Center for Public Health Genomics

Firenze, Italy

Center for Public Health Genomics

Firenze, Italy
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Ramos P.S.,Medical University of South Carolina | Sajuthi S.,Center for Public Health Genomics | Langefeld C.D.,Center for Public Health Genomics | Walker S.J.,Wake Forest Institute for Regenerative Medicine
Molecular Autism | Year: 2012

Background: A growing number of clinical and basic research studies have implicated immunological abnormalities as being associated with and potentially responsible for the cognitive and behavioral deficits seen in autism spectrum disorder (ASD) children. Here we test the hypothesis that immune-related gene loci are associated with ASD. Findings: We identified 2,012 genes of known immune-function via Ingenuity Pathway Analysis. Family-based tests of association were computed on the 22,904 single nucleotide polymorphisms (SNPs) from the 2,012 immunerelated genes on 1,510 trios available at the Autism Genetic Resource Exchange (AGRE) repository. Several SNPs in immune-related genes remained statistically significantly associated with ASD after adjusting for multiple comparisons. Specifically, we observed significant associations in the CD99 molecule-like 2 region (CD99L2, rs11796490, P = 4.01 × 10 -06, OR = 0.68 (0.58-0.80)), in the jumonji AT rich interactive domain 2 (JARID2) gene (rs13193457, P = 2.71 × 10-06, OR = 0.61 (0.49-0.75)), and in the thyroid peroxidase gene (TPO) (rs1514687, P = 5.72 × 10-06, OR = 1.46 (1.24-1.72)). Conclusions: This study suggests that despite the lack of a general enrichment of SNPs in immune function genes in ASD children, several novel genes with known immune functions are associated with ASD. © 2012 Ramos et al.; licensee BioMed Central Ltd.

Langefeld C.D.,Biostatistical science | Langefeld C.D.,Center for Public Health Genomics | Divers J.,Biostatistical science | Divers J.,Center for Public Health Genomics | And 13 more authors.
Kidney International | Year: 2015

Apolipoprotein L1 gene (APOL1) G1 and G2 coding variants are strongly associated with chronic kidney disease (CKD) in African Americans (AAs). Here APOL1 association was tested with baseline estimated glomerular filtration rate (eGFR), urine albumin:creatinine ratio (UACR), and prevalent cardiovascular disease (CVD) in 2571 AAs from The Systolic Blood Pressure Intervention Trial (SPRINT), a trial assessing effects of systolic blood pressure reduction on renal and CVD outcomes. Logistic regression models that adjusted for potentially important confounders tested for association between APOL1 risk variants and baseline clinical CVD (myocardial infarction, coronary, or carotid artery revascularization) and CKD (eGFR under 60 ml/min per 1.73 m 2 and/or UACR over 30 mg/g). AA SPRINT participants were 45.3% female with a mean (median) age of 64.3 (63) years, mean arterial pressure 100.7 (100) mm Hg, eGFR 76.3 (77.1) ml/min per 1.73 m 2, and UACR 49.9 (9.2) mg/g, and 8.2% had clinical CVD. APOL1 (recessive inheritance) was positively associated with CKD (odds ratio 1.37, 95% confidence interval 1.08-1.73) and log UACR estimated slope (β) 0.33) and negatively associated with eGFR (β-3.58), all significant. APOL1 risk variants were not significantly associated with prevalent CVD (1.02, 0.82-1.27). Thus, SPRINT data show that APOL1 risk variants are associated with mild CKD but not with prevalent CVD in AAs with a UACR under 1000 mg/g. © 2015 International Society of Nephrology.

