The International Transfer of Semi-Conductor Technology Through U.S. Based Firms

This study of the U.S. semiconductor industry seeks to examine its international pattern of exports, licensing, and foreign investments. This industry was selected for study because previous work had shown the United States tended to have a favorable trade balance in industries characterized by high technology processes or products. The study is divided into three parts. The first part, consisting of Chapters 2 and 3, discusses the characteristics of the U.S. semiconductor industry and semiconductor technology. The next part, Chapters 4, 5 and 6 examines the different transfer channels and the factors which determine a firm's selection between exports, licensing, and foreign production to supply foreign markets. The final section, Chapter 7, seeks to determine the characteristics of the American firms most responsible for the transfer of technology offshore and the impact of foreign direct investment on trade patterns.

Lipid lowering and Alzheimer disease risk: A mendelian randomization study.

OBJECTIVE: To examine whether genetic variation affecting the expression or function of lipid-lowering drug targets is associated with Alzheimer disease (AD) risk, to evaluate the potential impact of long-term exposure to corresponding therapeutics. METHODS: We conducted Mendelian randomization analyses using variants in genes that encode the protein targets of several approved lipid-lowering drug classes: HMGCR (encoding the target for statins), PCSK9 (encoding the target for PCSK9 inhibitors, eg, evolocumab and alirocumab), NPC1L1 (encoding the target for ezetimibe), and APOB (encoding the target of mipomersen). Variants were weighted by associations with low-density lipoprotein cholesterol (LDL-C) using data from lipid genetics consortia (n up to 295,826). We meta-analyzed Mendelian randomization estimates for regional variants weighted by LDL-C on AD risk from 2 large samples (total n = 24,718 cases, 56,685 controls). RESULTS: Models for HMGCR, APOB, and NPC1L1 did not suggest that the use of related lipid-lowering drug classes would affect AD risk. In contrast, genetically instrumented exposure to PCSK9 inhibitors was predicted to increase AD risk in both of the AD samples (combined odds ratio per standard deviation lower LDL-C inducible by the drug target = 1.45, 95% confidence interval = 1.23-1.69). This risk increase was opposite to, although more modest than, the degree of protection from coronary artery disease predicted by these same methods for PCSK9 inhibition. INTERPRETATION: We did not identify genetic support for the repurposing of statins, ezetimibe, or mipomersen for AD prevention. Notwithstanding caveats to this genetic evidence, pharmacovigilance for AD risk among users of PCSK9 inhibitors may be warranted. ANN NEUROL 2020;87:30-39

Polygenic risk scores for coronary artery disease and subsequent event risk amongst established cases

BACKGROUND: There is growing evidence that polygenic risk scores (PRS) can identify individuals with elevated lifetime risk of coronary artery disease (CAD). Whether they can also be used to stratify risk of subsequent events among those surviving a first CAD event remains uncertain, with possible biological differences between CAD onset and progression, and the potential for index event bias. METHODS: Using two baseline subsamples of UK Biobank; prevalent CAD cases (N = 10 287) and individuals without CAD (N = 393 108), we evaluated associations between a CAD PRS and incident cardiovascular and fatal outcomes. RESULTS: A 1 S.D. higher PRS was associated with increased risk of incident MI in participants without CAD (OR 1.33; 95% C.I. 1.29, 1.38), but the effect estimate was markedly attenuated in those with prevalent CAD (OR 1.15; 95% C.I. 1.06, 1.25); heterogeneity P = 0.0012. Additionally, among prevalent CAD cases, we found evidence of an inverse association between the CAD PRS and risk of all-cause death (OR 0.91; 95% C.I. 0.85, 0.98) compared to those without CAD (OR 1.01; 95% C.I. 0.99, 1.03); heterogeneity P = 0.0041. A similar inverse association was found for ischaemic stroke (Prevalent CAD (OR 0.78; 95% C.I. 0.67, 0.90); without CAD (OR 1.09; 95% C.I. 1.04, 1.15), heterogeneity P < 0.001). CONCLUSIONS: Bias induced by case stratification and survival into UK Biobank may distort associations of polygenic risk scores derived from case-control studies or populations initially free of disease. Differentiating between effects of possible biases and genuine biological heterogeneity is a major challenge in disease progression research

