Risk factors affecting polygenic score performance across diverse cohorts.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N=491,111) and African (N=21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best and worst performing quintiles for certain covariates. 28 covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account non-linear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge GWAS effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Mendelian randomization analyses clarify the effects of height on cardiovascular diseases.

An inverse correlation between stature and risk of coronary artery disease (CAD) has been observed in several epidemiologic studies, and recent Mendelian randomization (MR) experiments have suggested causal association. However, the extent to which the effect estimated by MR can be explained by established cardiovascular risk factors is unclear, with a recent report suggesting that lung function traits could fully explain the height-CAD effect. To clarify this relationship, we utilized a well-powered set of genetic instruments for human stature, comprising >1,800 genetic variants for height and CAD. In univariable analysis, we confirmed that a one standard deviation decrease in height (~6.5 cm) was associated with a 12.0% increase in the risk of CAD, consistent with previous reports. In multivariable analysis accounting for effects from up to 12 established risk factors, we observed a >3-fold attenuation in the causal effect of height on CAD susceptibility (3.7%, p = 0.02). However, multivariable analyses demonstrated independent effects of height on other cardiovascular traits beyond CAD, consistent with epidemiologic associations and univariable MR experiments. In contrast with published reports, we observed minimal effects of lung function traits on CAD risk in our analyses, indicating that these traits are unlikely to explain the residual association between height and CAD risk. In sum, these results suggest the impact of height on CAD risk beyond previously established cardiovascular risk factors is minimal and not explained by lung function measures.

High-throughput method to analyze the cytotoxicity of CAR-T Cells in a 3D tumor spheroid model using image cytometry.

Solid tumors account for approximately 90% of all adult human cancers. As such, the development of novel cellular therapies has become of increasing importance to target solid tumor malignancies, such as prostate, lung, breast, bladder, colon, and liver cancers. One such cellular therapy relies on the use of chimeric antigen receptor T cells (CAR-T cells). CAR-T cells are engineered to target specific antigens on tumor cells. To date, there are six FDA-approved CAR-T cell therapies that have been utilized for hematologic B cell malignancies. Immune cell trafficking and immunosuppressive factors within the tumor microenvironment increase the relative difficulty in developing a robust CAR-T cell therapy against solid tumors. Therefore, it is critical to develop novel methodologies for high-throughput phenotypic and functional assays using 3D tumor spheroid models to assess CAR-T cell products against solid tumors. In this manuscript, we discuss the use of CAR-T cells targeted towards PSMA, an antigen that is found on prostate cancer tumor cells, the second most common cause of cancer deaths among men worldwide. We demonstrate the use of high-throughput, plate-based image cytometry to characterize CAR-T cell-mediated cytotoxic potency against 3D prostate tumor spheroids. We were able to kinetically evaluate the efficacy and therapeutic value of PSMA CAR-T cells by analyzing the cytotoxicity against prostate tumor spheroids. In addition, the CAR-T cells were fluorescently labeled to visually identify the location of the T cells as cytotoxicity occurs, which may provide more meaningful information for assessing the functionality of the CAR-T cells. The proposed image cytometry method can overcome limitations placed on traditional methodologies to effectively assess cell-mediated 3D tumor spheroid cytotoxicity and efficiently generate time- and dose-dependent results.

Gene burden analysis identifies genes associated with increased risk and severity of adult-onset hearing loss in a diverse hospital-based cohort.

Loss or absence of hearing is common at both extremes of human lifespan, in the forms of congenital deafness and age-related hearing loss. While these are often studied separately, there is increasing evidence that their genetic basis is at least partially overlapping. In particular, both common and rare variants in genes associated with monogenic forms of hearing loss also contribute to the more polygenic basis of age-related hearing loss. Here, we directly test this model in the Penn Medicine BioBank-a healthcare system cohort of around 40,000 individuals with linked genetic and electronic health record data. We show that increased burden of predicted deleterious variants in Mendelian hearing loss genes is associated with increased risk and severity of adult-onset hearing loss. As a specific example, we identify one gene-TCOF1, responsible for a syndromic form of congenital hearing loss-in which deleterious variants are also associated with adult-onset hearing loss. We also identify four additional novel candidate genes (COL5A1, HMMR, RAPGEF3, and NNT) in which rare variant burden may be associated with hearing loss. Our results confirm that rare variants in Mendelian hearing loss genes contribute to polygenic risk of hearing loss, and emphasize the utility of healthcare system cohorts to study common complex traits and diseases.