PubMed | University of Colorado at Denver, Center for Public Health Genomics, Center for Genomics and Personalized Medicine Research, University of California at Los Angeles and 3 more.
Type: | Journal: Annals of human genetics | Year: 2017

Family-based methods are a potentially powerful tool to identify trait-defining genetic variants in extended families, particularly when used to complement conventional association analysis. We utilized two-point linkage analysis and single variant association analysis to evaluate whole exome sequencing (WES) data from 1205 Hispanic Americans (78 families) from the Insulin Resistance Atherosclerosis Family Study. WES identified 211,612 variants above the minor allele frequency threshold of 0.005. These variants were tested for linkage and/or association with 50 cardiometabolic traits after quality control checks. Two-point linkage analysis yielded 10,580,600 logarithm of the odds (LOD) scores with 1148 LOD scores 3, 183 LOD scores 4, and 29 LOD scores 5. The maximal novel LOD score was 5.50 for rs2289043:T>C, in UNC5C with subcutaneous adipose tissue volume. Association analysis identified 13 variants attaining genome-wide significance (P<510

Langefeld C.D.,Center for Public Health Genomics
Nature Communications | Year: 2017

Systemic lupus erythematosus (SLE) is an autoimmune disease with marked gender and ethnic disparities. We report a large transancestral association study of SLE using Immunochip genotype data from 27,574 individuals of European (EA), African (AA) and Hispanic Amerindian (HA) ancestry. We identify 58 distinct non-HLA regions in EA, 9 in AA and 16 in HA (∼50% of these regions have multiple independent associations); these include 24 novel SLE regions (P<5 × 10(-8)), refined association signals in established regions, extended associations to additional ancestries, and a disentangled complex HLA multigenic effect. The risk allele count (genetic load) exhibits an accelerating pattern of SLE risk, leading us to posit a cumulative hit hypothesis for autoimmune disease. Comparing results across the three ancestries identifies both ancestry-dependent and ancestry-independent contributions to SLE risk. Our results are consistent with the unique and complex histories of the populations sampled, and collectively help clarify the genetic architecture and ethnic disparities in SLE. © 2017 The Author(s).

News Article | December 14, 2016

In an important step in the battle against osteoporosis, a serious brittle bone disease that affects millions, researchers have identified more than a dozen genes amid the vast human genome likely responsible for bone density and strength. The crafty approach the researchers used to find these genes - essentially identifying needles in a haystack - could speed the development of new and better treatments for osteoporosis and many other diseases. Scientists seeking to map out the human genome face a challenge akin to that faced by the astronomer peering into the night sky: The places to look and explore are almost endless. So when hunting for something specific - in this case, the genes responsible for bone density - the question becomes: Where to begin? The researchers decided the first thing to do was to figure out an answer to that question, and their approach paid big dividends. In charting the genome, scientists commonly rely on what are known as genome-wide association studies, or GWAS. These studies identify locations in the genome where genes associated with a certain disease are thought to be located. The problem, though, is that GWAS alone doesn't tell them which genes are truly influencing a disease. "What's really challenging is going that next step and figuring out which genes are responsible," said researcher Charles Farber, PhD, of the University of Virginia School of Medicine's Center for Public Health Genomics. "It's similar to dropping a pin on a map app. So the GWAS drops the pin, but it doesn't tell you anything about what's going on at that location, the mechanism through which genetic variants influence a disease." Farber's team, including researchers at the University of Maryland, Yale and Maine Medical Center Research Institute, wanted to go beyond GWAS. So they identified genes that appeared to work together, and then they mapped those genes onto the locations identified by GWAS. By cross-referencing the two, they were able to predict 33 genes that they believe are responsible for controlling bone density. Eighteen of the genes had been shown previously to play a role, but the other 15 were new. "Many were genes known to have a role in the regulation of bone mineral density. In fact, over half of them were," Farber noted. "So that was a good proof of principle." The researchers have since tested two of the previously unknown genes and confirmed that they contribute to controlling bone mineral density. The researchers don't expect their predictions will have a 100 percent success rate, but they believe the technique has great promise for helping accelerate the process of determining gene function. And by more quickly understanding gene function, they accelerate the process of developing new drugs to target those genes to treat disease. "This was a way that we could take existing data and make predictions without going [gene] locus by locus without any direction. At least now we have hypotheses that we can test," Farber said. "I think that will speed up future attempts at trying to figure out which genes are truly causative." The findings have been published in the scientific journal Cell Systems. The article was authored by Gina Calabrese, Larry D. Mesner, Joseph P. Stains, Steven M. Tommasini, Mark C. Horowitz, Clifford J. Rosen and Farber. The work was supported by the National Institutes of Health's National Institute of Arthritis and Musculoskeletal and Skin Diseases, grant R01AR057759.

