germline exome sequencing from the Penn Medicine BioBank (n = 44,256) and in gnomAD. The PMS2 c.2523G>A p.(W841∗) was identified as a homozygous variant on tumor testing in an adult patient of self-identified Black race/ethnicity with no evidence of constitutional mismatch repair deficiency. The variant was clinically reported on 35 total tumor and liquid biopsy tests (0.1%), and all individuals with the variant were of self-identified Black race/ethnicity (0.6% of n = 5787). In individuals of African genetic ancestry (AFR), the variant's germline frequency was reported to be 0.2% and 1.3% in the Penn Medicine BioBank (PMBB) and gnomAD, respectively. The variant cannot be found in any individuals of European genetic ancestry (EUR) from either of the databases. The variant is found in a region of PMS2 with 100% homology to the PMS2CL pseudogene. PMS2 c.2523G>A p.(W841∗), when identified, is typically an African-ancestry-specific PMS2CL pseudogene variant, which should be recognized to prevent misdiagnosis of Lynch syndrome in Blacks.

Summary: Background: Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases. Methods: We performed an epigenome-wide association study using whole blood from Framingham Heart Study (FHS; n = 3,471, 46% females) participants and validated results using the Childhood Asthma Management Program (CAMP; n = 674, 39% females) and the Genetic Epidemiology of Asthma in Costa Rica Study (CRA; n = 787, 41% females). Using the closest gene to each IgE-associated CpG, we highlighted biologically plausible pathways underlying IgE regulation and analyzed the transcription patterns linked to IgE-associated CpGs (expression quantitative trait methylation loci; eQTMs). Using prior UK Biobank summary data from genome-wide association studies of asthma and allergy, we performed Mendelian randomization (MR) for causal inference testing using the IgE-associated CpGs from FHS with methylation quantitative trait loci (mQTLs) as instrumental variables. Findings: We identified 490 statistically significant differentially methylated CpGs associated with IgE in FHS, of which 193 (39.3%) replicated in CAMP and CRA (FDR < 0.05). Gene ontology analysis revealed enrichment in pathways related to transcription factor binding, asthma, and other immunological processes. eQTM analysis identified 124 cis-eQTMs for 106 expressed genes (FDR < 0.05). MR in combination with drug-target analysis revealed CTSB and USP20 as putatively causal regulators of IgE levels (Bonferroni adjusted P

Understanding regulation of genes associated to disease can reveal insights into disease mechanisms. Here, the authors perform an airway epithelial transcriptome-wide association analysis to elucidate genetic determinants of airway dysfunction in asthma, identifying genetic mechanisms of mucus pathobiology.

Penetrance of variants in monogenic disease and clinical utility of common polygenic variation has not been well explored on a large-scale. Here, the authors use exome sequencing data from 77,184 individuals to generate penetrance estimates and assess the utility of polygenic variation in risk prediction of monogenic variants.

Drugs targeting cardiovascular disease (CVD) can have negative consequences for liver function. Here, the authors combine genome wide analyses on 69,479 individuals to identify loss-of-function variants with beneficial effects on CVD-related traits without negative impacts on liver function.

Enhancer RNAs are transcribed bidirectionally from core transcription initiation regions. Here, by employing nascent RNA sequencing, the authors identify quantitative trait loci (QTLs) associated with enhancer RNA level and directionality, revealing the bipartite architecture of enhancers.

ACE2 and TMPRSS2 have received recent attention as entry factors for SARS-CoV-2. Here the authors analyze nasal airway transcriptome data from 695 children determining ACE2 and TMPRSS2 expression is induced by viral and type2 inflammation, respectively, and both exhibit eQTLs that vary across world populations.

Bone mineral density (BMD) is associated with fracture risk and many genetic loci with small effect sizes have been discovered by genome-wide association studies (GWAS). Here, the authors discover a large-effect rare loss-of-function genetic variant for BMD in the MEPE gene in the Norwegian HUNT study which replicates in the UK Biobank.