Leveraging Multi-Ancestry Polygenic Risk Scores for Body Mass Index to Predict Antiretroviral Therapy-Induced Weight Gain.

Widespread availability of antiretroviral therapies (ART) for HIV-1 have generated considerable interest in understanding the pharmacogenomics of ART. In some individuals, ART has been associated with excessive weight gain, which disproportionately affects women of African ancestry. The underlying biology of ART-associated weight gain is poorly understood, but some genetic markers which modify weight gain risk have been suggested, with more genetic factors likely remaining undiscovered. To overcome limitations in available sample sizes for genome-wide association studies (GWAS) in people with HIV, we explored whether a multi-ancestry polygenic risk score (PRS) derived from large, publicly available non-HIV GWAS for body mass index (BMI) can achieve high cross-ancestry performance for predicting baseline BMI in diverse, prospective ART clinical trials datasets, and whether that PRSBMI is also associated with change in BMI over 48 weeks on ART. We show that PRSBMI explained ∼5-7% of variability in baseline (pre-ART) BMI, with high performance in both European and African genetic ancestry groups, but that PRSBMI was not associated with change in BMI on ART. This study argues against a shared genetic predisposition for baseline (pre-ART) BMI and ART-associated weight gain.

Not all roads lead to the immune system: the genetic basis of multiple sclerosis severity.

Multiple sclerosis is a leading cause of neurological disability in adults. Heterogeneity in multiple sclerosis clinical presentation has posed a major challenge for identifying genetic variants associated with disease outcomes. To overcome this challenge, we used prospectively ascertained clinical outcomes data from the largest international multiple sclerosis registry, MSBase. We assembled a cohort of deeply phenotyped individuals of European ancestry with relapse-onset multiple sclerosis. We used unbiased genome-wide association study and machine learning approaches to assess the genetic contribution to longitudinally defined multiple sclerosis severity phenotypes in 1813 individuals. Our primary analyses did not identify any genetic variants of moderate to large effect sizes that met genome-wide significance thresholds. The strongest signal was associated with rs7289446 (ß = -0.4882, P = 2.73 × 10-7), intronic to SEZ6L on chromosome 22. However, we demonstrate that clinical outcomes in relapse-onset multiple sclerosis are associated with multiple genetic loci of small effect sizes. Using a machine learning approach incorporating over 62 000 variants together with clinical and demographic variables available at multiple sclerosis disease onset, we could predict severity with an area under the receiver operator curve of 0.84 (95% CI 0.79-0.88). Our machine learning algorithm achieved positive predictive value for outcome assignation of 80% and negative predictive value of 88%. This outperformed our machine learning algorithm that contained clinical and demographic variables alone (area under the receiver operator curve 0.54, 95% CI 0.48-0.60). Secondary, sex-stratified analyses identified two genetic loci that met genome-wide significance thresholds. One in females (rs10967273; ßfemale = 0.8289, P = 3.52 × 10-8), the other in males (rs698805; ßmale = -1.5395, P = 4.35 × 10-8), providing some evidence for sex dimorphism in multiple sclerosis severity. Tissue enrichment and pathway analyses identified an overrepresentation of genes expressed in CNS compartments generally, and specifically in the cerebellum (P = 0.023). These involved mitochondrial function, synaptic plasticity, oligodendroglial biology, cellular senescence, calcium and G-protein receptor signalling pathways. We further identified six variants with strong evidence for regulating clinical outcomes, the strongest signal again intronic to SEZ6L (adjusted hazard ratio 0.72, P = 4.85 × 10-4). Here we report a milestone in our progress towards understanding the clinical heterogeneity of multiple sclerosis outcomes, implicating functionally distinct mechanisms to multiple sclerosis risk. Importantly, we demonstrate that machine learning using common single nucleotide variant clusters, together with clinical variables readily available at diagnosis can improve prognostic capabilities at diagnosis, and with further validation has the potential to translate to meaningful clinical practice change.

Quantifying factors that affect polygenic risk score performance across diverse ancestries and age groups for body mass index.

