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"Brody, Jennifer A."
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Addressing corruption allegations in international arbitration
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.
Book
Mitochondrial DNA copy number can influence mortality and cardiovascular disease via methylation of nuclear DNA CpGs
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.
Journal Article
Prioritization of causal genes from genome-wide association studies by Bayesian data integration across loci
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.
Journal Article
Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations
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.
Journal Article
Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology
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.
Journal Article
Epigenome-wide association analysis of daytime sleepiness in the Multi-Ethnic Study of Atherosclerosis reveals African-American-specific associations
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.
Journal Article
Large-scale whole-exome sequencing association studies identify rare functional variants influencing serum urate levels
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.
Journal Article
Epigenome-wide DNA methylation association study of CHIP provides insight into perturbed gene regulation
With age, hematopoietic stem cells can acquire somatic mutations in leukemogenic genes that confer a proliferative advantage in a phenomenon termed CHIP. How these mutations result in increased risk for numerous age-related diseases remains poorly understood. We conduct a multiracial meta-analysis of EWAS of CHIP in the Framingham Heart Study, Jackson Heart Study, Cardiovascular Health Study, and Atherosclerosis Risk in Communities cohorts (
N
= 8196) to elucidate the molecular mechanisms underlying CHIP and illuminate how these changes influence cardiovascular disease risk. We functionally validate the EWAS findings using human hematopoietic stem cell models of CHIP. We then use expression quantitative trait methylation analysis to identify transcriptomic changes associated with CHIP-associated CpGs. Causal inference analyses reveal 261 CHIP-associated CpGs associated with cardiovascular traits and all-cause mortality (FDR adjusted
p
-value < 0.05). Taken together, our study reports the epigenetic changes impacted by CHIP and their associations with age-related disease outcomes.
In CHIP, somatic mutations in a hematopoietic stem cell lead to a clonal subpopulation of blood cells. Here, the authors perform a CHIP meta-EWAS to establish its epigenetic features and age-related outcomes.
Journal Article
Analysis of loss-of-function variants and 20 risk factor phenotypes in 8,554 individuals identifies loci influencing chronic disease
Eric Boerwinkle and colleagues carried out exome sequencing on 8,554 individuals and tested loss-of-function variants for association with 20 phenotypes related to common chronic diseases. They identified several new associations and illustrate the value of applying exome sequencing to a large sample of deeply phenotyped individuals.
A typical human exome harbors dozens of loss-of-function (LOF) variants
1
, which can lower disease risk factor levels and affect drug efficacy
2
. We hypothesized that LOF variants are enriched in genes influencing risk factor levels and the onset of common chronic diseases, such as cardiovascular disease and diabetes. To test this hypothesis, we sequenced the exomes of 8,554 individuals and analyzed the effects of predicted LOF variants on 20 chronic disease risk factor phenotypes. Analysis of this sample as discovery and replication strata of equal size verified two relationships in well-studied genes (
PCSK9
and
APOC3
) and identified eight new loci. Previously unknown relationships included elevated fasting glucose in carriers of heterozygous LOF variation in
TXNDC5
, which encodes a biomarker for type 1 diabetes progression, and apparent recessive effects of
C1QTNF8
on serum magnesium levels. These data demonstrate the utility of functional-variant annotation within a large sample of deeply phenotyped individuals for gene discovery.
Journal Article