Ramos P.S.,Medical University of South Carolina | Shaftman S.R.,Medical University of South Carolina | Ward R.C.,Medical University of South Carolina | Langefeld C.D.,Center for Public Health Genomics
Autoimmune Diseases | Year: 2014

The reasons for the ethnic disparities in the prevalence of systemic lupus erythematosus (SLE) and the relative high frequency of SLE risk alleles in the population are not fully understood. Population genetic factors such as natural selection alter allele frequencies over generations and may help explain the persistence of such common risk variants in the population and the differential risk of SLE. In order to better understand the genetic basis of SLE that might be due to natural selection, a total of 74 genomic regions with compelling evidence for association with SLE were tested for evidence of recent positive selection in the HapMap and HGDP populations, using population differentiation, allele frequency, and haplotype-based tests. Consistent signs of positive selection across different studies and statistical methods were observed at several SLE-associated loci, including PTPN22, TNFSF4, TET3-DGUOK, TNIP1, UHRF1BP1, BLK, and ITGAM genes. This study is the first to evaluate and report that several SLE-associated regions show signs of positive natural selection. These results provide corroborating evidence in support of recent positive selection as one mechanism underlying the elevated population frequency of SLE risk loci and supports future research that integrates signals of natural selection to help identify functional SLE risk alleles. © 2014 Paula S. Ramos et al.

Guy R.T.,Center for Public Health Genomics | Guy R.T.,University of Toronto | Santago P.,Wake forest University | Santago P.,Virginia Polytechnic Institute and State University | Langefeld C.D.,Center for Public Health Genomics
Genetic Epidemiology | Year: 2012

Complex genetic disorders are a result of a combination of genetic and nongenetic factors, all potentially interacting. Machine learning methods hold the potential to identify multilocus and environmental associations thought to drive complex genetic traits. Decision trees, a popular machine learning technique, offer a computationally low complexity algorithm capable of detecting associated sets of single nucleotide polymorphisms (SNPs) of arbitrary size, including modern genome-wide SNP scans. However, interpretation of the importance of an individual SNP within these trees can present challenges.We present a new decision tree algorithm denoted as Bagged Alternating Decision Trees (BADTrees) that is based on identifying common structural elements in a bootstrapped set of Alternating Decision Trees (ADTrees). The algorithm is order nk2, where n is the number of SNPs considered and k is the number of SNPs in the tree constructed. Our simulation study suggests that BADTrees have higher power and lower type I error rates than ADTrees alone and comparable power with lower type I error rates compared to logistic regression. We illustrate the application of these data using simulated data as well as from the Lupus Large Association Study 1 (7,822 SNPs in 3,548 individuals). Our results suggest that BADTrees hold promise as a low computational order algorithm for detecting complex combinations of SNP and environmental factors associated with disease. © 2012 Wiley Periodicals, Inc.

Furlotte N.A.,University of California at Los Angeles | Kang E.Y.,University of California at Los Angeles | Van Nas A.,University of California at Los Angeles | Farber C.R.,Center for Public Health Genomics | And 2 more authors.
Genetics | Year: 2012