Polygenic risk scores (PRS) have led to enthusiasm for precision medicine. However, it is well documented that PRS do not generalize across groups differing in ancestry or sample characteristics e.g., age. Quantifying performance of PRS across different groups of study participants, using genome-wide association study (GWAS) summary statistics from multiple ancestry groups and sample sizes, and using different linkage disequilibrium (LD) reference panels may clarify which factors are limiting PRS transferability. To evaluate these factors in the PRS generation process, we generated body mass index (BMI) PRS (PRSBMI) in the Electronic Medical Records and Genomics (eMERGE) network (N=75,661). Analyses were conducted in two ancestry groups (European and African) and three age ranges (adult, teenagers, and children). For PRSBMI calculations, we evaluated five LD reference panels and three sets of GWAS summary statistics of varying sample size and ancestry. PRSBMI performance increased for both African and European ancestry individuals using cross-ancestry GWAS summary statistics compared to European-only summary statistics (6.3% and 3.7% relative R2 increase, respectively, pAfrican=0.038, pEuropean=6.26x10-4). The effects of LD reference panels were more pronounced in African ancestry study datasets. PRSBMI performance degraded in children; R2 was less than half of teenagers or adults. The effect of GWAS summary statistics sample size was small when modeled with the other factors. Additionally, the potential of using a PRS generated for one trait to predict risk for comorbid diseases is not well understood especially in the context of cross-ancestry analyses - we explored clinical comorbidities from the electronic health record associated with PRSBMI and identified significant associations with type 2 diabetes and coronary atherosclerosis. In summary, this study quantifies the effects that ancestry, GWAS summary statistic sample size, and LD reference panel have on PRS performance, especially in cross-ancestry and age-specific analyses.

Quality Control Procedures for Genome-Wide Association Studies.

Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of many complex diseases. Regardless of the context, the practical utility of this information ultimately depends upon the quality of the data used for statistical analyses. Quality control (QC) procedures for GWAS are constantly evolving. Here, we enumerate some of the challenges in QC of genotyped GWAS data and describe the approaches involving genotype imputation of a sample dataset along with post-imputation quality assurance, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of the GWAS data (genotyped and imputed), including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We provide detailed guidelines along with a sample dataset to suggest current best practices and discuss areas of ongoing and future research. © 2022 Wiley Periodicals LLC.

Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine.

One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.

A Genome-First Approach to Rare Variants in Dominant Postlingual Hearing Loss Genes in a Large Adult Population.

OBJECTIVE: To investigate the importance of rare variants in adult-onset hearing loss. STUDY DESIGN: Genomic association study. SETTING: Large biobank from tertiary care center. METHODS: We investigated rare variants (minor allele frequency <5%) in 42 autosomal dominant (DFNA) postlingual hearing loss (HL) genes in 16,657 unselected individuals in the Penn Medicine Biobank. We determined the prevalence of known pathogenic and predicted deleterious variants in subjects with audiometric-proven sensorineural hearing loss. We scanned across known postlingual DFNA HL genes to determine those most significantly contributing to the phenotype. We replicated findings in an independent cohort (UK Biobank). RESULTS: While rare individually, when considering the accumulation of variants in all postlingual DFNA genes, more than 90% of participants carried at least 1 rare variant. Rare variants predicted to be deleterious were enriched in adults with audiometric-proven hearing loss (pure-tone average >25 dB; P = .015). Patients with a rare predicted deleterious variant had an odds ratio of 1.27 for HL compared with genotypic controls (P = .029). Gene burden in DIABLO, PTPRQ, TJP2, and POU4F3 were independently associated with sensorineural hearing loss. CONCLUSION: Although prior reports have focused on common variants, we find that rare predicted deleterious variants in DFNA postlingual HL genes are enriched in patients with adult-onset HL in a large health care system population. We show the value of investigating rare variants to uncover hearing loss phenotypes related to implicated genes.

Gender of Department Chair and Paid Parental Leave Benefits in Academic Radiology Residency Programs.

RATIONALE AND OBJECTIVES: Residency training often overlaps with prime childbearing years, yet variability in availability and duration of parental leave in residency can complicate the decision to become parents. Gender disparities in attitudes towards parenthood in residency is well recognized, with females generally reporting more concerns surrounding prolonged training, hindrance of future career plans, and negative perception from peers. However, gender of the department chair has not yet been examined as a factor influencing parental leave policies for residents in Radiology. MATERIALS AND METHODS: The gender of the department chair and parental leave policies for residents in 209 ACGME accredited diagnostic radiology programs across the United States were procured from their websites. These programs were stratified into 6 geographical regions to identify regional differences. Chi-squared analyses were used to compare availability of paid parental benefits with the gender of department chairs. RESULTS: Seventy-seven percent of diagnostic radiology program department chairs were male. 34 of 49 programs (69%) with female department chairs advertised paid parental benefits, compared to 61 of 160 programs (38%) chaired by males (P < 0.001). When stratified by region, this gender difference remained statistically significant in the mid-Atlantic and New England. CONCLUSION: Female gender of the department chair was associated with the increased availability of paid parental leave benefits for residents, yet females hold fewer academic leadership positions than males. Future discussions regarding parental leave policies for residents will have to consider the unique challenges in residency such as length of training and burden on coresidents.

