Abstract Background Studies evaluating the association between obesity and pelvic organ prolapse (POP) report estimates that range from negative to positive associations. Heterogeneous definitions ...for POP and variable choices for categorizing obesity measures have made it challenging to conduct meta-analysis. Objective We systematically evaluated evidence to provide quantitative summaries of association between degrees of obesity and POP, and identify sources of heterogeneity. Evidence acquisition and method of synthesis We searched for all indexed publications relevant to POP up until June 18, 2015 in PubMed/Medline to identify analytical observational studies published in English that reported risk ratios (relative risk, odds ratio or hazard ratio) for body mass index (BMI) categories in relation to POP. Random-effects meta-analyses were conducted to report associations with POP for overweight and obese BMI categories compared with women in the normal-weight category (referent: BMI <25 kg/m2 ). Results Of the seventy studies that reported evidence on obesity and POP, 22 eligible studies provided effect estimates for meta-analysis of the overweight and obese BMI categories. Compared with the referent category, women in the overweight and obese categories had meta-analysis risk ratios of at least 1.36 (95% confidence interval CI: 1.20, 1.53) and at least 1.47 (95% CI: 1.35, 1.59), respectively. Subgroup analyses showed effect estimates for objectively-measured clinically-significant POP were higher than for self-reported POP. Other potential sources of heterogeneity included proportion of post-menopausal women in study and reported study design. Conclusions Overweight and obese women are more likely to have POP compared with women with BMI in the normal range. The finding that the associations for obesity measures were strongest for objectively-measured, clinically-significant POP further strengthens this evidence. However, prospective investigations evaluating obesity and POP are few.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The functional interpretation of genome-wide association studies (GWAS) is challenging due to the cell-type-dependent influences of genetic variants. Here, we generated comprehensive maps of ...expression quantitative trait loci (eQTLs) for 659 microdissected human kidney samples and identified cell-type-eQTLs by mapping interactions between cell type abundances and genotypes. By partitioning heritability using stratified linkage disequilibrium score regression to integrate GWAS with single-cell RNA sequencing and single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing data, we prioritized proximal tubules for kidney function and endothelial cells and distal tubule segments for blood pressure pathogenesis. Bayesian colocalization analysis nominated more than 200 genes for kidney function and hypertension. Our study clarifies the mechanism of commonly used antihypertensive and renal-protective drugs and identifies drug repurposing opportunities for kidney disease.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple—even distinct—traits. ...Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10−8) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10−7) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Chronic kidney disease (CKD), defined by low estimated glomerular filtration rate (eGFR), contributes to global morbidity and mortality. Here we conduct a transethnic Genome-Wide Association Study of ...eGFR in 280,722 participants of the Million Veteran Program (MVP), with replication in 765,289 participants from the Chronic Kidney Disease Genetics (CKDGen) Consortium. We identify 82 previously unreported variants, confirm 54 loci, and report interesting findings including association of the sickle cell allele of betaglobin among non-Hispanic blacks. Our transcriptome-wide association study of kidney function in healthy kidney tissue identifies 36 previously unreported and nine known genes, and maps gene expression to renal cell types. In a Phenome-Wide Association Study in 192,868 MVP participants using a weighted genetic score we detect associations with CKD stages and complications and kidney stones. This investigation reinterprets the genetic architecture of kidney function to identify the gene, tissue, and anatomical context of renal homeostasis and the clinical consequences of dysregulation.
Diseases such as uterine leiomyomata (fibroids and benign tumors of the uterus) and keloids (raised scars) may share common etiology. Fibroids and keloids can co-occur in individuals, and both are ...highly heritable, suggesting they may share common genetic risk factors. Fibroproliferative diseases are common and characterized by scarring and overgrowth of connective tissue, impacting multiple organ systems. These conditions both have racial disparities in prevalence, with the highest prevalence observed among individuals of African ancestry. Several fibroproliferative diseases are more severe and common in populations of sub-Saharan Africa. This mini-review aims to provide a broad overview of the current knowledge of the evolutionary origins and causes of fibroproliferative diseases. We also discuss current hypotheses proposing that the increased prevalence of these diseases in African-derived populations is due to the selection for profibrotic alleles that are protective against helminth infections and provide examples from knowledge of uterine fibroid and keloid research.