Exploring the Role of Plasma Lipids and Statins Interventions on Multiple Sclerosis Risk and Severity: A Mendelian Randomization Study

BACKGROUND: There has been considerable interest in statins due to their pleiotropic effects beyond their lipid-lowering properties. Many of these pleiotropic effects are predominantly ascribed to Rho small guanosine triphosphatases (Rho GTPases) proteins. We aimed to genetically investigate the role of lipids and statin interventions on multiple sclerosis (MS) risk and severity. METHOD: We employed two-sample Mendelian randomization (MR) to investigate: (1) the causal role of genetically mimic both cholesterol-dependent (via low-density lipoprotein cholesterol (LDL-C) and cholesterol biosynthesis pathway) and cholesterol-independent (via Rho GTPases) effects of statins on MS risk and MS severity, (2) the causal link between lipids (high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG)) levels and MS risk and severity; and (3) the reverse causation between lipid fractions and MS risk. We used summary statistics from the Global Lipids Genetics Consortium (GLGC), eQTLGen Consortium and the International MS Genetics Consortium (IMSGC) for lipids, expression quantitative trait loci and MS, respectively (GLGC: n = 188,577; eQTLGen: n = 31,684; IMSGC (MS risk): n = 41,505; IMSGC (MS severity): n =7,069). RESULTS: The results of MR using the inverse variance weighted method show that genetically predicted RAC2, a member of cholesterol-independent pathway, (OR 0.86 (95% CI 0.78 to 0.95), p-value 3.80E-03) is implicated causally in reducing MS risk. We found no evidence for the causal role of LDL-C and the member of cholesterol biosynthesis pathway on MS risk. MR results also show that lifelong higher HDL-C (OR 1.14 (95% CI 1.04 to1.26), p-value 7.94E-03) increase MS risk but TG was not. Furthermore, we found no evidence for the causal role of lipids and genetically mimicked statins on MS severity. There is no evidence of reverse causation between MS risk and lipids. CONCLUSION: Evidence from this study suggests that RAC2 is a genetic modifier of MS risk. Since RAC2 has been reported to mediate some of the pleiotropic effects of statins, we suggest that statins may reduce MS risk via a cholesterol-independent pathway (i.e., RAC2-related mechanism(s)). MR analyses also support a causal effect of HDL-C on MS risk

Youth daily exposure to tobacco outlets and cigarette smoking behaviors: does exposure within activity space matter?

Aims: To examine whether daily exposure to tobacco outlets within activity spaces is associated with cigarette smoking and with the number of cigarettes smoked by youth that day. Design: The study used geographic ecological momentary assessment (GEMA) data that combined daily surveys with ecological momentary assessment of global positioning systems (GPS) using geographic information systems (GIS) to allow for real-time data collection of participants' environments and behaviors. Setting: Eight mid-sized California (USA) city areas. Participants: The analytical sample included 1065 days, which were clustered within 100 smoker and non-smoker participants (aged 16–20 years, 60% female). Measurements: Any cigarette smoking and number of cigarettes smoked on a given day, the number of tobacco outlets within 100 m of activity space polylines each day, the number of minutes participants spent within 100 m of tobacco outlets each day and demographic characteristics (age, sex, race/ethnicity and perceived socio-economic status). Findings: Controlling for demographic characteristics, the findings of multi-level mixed effects logistic models were inconclusive, whether or not the number of tobacco outlets within 100 m of youths' activity space polylines or the number of minutes spent within 100 m of tobacco outlets were associated with whether the participant smoked cigarettes on a given day [odds ratio (OR) = 1.05, P = 0.24; OR = 0.99, P = 0.81, respectively]. However, in multi-level zero-inflated negative binomial models, the risk of smoking an additional cigarette on a given day increased with each additional tobacco outlet [incidence rate ratio (IRR) = 1.04, P < 0.05] and each additional minute spent within 100 m of tobacco outlets (IRR = 1.01, P < 0.001) each day. Conclusions: Among young people in urban California, differences in day-to-day exposure to tobacco outlets within activity spaces does not seem to be significantly associated with whether a person smokes a cigarette on a given day, but higher exposure to tobacco outlets appears to be positively associated with the number of cigarettes smoked on that day