Transcriptome wide association studies (TWAS) can be used as a powerful method to identify and interpret the underlying biological mechanisms behind GWAS by mapping gene expression levels with phenotypes. In TWAS, gene expression is often imputed from individual-level genotypes of regulatory variants identified from external resources, such as Genotype-Tissue Expression (GTEx) Project. In this setting, a straightforward approach to impute expression levels of a specific tissue is to use the model trained from the same tissue type. When multiple tissues are available for the same subjects, it has been demonstrated that training imputation models from multiple tissue types improves the accuracy because of shared eQTLs between the tissues and increase in effective sample size. However, existing joint-tissue methods require access of genotype and expression data across all tissues. Moreover, they cannot leverage the abundance of various expression datasets across various tissues for non-overlapping individuals. Here, we explore the optimal way to combine imputed levels across training models from multiple tissues and datasets in a flexible manner using summary-level data. Our proposed method (SWAM) combines arbitrary number of transcriptome imputation models to linearly optimize the imputation accuracy given a target tissue. By integrating models across tissues and/or individuals, SWAM can improve the accuracy of transcriptome imputation or to improve power to TWAS while only requiring individual-level data from a single reference cohort. To evaluate the accuracy of SWAM, we combined 49 tissue-specific gene expression imputation models from the GTEx Project as well as from a large eQTL study of Depression Susceptibility Genes and Networks (DGN) Project and tested imputation accuracy in GEUVADIS lymphoblastoid cell lines samples. We also extend our meta-imputation method to meta-TWAS to leverage multiple tissues in TWAS analysis with summary-level statistics. Our results capitalize on the importance of integrating multiple tissues to unravel regulatory impacts of genetic variants on complex traits.

Transcriptome analysis of circulating tumor cells (CTCs) provides insights into monitoring target therapeutics and underlying tumor metastasis. Here the authors present Hydro-Seq, a contamination-free high-throughput hydrodynamic scRNA-seq barcoding technique for rare CTCs.

Estimated glomerular filtration rate (eGFR) is a measure of kidney function and used to characterize chronic kidney disease. Here, Graham et al. identify 53 novel loci for eGFR in a GWAS meta-analysis, a subset of which are associated with other common diseases, such as diabetes and hypertension, based on PheWAS.

Handgrip strength is a widely used measure of muscle strength and a predictor of a range of morbidities including cardiovascular diseases and all-cause mortality. Previous genome-wide association studies of handgrip strength have focused on common variants primarily in persons of European descent. We aimed to identify rare and ancestry-specific genetic variants associated with handgrip strength by conducting whole-genome sequence association analyses using 13,552 participants from six studies representing diverse population groups from the Trans-Omics in Precision Medicine (TOPMed) Program. By leveraging multiple handgrip strength measures performed in study participants over time, we increased our effective sample size by 7-12%. Single-variant analyses identified ten handgrip strength loci among African-Americans: four rare variants, five low-frequency variants, and one common variant. One significant and four suggestive genes were identified associated with handgrip strength when aggregating rare and functional variants; all associations were ancestry-specific. We additionally leveraged the different ancestries available in the UK Biobank to further explore the ancestry-specific association signals from the single-variant association analyses. In conclusion, our study identified 11 new loci associated with handgrip strength with rare and/or ancestry-specific genetic variations, highlighting the added value of whole-genome sequencing in diverse samples. Several of the associations identified using single-variant or aggregate analyses lie in genes with a function relevant to the brain or muscle or were reported to be associated with muscle or age-related traits. Further studies in samples with sequence data and diverse ancestries are needed to confirm these findings.

Gene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and contentious. Here, we present a statistical framework and computational tool to integrate heterogeneous annotations with GWAS summary statistics for gene-based analysis, applied with comprehensive coding and tissue-specific regulatory annotations. We compare power and accuracy identifying causal genes across single-annotation, omnibus, and annotation-agnostic gene-based tests in simulation studies and an analysis of 128 traits from the UK Biobank, and find that incorporating heterogeneous annotations in gene-based association analysis increases power and performance identifying causal genes.

With the increasing interest in hydrogen energy, the stability of hydrogen storage facilities and components is emphasized. In this study, we analyzed the effect of high-pressure hydrogen gas treatment in silica-filled EPDM composites with different silica contents. In detail, cure characteristics, crosslink density, mechanical properties, and hydrogen permeation properties were investigated. Results showed that material volume, remaining hydrogen content, and mechanical properties were changed after 96.3 MPa hydrogen gas exposure. With an increase in the silica content, the crosslink density and mechanical properties increased, but hydrogen permeability was decreased. After treatment, high-silica-content composites showed lower volume change than low-silica-content composites. The crack damage due to the decompression caused a decrease in mechanical properties, but high silica content can inhibit the reduction in mechanical properties. In particular, EPDM/silica composites with a silica content of above 60 phr exhibited excellent resistance to hydrogen gas, as no change in their physical and mechanical properties was observed.