Jukarainen et al. provide a novel perspective on the interpretation of heritable risk factors and human health. This study provides opportunities to focus translational efforts, characterize genetic ...influences on disease disparities, and improve communication between clinicians and patients regarding genetic risks. We describe their approach and discuss its implications, utility, and limitations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The causes for disparities in implementation of precision medicine are complex, due in part to differences in clinical care and a lack of engagement and recruitment of under-represented populations ...in studies. New tools and large genetic cohorts can change these circumstances and build access to personalized medicine for disadvantaged populations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Fibroproliferative diseases are common complex traits featuring scarring and overgrowth of connective tissue which vary widely in presentation because they affect many organ systems. Most ...fibroproliferative diseases are more prevalent in African-derived populations than in European populations, leading to pronounced health disparities. It is hypothesized that the increased prevalence of these diseases in African-derived populations is due to selection for pro-fibrotic alleles that are protective against helminth infections. We constructed a genetic risk score (GRS) of fibroproliferative disease risk-increasing alleles using 147 linkage disequilibrium-pruned variants identified through genome-wide association studies of seven fibroproliferative diseases with large African-European prevalence disparities. A comparison of the fibroproliferative disease GRS between 1000 Genomes Phase 3 populations detected a higher mean GRS in AFR (mean = 148 risk alleles) than EUR (mean = 136 risk alleles; T-test p-value = 1.75x10-123). To test whether differences in GRS burden are systematic and may be due to selection, we employed the quantitative trait loci (QTL) sign test. The QTL sign test result indicates that population differences in risk-increasing allele burdens at these fibroproliferative disease variants are systematic and support a model featuring selective pressure (p-value = 0.011). These observations were replicated in an independent sample and were more statistically significant (T-test p-value = 7.26x10-237, sign test p-value = 0.015). This evidence supports the role of selective pressure acting to increase frequency of fibroproliferative alleles in populations of African relative to European ancestry populations.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Race, specifically African ancestry, and obesity are important risk factors for uterine fibroids, and likely interact to provide the right conditions for fibroid growth. However, existing studies ...largely focus on the main-effects rather than their interaction. Here, we firstly provide evidence for interaction between categories of body mass index (BMI) and reported-race in relation to uterine fibroids. We then investigate whether the association between inferred local European ancestry and fibroid risk is modified by BMI in African American (AA) women in the Vanderbilt University Medical Center bio-repository (BioVU) (539 cases and 794 controls) and the Coronary Artery Risk Development in Young Adults study (CARDIA, 264 cases and 173 controls). We used multiple logistic regression to evaluate interactions between local European ancestry and BMI in relation to fibroid risk, then performed fixed effects meta-analysis. Statistical significance threshold for local-ancestry and BMI interactions was empirically estimated with 10,000 permutations (p-value = 1.18x10-4). Admixture mapping detected an association between European ancestry and fibroid risk which was modified by BMI (continuous-interaction p-value = 3.75x10-5) around ADTRP (chromosome 6p24); the strongest association was found in the obese category (ancestry odds ratio (AOR) = 0.51, p-value = 2.23x10-5). Evaluation of interaction between genotyped/imputed variants and BMI in this targeted region suggested race-specific interaction, present in AAs only; strongest evidence was found for insertion/deletion variant (6:11946435), again in the obese category (OR = 1.66, p-value = 1.72x10-6). We found nominal evidence for interaction between local ancestry and BMI at a previously reported region in chromosome 2q31-32, which includes COL5A2, and TFPI, an immediate downstream target of ADTRP. Interactions between BMI and SNPs (single nucleotide polymorphisms) found in this region in AA women were also detected in an independent European American population of 1,195 cases and 1,164 controls. Findings from our study provide an example of how modifiable and non-modifiable factors may interact to influence fibroid risk and suggest a biological role for BMI in fibroid etiology.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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