Flipping the odds of drug development success through human genomics

Drug development depends on accurately identifying molecular targets that both play a causal role in a disease and are amenable to pharmacological action by small molecule drugs or bio-therapeutics, such as monoclonal antibodies. Errors in drug target specification contribute to the extremely high rates of drug development failure. Integrating knowledge of genes that encode druggable targets with those that influence susceptibility to common disease has the potential to radically improve the probability of drug development success

Prioritising genetic findings for drug target identification and validation

The decreasing costs of high-throughput genetic sequencing and increasing abundance of sequenced genome data have paved the way for the use of genetic data in identifying and validating potential drug targets. However, the number of identified potential drug targets is often prohibitively large to experimentally evaluate in wet lab experiments, highlighting the need for systematic approaches for target prioritisation. In this review, we discuss principles of genetically guided drug development, specifically addressing loss-of-function analysis, colocalization and Mendelian randomisation (MR), and the contexts in which each may be most suitable. We subsequently present a range of biomedical resources which can be used to annotate and prioritise disease-associated proteins identified by these studies including 1) ontologies to map genes, proteins, and disease, 2) resources for determining the druggability of a potential target, 3) tissue and cell expression of the gene encoding the potential target, and 4) key biological pathways involving the potential target. We illustrate these concepts through a worked example, identifying a prioritised set of plasma proteins associated with non-alcoholic fatty liver disease (NAFLD). We identified five proteins with strong genetic support for involvement with NAFLD: CYB5A, NT5C, NCAN, TGFBI and DAPK2. All of the identified proteins were expressed in both liver and adipose tissues, with TGFBI and DAPK2 being potentially druggable. In conclusion, the current review provides an overview of genetic evidence for drug target identification, and how biomedical databases can be used to provide actionable prioritisation, fully informing downstream experimental validation

Therapeutic Targets for Heart Failure Identified Using Proteomics and Mendelian Randomization

Background: Heart failure (HF) is a highly prevalent disorder for which disease mechanisms are incompletely understood. The discovery of disease-associated proteins with causal genetic evidence provides an opportunity to identify new therapeutic targets. Methods: We investigated the observational and causal associations of 90 cardiovascular proteins, which were measured using affinity-based proteomic assays. First, we estimated the associations of 90 cardiovascular proteins with incident heart failure by means of a fixed-effect meta-analysis of four population-based studies, comprising a total of 3,019 participants with 732 HF events. The causal effects of HF-associated proteins were then investigated by Mendelian randomization (MR), using cis-protein quantitative loci genetic instruments identified from genome-wide association studies (GWAS) in over 30,000 individuals. To improve the precision of causal estimates, we implemented an MR model that accounted for linkage disequilibrium between instruments and tested the robustness of causal estimates through a multiverse sensitivity analysis that included up to 120 combinations of instrument selection parameters and MR models per protein. The druggability of candidate proteins was surveyed, and mechanism of action and potential on-target side effects were explored with cross-trait MR analysis. Results: 44/90 proteins were positively associated with risk of incident HF (P < 6.0 x 10-4). Among these, eight proteins had evidence of a causal association with HF that was robust to multiverse sensitivity analysis: higher CSF-1 (macrophage colony-stimulating factor 1), Gal-3 (galectin-3) and KIM-1 (kidney injury molecule 1) were positively associated with risk of HF, whereas higher ADM (adrenomedullin), CHI3L1 (chitinase-3-like protein 1), CTSL1 (cathepsin L1), FGF-23 (fibroblast growth factor 23) and MMP-12 (Matrix metalloproteinase-12) were protective. Therapeutics targeting ADM and Gal-3 are currently under evaluation in clinical trials, and all the remaining proteins were considered druggable, except KIM-1. Conclusions: We identified 44 circulating proteins that were associated with incident HF, of which eight showed evidence of a causal relationship and seven were druggable, including adrenomedullin which represents a particularly promising drug target. Our approach demonstrates a tractable roadmap for the triangulation of population genomic and proteomic data for the prioritization of therapeutic targets for complex human diseases