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In 'Addressing Corruption Allegations in International Arbitration', Brody K. Greenwald and Jennifer A. Ivers provide a comprehensive overview of the key issues that arise in international arbitrations involving allegations of corruption by drawing upon their significant experience in these high-stakes cases, including in the only two reported investment treaty cases dismissed specifically as a result of corruption. Their monograph is a valuable resource that analyzes, among other things, the public policy against corruption, the requirements for establishing corruption, issues relating to the burden and standard of proof, how corruption has been proved in practice, and the legal consequences where corruption is established. Greenwald and Ivers also assess issues that arise where a sovereign State raises an arbitration defense based on alleged corruption, but does not prosecute the alleged wrongdoers in its domestic courts.

Background Mitochondrial DNA copy number (mtDNA-CN) has been associated with a variety of aging-related diseases, including all-cause mortality. However, the mechanism by which mtDNA-CN influences disease is not currently understood. One such mechanism may be through regulation of nuclear gene expression via the modification of nuclear DNA (nDNA) methylation. Methods To investigate this hypothesis, we assessed the relationship between mtDNA-CN and nDNA methylation in 2507 African American (AA) and European American (EA) participants from the Atherosclerosis Risk in Communities (ARIC) study. To validate our findings, we assayed an additional 2528 participants from the Cardiovascular Health Study (CHS) ( N  = 533) and Framingham Heart Study (FHS) ( N  = 1995). We further assessed the effect of experimental modification of mtDNA-CN through knockout of TFAM , a regulator of mtDNA replication, via CRISPR-Cas9. Results Thirty-four independent CpGs were associated with mtDNA-CN at genome-wide significance ( P  < 5 × 10 − 8 ). Meta-analysis across all cohorts identified six mtDNA-CN-associated CpGs at genome-wide significance ( P  < 5 × 10 − 8 ). Additionally, over half of these CpGs were associated with phenotypes known to be associated with mtDNA-CN, including coronary heart disease, cardiovascular disease, and mortality. Experimental modification of mtDNA-CN demonstrated that modulation of mtDNA-CN results in changes in nDNA methylation and gene expression of specific CpGs and nearby transcripts. Strikingly, the “neuroactive ligand receptor interaction” KEGG pathway was found to be highly overrepresented in the ARIC cohort ( P  = 5.24 × 10 − 12 ), as well as the TFAM knockout methylation ( P =  4.41 × 10 − 4 ) and expression ( P =  4.30 × 10 − 4 ) studies. Conclusions These results demonstrate that changes in mtDNA-CN influence nDNA methylation at specific loci and result in differential expression of specific genes that may impact human health and disease via altered cell signaling.

Motivation: Genome-wide association studies (GWAS) have identified genetic variants, usually single-nucleotide polymorphisms (SNPs), associated with human traits, including disease and disease risk. These variants (or causal variants in linkage disequilibrium with them) usually affect the regulation or function of a nearby gene. A GWAS locus can span many genes, however, and prioritizing which gene or genes in a locus are most likely to be causal remains a challenge. Better prioritization and prediction of causal genes could reveal disease mechanisms and suggest interventions. Results: We describe a new Bayesian method, termed SigNet for significance networks, that combines information both within and across loci to identify the most likely causal gene at each locus. The SigNet method builds on existing methods that focus on individual loci with evidence from gene distance and expression quantitative trait loci (eQTL) by sharing information across loci using protein-protein and gene regulatory interaction network data. In an application to cardiac electrophysiology with 226 GWAS loci, only 46 (20%) have within-locus evidence from Mendelian genes, protein-coding changes, or colocalization with eQTL signals. At the remaining 180 loci lacking functional information, SigNet selects 56 genes other than the minimum distance gene, equal to 31% of the information-poor loci and 25% of the GWAS loci overall. Assessment by pathway enrichment demonstrates improved performance by SigNet . Review of individual loci shows literature evidence for genes selected by SigNet , including PMP22 as a novel causal gene candidate.

Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22% for height, 27% for HDL cholesterol, 43% for body mass index, 50% for sleep duration, 58% for systolic blood pressure, 64% for total cholesterol, 66% for triglycerides, 77% for LDL cholesterol, and 100% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models. Combining a standard polygenic risk score (PRS) as a feature in a machine learning model increases the percentage variance explained for those traits, helping to account for non-linearities or interaction effects in genetics-based prediction models.