Summary: DNA damage often induces heterogeneous cell-fate responses, such as cell-cycle arrest and apoptosis. Through single-cell RNA sequencing (scRNA-seq), we characterize the transcriptome response of cultured colon cancer cell lines to 5-fluorouracil (5FU)-induced DNA damage. After 5FU treatment, a single population of colon cancer cells adopts three distinct transcriptome phenotypes, which correspond to diversified cell-fate responses: apoptosis, cell-cycle checkpoint, and stress resistance. Although some genes are regulated uniformly across all groups of cells, many genes showed group-specific expression patterns mediating DNA damage responses specific to the corresponding cell fate. Some of these observations are reproduced at the protein level by flow cytometry and are replicated in cells treated with other 5FU-unrelated genotoxic drugs, camptothecin and etoposide. This work provides a resource for understanding heterogeneous DNA damage responses involving fractional killing and chemoresistance, which are among the major challenges in current cancer chemotherapy.

BackgroundMitochondria support critical cellular functions, such as energy production through oxidative phosphorylation, regulation of reactive oxygen species, apoptosis, and calcium homeostasis.ObjectiveGiven the heightened level of cellular activity in patients with asthma, we sought to determine whether mitochondrial DNA (mtDNA) copy number measured in peripheral blood differed between individuals with and without asthma.MethodsWhole genome sequence data was generated as part of the Trans-Omics for Precision Medicine (TOPMed) Program on participants from the Study of Asthma Phenotypes and Pharmacogenomic Interactions by Race-ethnicity (SAPPHIRE) and the Study of African Americans, Asthma, Genes, & Environment II (SAGE II). We restricted our analysis to individuals who self-identified as African American (3,651 asthma cases and 1,344 controls). Mitochondrial copy number was estimated using the sequencing read depth ratio for the mitochondrial and nuclear genomes. Respiratory complex expression was assessed using RNA-sequencing.ResultsAverage mitochondrial copy number was significantly higher among individuals with asthma when compared with controls (SAPPHIRE: 218.60 vs. 200.47, PConclusionsWe observed a robust association between asthma and higher mitochondrial copy number. Asthma having an effect on mitochondria function was also supported by lower respiratory complex gene expression in this group.

The emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. These strategies have benefited from improvements in imputation methods and association tests, however little attention has been paid to ways in which array design can increase rare variant association power. Therefore, we developed a novel framework to select tag SNPs using the reference panel of 26 populations from Phase 3 of the 1000 Genomes Project. We evaluate tag SNP performance via mean imputed r2 at untyped sites using leave-one-out internal validation and standard imputation methods, rather than pairwise linkage disequilibrium. Moving beyond pairwise metrics allows us to account for haplotype diversity across the genome for improve imputation accuracy and demonstrates population-specific biases from pairwise estimates. We also examine array design strategies that contrast multi-ethnic cohorts vs. single populations, and show a boost in performance for the former can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Using our framework, we demonstrate increased imputation accuracy for rare variants (frequency < 1%) by 0.5–3.1% for an array of one million sites and 0.7–7.1% for an array of 500,000 sites, depending on the population. Finally, we show how recent explosive growth in non-African populations means tag SNPs capture on average 30% fewer other variants than in African populations. The unified framework presented here will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.

Sharing of whole genome sequencing (WGS) data improves study scale and power, but data from different groups are often incompatible. Here, US genome centers and NIH programs define WGS data processing standards and a flexible validation method, facilitating collaboration in human genetics research.

Germline mutation rate is a critical parameter in the study of genetics and evolution. Here, Carlson et al. infer fine-scale patterns of human mutation rate heterogeneity by analyzing ~36 million singleton variants from 3560 whole-genome sequences.

Bicuspid aortic valve (BAV) is the most common human congenital cardiovascular malformation. Here, the authors perform a genome-wide association study for BAV and identify risk variants in the gene region of cardiac-specific transcription factor GATA4 and implicate GATA4 in heart valve development.