A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts.

Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify new lipid-associated genes and study the relationships among lipids, genotypes, gene expression and hundreds of complex human diseases from the Electronic Medical Records and Genomics (347 traits) and the UK Biobank (549 traits). Aside from 67 new lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies.

Integrated Skin Transcriptomics and Serum Multiplex Assays Reveal Novel Mechanisms of Wound Healing in Diabetic Foot Ulcers.

Nonhealing diabetic foot ulcers (DFUs) are characterized by low-grade chronic inflammation, both locally and systemically. We prospectively followed a group of patients who either healed or developed nonhealing chronic DFUs. Serum and forearm skin analysis, both at the protein expression and the transcriptomic level, indicated that increased expression of factors such as interferon-γ (IFN-γ), vascular endothelial growth factor, and soluble vascular cell adhesion molecule-1 were associated with DFU healing. Furthermore, foot skin single-cell RNA sequencing analysis showed multiple fibroblast cell clusters and increased inflammation in the dorsal skin of patients with diabetes mellitus (DM) and DFU specimens compared with control subjects. In addition, in myeloid cell DM and DFU upstream regulator analysis, we observed inhibition of interleukin-13 and IFN-γ and dysregulation of biological processes that included cell movement of monocytes, migration of dendritic cells, and chemotaxis of antigen-presenting cells pointing to an impaired migratory profile of immune cells in DM skin. The SLCO2A1 and CYP1A1 genes, which were upregulated at the forearm of nonhealers, were mainly expressed by the vascular endothelial cell cluster almost exclusively in DFU, indicating a potential important role in wound healing. These results from integrated protein and transcriptome analyses identified individual genes and pathways that can potentially be targeted for enhancing DFU healing.

Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.

In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait's heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments.

Epigenome-wide effects of vitamin D on asthma bronchial epithelial cells.

Vitamin D is a nutrient and a hormone with multiple effects on immune regulation and respiratory viral infections, which can worsen asthma and lead to severe asthma exacerbations. We set up a complete experimental and analytical pipeline for ATAC-Seq and RNA-Seq to study genome-wide epigenetic changes in human bronchial epithelial cells of asthmatic subjects, following treatment of these cells with calcitriol (vitamin D3) and Poly (I:C)(a viral analogue). This approach led to the identification of biologically plausible candidate genes for viral infections and asthma, such as DUSP10 and SLC44A1.

Semaphorin 3C as a Therapeutic Target in Prostate and Other Cancers.

The semaphorins represent a large family of signaling molecules with crucial roles in neuronal and cardiac development. While normal semaphorin function pertains largely to development, their involvement in malignancy is becoming increasingly evident. One member, Semaphorin 3C (SEMA3C), has been shown to drive a number of oncogenic programs, correlate inversely with cancer prognosis, and promote the progression of multiple different cancer types. This report surveys the body of knowledge surrounding SEMA3C as a therapeutic target in cancer. In particular, we summarize SEMA3C's role as an autocrine andromedin in prostate cancer growth and survival and provide an overview of other cancer types that SEMA3C has been implicated in including pancreas, brain, breast, and stomach. We also propose molecular strategies that could potentially be deployed against SEMA3C as anticancer agents such as biologics, small molecules, monoclonal antibodies and antisense oligonucleotides. Finally, we discuss important considerations for the inhibition of SEMA3C as a cancer therapeutic agent.

SEMA3C drives cancer growth by transactivating multiple receptor tyrosine kinases via Plexin B1.

Growth factor receptor tyrosine kinase (RTK) pathway activation is a key mechanism for mediating cancer growth, survival, and treatment resistance. Cognate ligands play crucial roles in autocrine or paracrine stimulation of these RTK pathways. Here, we show SEMA3C drives activation of multiple RTKs including EGFR, ErbB2, and MET in a cognate ligand-independent manner via Plexin B1. SEMA3C expression levels increase in castration-resistant prostate cancer (CRPC), where it functions to promote cancer cell growth and resistance to androgen receptor pathway inhibition. SEMA3C inhibition delays CRPC and enzalutamide-resistant progression. Plexin B1 sema domain-containing:Fc fusion proteins suppress RTK signaling and cell growth and inhibit CRPC progression of LNCaP xenografts post-castration in vivo SEMA3C inhibition represents a novel therapeutic strategy for treatment of advanced prostate cancer.