Genetic studies in mouse models have played an integral role in the discovery of the mechanisms underlying many human diseases. The primary mode of discovery has been the application of linkage analysis to mouse crosses. This approach results in high power to identify regions that affect traits, but in low resolution, making it difficult to identify the precise genomic location harboring the causal variant. Recently, a panel of mice referred to as the hybrid mouse diversity panel (HMDP) has been developed to overcome this problem. However, power in this panel is limited by the availability of inbred strains. Previous studies have suggested combining results across multiple panels as a means to increase power, but the methods employed may not be well suited to structured populations, such as the HMDP. In this article, we introduce a meta-analysis-based method that may be used to combine HMDP studies with F2 cross studies to gain power, while increasing resolution. Due to the drastically different genetic structure of F2s and the HMDP, the best way to combine two studies for a given SNP depends on the strain distribution pattern in each study. We show that combining results, while accounting for these patterns, leads to increased power and resolution. Using our method to map bone mineral density, we find that two previously implicated loci are replicated with increased significance and that the size of the associated is decreased. We also map HDL cholesterol and show a dramatic increase in the significance of a previously identified result. © 2012 by the Genetics Society of America.

Hwang D.Y.,Yale University | Dell C.A.,Maryland Stroke Center | Sparks M.J.,Maryland Stroke Center | Watson T.D.,Maryland Stroke Center | And 12 more authors.
Neurology | Year: 2016

Objective: To compare the performance of formal prognostic instruments vs subjective clinical judgment with regards to predicting functional outcome in patients with spontaneous intracerebral hemorrhage (ICH). Methods: This prospective observational study enrolled 121 ICH patients hospitalized at 5 US tertiary care centers. Within 24 hours of each patient's admission to the hospital, one physician and one nurse on each patient's clinical team were each asked to predict the patient's modified Rankin Scale (mRS) score at 3 months and to indicate whether he or she would recommend comfort measures. The admission ICH score and FUNC score, 2 prognostic scales selected for their common use in neurologic practice, were calculated for each patient. Spearman rank correlation coefficients (r) with respect to patients' actual 3-month mRS for the physician and nursing predictions were compared against the same correlation coefficients for the ICH score and FUNC score. Results: The absolute value of the correlation coefficient for physician predictions with respect to actual outcome (0.75) was higher than that of either the ICH score (0.62, p 0.057) or the FUNC score (0.56, p 0.01). The nursing predictions of outcome (r 0.72) also trended towards an accuracy advantage over the ICH score (p 0.09) and FUNC score (p 0.03). In an analysis that excluded patients for whom comfort care was recommended, the 65 available attending physician predictions retained greater accuracy (r 0.73) than either the ICH score (r 0.50, p 0.02) or the FUNC score (r 0.42, p 0.004). Conclusions: Early subjective clinical judgment of physicians correlates more closely with 3-month outcome after ICH than prognostic scales. © 2015 American Academy of Neurology.

Ramos P.S.,Medical University of South Carolina | Shedlock A.M.,College of Charleston | Shedlock A.M.,Medical University of South Carolina | Langefeld C.D.,Center for Public Health Genomics
Journal of Human Genetics | Year: 2015

Human genetic diversity is the result of population genetic forces. This genetic variation influences disease risk and contributes to health disparities. Autoimmune diseases (ADs) are a family of complex heterogeneous disorders with similar underlying mechanisms characterized by immune responses against self. Collectively, ADs are common, exhibit gender and ethnic disparities, and increasing incidence. As natural selection is an important influence on human genetic variation, and immune function genes are enriched for signals of positive selection, it is thought that the prevalence of AD risk alleles seen in different population is partially the result of differing selective pressures (for example, due to pathogens). With the advent of high-throughput technologies, new analytical methodologies and large-scale projects, evidence for the role of natural selection in contributing to the heritable component of ADs keeps growing. This review summarizes the genetic regions associated with susceptibility to different ADs and concomitant evidence for selection, including known agents of selection exerting selective pressure in these regions. Examples of specific adaptive variants with phenotypic effects are included as an evidence of natural selection increasing AD susceptibility. Many of the complexities of gene effects in different ADs can be explained by population genetics phenomena. Integrating AD susceptibility studies with population genetics to investigate how natural selection has contributed to genetic variation that influences disease risk will help to identify functional variants and elucidate biological mechanisms. As such, the study of population genetics in human population holds untapped potential for elucidating the genetic causes of human disease and more rapidly focusing to personalized medicine. © 2015 The Japan Society of Human Genetics All rights reserved.

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