DNA sequencing identifies common and rare genetic variants for association studies, but studies typically focus on variants in nuclear DNA and ignore the mitochondrial genome. In fact, analyzing variants in mitochondrial DNA (mtDNA) sequences presents special problems, which we resolve here with a general solution for the analysis of mtDNA in next-generation sequencing studies. The new program package comprises 1) an algorithm designed to identify mtDNA variants (i.e., homoplasmies and heteroplasmies), incorporating sequencing error rates at each base in a likelihood calculation and allowing allele fractions at a variant site to differ across individuals; and 2) an estimation of mtDNA copy number in a cell directly from whole-genome sequencing data. We also apply the methods to DNA sequence from lymphocytes of ~2,000 SardiNIA Project participants. As expected, mothers and offspring share all homoplasmies but a lesser proportion of heteroplasmies. Both homoplasmies and heteroplasmies show 5-fold higher transition/transversion ratios than variants in nuclear DNA. Also, heteroplasmy increases with age, though on average only ~1 heteroplasmy reaches the 4% level between ages 20 and 90. In addition, we find that mtDNA copy number averages ~110 copies/lymphocyte and is ~54% heritable, implying substantial genetic regulation of the level of mtDNA. Copy numbers also decrease modestly but significantly with age, and females on average have significantly more copies than males. The mtDNA copy numbers are significantly associated with waist circumference (p-value = 0.0031) and waist-hip ratio (p-value = 2.4×10-5), but not with body mass index, indicating an association with central fat distribution. To our knowledge, this is the largest population analysis to date of mtDNA dynamics, revealing the age-imposed increase in heteroplasmy, the relatively high heritability of copy number, and the association of copy number with metabolic traits.

Variants in the growth factor receptor-bound protein 10 (GRB10) gene were in a GWAS meta-analysis associated with reduced glucose-stimulated insulin secretion and increased risk of type 2 diabetes (T2D) if inherited from the father, but inexplicably reduced fasting glucose when inherited from the mother. GRB10 is a negative regulator of insulin signaling and imprinted in a parent-of-origin fashion in different tissues. GRB10 knock-down in human pancreatic islets showed reduced insulin and glucagon secretion, which together with changes in insulin sensitivity may explain the paradoxical reduction of glucose despite a decrease in insulin secretion. Together, these findings suggest that tissue-specific methylation and possibly imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis. The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father.

Nephrotic syndrome (NS) gene discovery efforts are now occurring in small kindreds and cohorts of sporadic cases. Power to identify causal variants in these groups beyond a statistical significance threshold is challenging due to small sample size and/or lack of family information. There is a need to develop novel methods to identify NS-associated variants. One way to determine putative functional relevance of a gene is to measure its strength of negative selection, as variants in genes under strong negative selection are more likely to be deleterious. We created a gene-level, integrated metric of negative selection (GIMS) score for 20,079 genes by combining multiple comparative genomics and population genetics measures. To understand the utility of GIMS for NS gene discovery, we examined this score in a diverse set of NS-relevant gene sets. These included genes known to cause monogenic forms of NS in humans as well as genes expressed in the cells of the glomerulus and, particularly, the podocyte. We found strong negative selection in the following NS-relevant gene sets: (1) autosomal-dominant Mendelian focal segmental glomerulosclerosis (FSGS) genes (p = 0.03 compared to reference), (2) glomerular expressed genes (p = 4×10(-23)), and (3) predicted podocyte genes (p = 3×10(-9)). Eight genes causing autosomal dominant forms of FSGS had a stronger combined score of negative selection and podocyte enrichment as compared to all other genes (p = 1 x 10(-3)). As a whole, recessive FSGS genes were not enriched for negative selection. Thus, we also created a transcript-level, integrated metric of negative selection (TIMS) to quantify negative selection on an isoform level. These revealed transcripts of known autosomal recessive disease-causing genes that were nonetheless under strong selection. We suggest that a filtering strategy that includes measuring negative selection on a gene or isoform level could aid in identifying NS-related genes. Our GIMS and TIMS scores are available at http://glom.sph.umich.edu/GIMS/.

Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the "Metabochip," a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.

β-blockers and β-agonists are primarily used to treat cardiovascular diseases. Inter-individual variability in response to both drug classes is well recognized, yet the identity and relative contribution of the genetic players involved are poorly understood. This work is the first genome-wide association study (GWAS) addressing the values and susceptibility of cardiovascular-related traits to a selective β(1)-blocker, Atenolol (ate), and a β-agonist, Isoproterenol (iso). The phenotypic dataset consisted of 27 highly heritable traits, each measured across 22 inbred mouse strains and four pharmacological conditions. The genotypic panel comprised 79922 informative SNPs of the mouse HapMap resource. Associations were mapped by Efficient Mixed Model Association (EMMA), a method that corrects for the population structure and genetic relatedness of the various strains. A total of 205 separate genome-wide scans were analyzed. The most significant hits include three candidate loci related to cardiac and body weight, three loci for electrocardiographic (ECG) values, two loci for the susceptibility of atrial weight index to iso, four loci for the susceptibility of systolic blood pressure (SBP) to perturbations of the β-adrenergic system, and one locus for the responsiveness of QTc (p