Hemophilia B Gene Therapy with a High-Specific-Activity Factor IX Variant.

BACKGROUND: The prevention of bleeding with adequately sustained levels of clotting factor, after a single therapeutic intervention and without the need for further medical intervention, represents an important goal in the treatment of hemophilia. METHODS: We infused a single-stranded adeno-associated viral (AAV) vector consisting of a bioengineered capsid, liver-specific promoter and factor IX Padua (factor IX-R338L) transgene at a dose of 5×1011 vector genomes per kilogram of body weight in 10 men with hemophilia B who had factor IX coagulant activity of 2% or less of the normal value. Laboratory values, bleeding frequency, and consumption of factor IX concentrate were prospectively evaluated after vector infusion and were compared with baseline values. RESULTS: No serious adverse events occurred during or after vector infusion. Vector-derived factor IX coagulant activity was sustained in all the participants, with a mean (±SD) steady-state factor IX coagulant activity of 33.7±18.5% (range, 14 to 81). On cumulative follow-up of 492 weeks among all the participants (range of follow-up in individual participants, 28 to 78 weeks), the annualized bleeding rate was significantly reduced (mean rate, 11.1 events per year [range, 0 to 48] before vector administration vs. 0.4 events per year [range, 0 to 4] after administration; P=0.02), as was factor use (mean dose, 2908 IU per kilogram [range, 0 to 8090] before vector administration vs. 49.3 IU per kilogram [range, 0 to 376] after administration; P=0.004). A total of 8 of 10 participants did not use factor, and 9 of 10 did not have bleeds after vector administration. An asymptomatic increase in liver-enzyme levels developed in 2 participants and resolved with short-term prednisone treatment. One participant, who had substantial, advanced arthropathy at baseline, administered factor for bleeding but overall used 91% less factor than before vector infusion. CONCLUSIONS: We found sustained therapeutic expression of factor IX coagulant activity after gene transfer in 10 participants with hemophilia who received the same vector dose. Transgene-derived factor IX coagulant activity enabled the termination of baseline prophylaxis and the near elimination of bleeding and factor use. (Funded by Spark Therapeutics and Pfizer; ClinicalTrials.gov number, NCT02484092 .).

Semaphorin 3 C drives epithelial-to-mesenchymal transition, invasiveness, and stem-like characteristics in prostate cells.

Prostate cancer (PCa) is among the most commonly-occurring cancers worldwide and a leader in cancer-related deaths. Local non-invasive PCa is highly treatable but limited treatment options exist for those with locally-advanced and metastatic forms of the disease underscoring the need to identify mechanisms mediating PCa progression. The semaphorins are a large grouping of membrane-associated or secreted signalling proteins whose normal roles reside in embryogenesis and neuronal development. In this context, semaphorins help establish chemotactic gradients and direct cell movement. Various semaphorin family members have been found to be up- and down-regulated in a number of cancers. One family member, Semaphorin 3 C (SEMA3C), has been implicated in prostate, breast, ovarian, gastric, lung, and pancreatic cancer as well as glioblastoma. Given SEMA3C's roles in development and its augmented expression in PCa, we hypothesized that SEMA3C promotes epithelial-to-mesenchymal transition (EMT) and stem-like phenotypes in prostate cells. In the present study we show that ectopic expression of SEMA3C in RWPE-1 promotes the upregulation of EMT and stem markers, heightened sphere-formation, and cell plasticity. In addition, we show that SEMA3C promotes migration and invasion in vitro and cell dissemination in vivo.

LAIT: a local ancestry inference toolkit.

BACKGROUND: Inferring local ancestry in individuals of mixed ancestry has many applications, most notably in identifying disease-susceptible loci that vary among different ethnic groups. Many software packages are available for inferring local ancestry in admixed individuals. However, most of these existing software packages require specific formatted input files and generate output files in various types, yielding practical inconvenience. RESULTS: We developed a tool set, Local Ancestry Inference Toolkit (LAIT), which can convert standardized files into software-specific input file formats as well as standardize and summarize inference results for four popular local ancestry inference software: HAPMIX, LAMP, LAMP-LD, and ELAI. We tested LAIT using both simulated and real data sets and demonstrated that LAIT provides convenience to run multiple local ancestry inference software. In addition, we evaluated the performance of local ancestry software among different supported software packages, mainly focusing on inference accuracy and computational resources used. CONCLUSION: We provided a toolkit to facilitate the use of local ancestry inference software, especially for users with limited bioinformatics background.