Background Systematic profiling of plasma proteins in population studies offers a complementary approach to discovery of novel risk factors and may provide new insights into the causes of coronary heart disease. Methods To explore relationships between the circulating proteome and coronary heart disease (CHD), we evaluated associations of 4780 plasma proteins with incident CHD in the Cardiovascular Health Study (CHS, N=2856, 575 CHD events) and replicated significant associations in the Atherosclerosis Risk in Communities Study (ARIC, N = 10456; 1375 events). Results We find that 11 proteins significantly associate with incident CHD after adjusting for risk factors; and eight significantly replicated in ARIC. Several proteins correlate with carotid intimal medial thickness and CHD associations are attenuated in participants without subclinical atherosclerosis. Macrophage metalloelastase (MMP12) is the strongest observed association (Hazard Ratio, 1.31; 95% Confidence Interval, 1.19-1.44). Mendelian randomization (MR) identifies a causal relationship between higher MMP12 and lower CHD (Odds Ratio, OR 0.94) and ischemic stroke (OR 0.90) risk, while reverse MR found that genetic propensity to CHD increased MMP12. Taken together, multivariable MR confirms a direct protective effect of higher plasma MMP12 on CHD risk and a genetic effect of atherosclerosis and CHD on elevating MMP12. Conclusions Proteomic analyses reveal associations with incident CHD and genomic evidence suggests that therapeutic MMP12 inhibition may confer adverse cardiovascular effects. Plain Language Summary Measuring many protein levels in the blood may provide new insights into the causes of heart disease (CHD). We evaluated thousands of plasma proteins in a study of older adults to determine whether any predicted risk of heart attacks or heart-related deaths and confirmed the findings in a second study. Eleven proteins showed evidence of association with CHD; often with stronger effects among those with pre-existing atherosclerosis. The strongest finding was for macrophage metalloelastase (MMP12), an endopeptidase expressed in vascular tissue and atherosclerotic plaque with roles in tissue repair, vascular remodeling, and reduction of inflammation. Our findings point to a complex role for this protein in response to underlying heart disease risk factors. Huber et al. measure 4,780 plasma proteins in the Cardiovascular Health Study to examine the association with incident coronary heart disease. They identify 11 proteins using genomic analyses and show the complexity of MMP12 in response to atherosclerosis and development of heart disease.

The increasing volume of whole-genome sequence (WGS) and multi-omics data requires new approaches for analysis. As one solution, we have created the cloud-based Analysis Commons, which brings together genotype and phenotype data from multiple studies in a setting that is accessible by multiple investigators. This framework addresses many of the challenges of multicenter WGS analyses, including data-sharing mechanisms, phenotype harmonization, integrated multi-omics analyses, annotation and computational flexibility. In this setting, the computational pipeline facilitates a sequence-to-discovery analysis workflow illustrated here by an analysis of plasma fibrinogen levels in 3,996 individuals from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) WGS program. The Analysis Commons represents a novel model for translating WGS resources from a massive quantity of phenotypic and genomic data into knowledge of the determinants of health and disease risk in diverse human populations.

Daytime sleepiness is a consequence of inadequate sleep, sleep-wake control disorder, or other medical conditions. Population variability in prevalence of daytime sleepiness is likely due to genetic and biological factors as well as social and environmental influences. DNA methylation (DNAm) potentially influences multiple health outcomes. Here, we explored the association between DNAm and daytime sleepiness quantified by the Epworth Sleepiness Scale (ESS). We performed multi-ethnic and ethnic-specific epigenome-wide association studies for DNAm and ESS in the Multi-Ethnic Study of Atherosclerosis (MESA; n = 619) and the Cardiovascular Health Study (n = 483), with cross-study replication and meta-analysis. Genetic variants near ESS-associated DNAm were analyzed for methylation quantitative trait loci and followed with replication of genotype-sleepiness associations in the UK Biobank. In MESA only, we detected four DNAm-ESS associations: one across all race/ethnic groups; three in African-Americans (AA) only. Two of the MESA AA associations, in genes KCTD5 and RXRA, nominally replicated in CHS (p-value < 0.05). In the AA meta-analysis, we detected 14 DNAm-ESS associations (FDR q-value < 0.05, top association p-value = 4.26 × 10-8). Three DNAm sites mapped to genes (CPLX3, GFAP, and C7orf50) with biological relevance. We also found evidence for associations with DNAm sites in RAI1, a gene associated with sleep and circadian phenotypes. UK Biobank follow-up analyses detected SNPs in RAI1, RXRA, and CPLX3 with nominal sleepiness associations. We identified methylation sites in multiple genes possibly implicated in daytime sleepiness. Most significant DNAm-ESS associations were specific to AA. Future work is needed to identify mechanisms driving ancestry-specific methylation effects.

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS , ADM17 , PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis . A method that allows for the detection of mosaic chromosomal alterations from blood whole-genome sequencing data highlights ancestry-specific differences in the distribution of common and rare germline susceptibility variants.

Elevated serum urate levels can cause gout, an excruciating disease with suboptimal treatment. Previous GWAS identified common variants with modest effects on serum urate. Here we report large-scale whole-exome sequencing association studies of serum urate and kidney function among ≤19,517 European ancestry and African-American individuals. We identify aggregate associations of low-frequency damaging variants in the urate transporters SLC22A12 (URAT1; p  = 1.3 × 10 −56 ) and SLC2A9 ( p  = 4.5 × 10 −7 ). Gout risk in rare SLC22A12 variant carriers is halved (OR = 0.5, p  = 4.9 × 10 −3 ). Selected rare variants in SLC22A12 are validated in transport studies, confirming three as loss-of-function (R325W, R405C, and T467M) and illustrating the therapeutic potential of the new URAT1-blocker lesinurad. In SLC2A9 , mapping of rare variants of large effects onto the predicted protein structure reveals new residues that may affect urate binding. These findings provide new insights into the genetic architecture of serum urate, and highlight molecular targets in SLC22A12 and SLC2A9 for lowering serum urate and preventing gout. Elevated serum urate levels are a risk factor for gout. Here, Tin et al. perform whole-exome sequencing in 19,517 individuals and detect low-frequency genetic variants in urate transporter genes, SLC22A12 and SLC2A9 , associated with serum urate levels and confirm their damaging nature in vitro and in silico.