The Metabochip is a custom genotyping array designed for replication and fine mapping of metabolic, cardiovascular, and anthropometric trait loci and includes low frequency variation content identified from the 1000 Genomes Project. It has 196,725 SNPs concentrated in 257 genomic regions. We evaluated the Metabochip in 5,863 African Americans; 89% of all SNPs passed rigorous quality control with a call rate of 99.9%. Two examples illustrate the value of fine mapping with the Metabochip in African-ancestry populations. At CELSR2/PSRC1/SORT1, we found the strongest associated SNP for LDL-C to be rs12740374 (p = 3.5 × 10(-11)), a SNP indistinguishable from multiple SNPs in European ancestry samples due to high correlation. Its distinct signal supports functional studies elsewhere suggesting a causal role in LDL-C. At CETP we found rs17231520, with risk allele frequency 0.07 in African Americans, to be associated with HDL-C (p = 7.2 × 10(-36)). This variant is very rare in Europeans and not tagged in common GWAS arrays, but was identified as associated with HDL-C in African Americans in a single-gene study. Our results, one narrowing the risk interval and the other revealing an associated variant not found in Europeans, demonstrate the advantages of high-density genotyping of common and rare variation for fine mapping of trait loci in African American samples.

The Plasmodium falciparum parasite's ability to adapt to environmental pressures, such as the human immune system and antimalarial drugs, makes malaria an enduring burden to public health. Understanding the genetic basis of these adaptations is critical to intervening successfully against malaria. To that end, we created a high-density genotyping array that assays over 17,000 single nucleotide polymorphisms (∼ 1 SNP/kb), and applied it to 57 culture-adapted parasites from three continents. We characterized genome-wide genetic diversity within and between populations and identified numerous loci with signals of natural selection, suggesting their role in recent adaptation. In addition, we performed a genome-wide association study (GWAS), searching for loci correlated with resistance to thirteen antimalarials; we detected both known and novel resistance loci, including a new halofantrine resistance locus, PF10_0355. Through functional testing we demonstrated that PF10_0355 overexpression decreases sensitivity to halofantrine, mefloquine, and lumefantrine, but not to structurally unrelated antimalarials, and that increased gene copy number mediates resistance. Our GWAS and follow-on functional validation demonstrate the potential of genome-wide studies to elucidate functionally important loci in the malaria parasite genome.

Significant advances have been made in the discovery of genes affecting bone mineral density (BMD); however, our understanding of its genetic basis remains incomplete. In the current study, genome-wide association (GWA) and co-expression network analysis were used in the recently described Hybrid Mouse Diversity Panel (HMDP) to identify and functionally characterize novel BMD genes. In the HMDP, a GWA of total body, spinal, and femoral BMD revealed four significant associations (-log10P>5.39) affecting at least one BMD trait on chromosomes (Chrs.) 7, 11, 12, and 17. The associations implicated a total of 163 genes with each association harboring between 14 and 112 genes. This list was reduced to 26 functional candidates by identifying those genes that were regulated by local eQTL in bone or harbored potentially functional non-synonymous (NS) SNPs. This analysis revealed that the most significant BMD SNP on Chr. 12 was a NS SNP in the additional sex combs like-2 (Asxl2) gene that was predicted to be functional. The involvement of Asxl2 in the regulation of bone mass was confirmed by the observation that Asxl2 knockout mice had reduced BMD. To begin to unravel the mechanism through which Asxl2 influenced BMD, a gene co-expression network was created using cortical bone gene expression microarray data from the HMDP strains. Asxl2 was identified as a member of a co-expression module enriched for genes involved in the differentiation of myeloid cells. In bone, osteoclasts are bone-resorbing cells of myeloid origin, suggesting that Asxl2 may play a role in osteoclast differentiation. In agreement, the knockdown of Asxl2 in bone marrow macrophages impaired their ability to form osteoclasts. This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits.

With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between markers from different blocks. Second, the true null distribution of the test statistic often fails to follow the asymptotic distribution at the tails of the distribution. We propose an accurate and efficient method for multiple testing correction in genome-wide association studies--SLIDE. Our method accounts for all correlation within a sliding window and corrects for the departure of the true null distribution of the statistic from the asymptotic distribution. In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods. We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP. SLIP and SLIDE are available at http://slide.cs.ucla.edu.