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Genetic studies of human diseases have identified many variants associated with pathogenesis and severity. However, most studies have used only statistical association to assess putative relationships to disease, and ignored other factors for evaluation. For example, evolution is a factor that has shaped disease risk, changing allele frequencies as human populations migrated into and inhabited new environments. Since many common variants differ among populations in frequency, as does disease prevalence, we hypothesized that patterns of disease and population structure, taken together, will inform association studies. Thus, the population distributions of allelic risk variants should reflect the distributions of their associated diseases. Evolutionary Triangulation (ET) exploits this evolutionary differentiation by comparing population structure among three populations with variable patterns of disease prevalence. By selecting populations based on patterns where two have similar rates of disease that differ substantially from a third, we performed a proof of principle analysis for this method. We examined three disease phenotypes, lactase persistence, melanoma, and Type 2 diabetes mellitus. We show that for lactase persistence, a phenotype with a simple genetic architecture, ET identifies the key gene, lactase. For melanoma, ET identifies several genes associated with this disease and/or phenotypes related to it, such as skin color genes. ET was less obviously successful for Type 2 diabetes mellitus, perhaps because of the small effect sizes in known risk loci and recent environmental changes that have altered disease risk. Alternatively, ET may have revealed new genes involved in conferring disease risk for diabetes that did not meet nominal GWAS significance thresholds. We also compared ET to another method used to filter for phenotype associated genes, population branch statistic (PBS), and show that ET performs better in identifying genes known to associate with diseases appropriately distributed among populations. Our results indicate that ET can filter association results to improve our ability to discover disease loci. [ABSTRACT FROM AUTHOR]

Background: The host response to infection by Plasmodium falciparum, the parasite most often responsible for severe malaria, ranges from asymptomatic parasitaemia to death. The clinical trajectory of malaria is influenced by host genetics and parasite load, but the factors determining why some infections produce uncomplicated malaria and some proceed to severe disease remain incompletely understood. Methods: To identify molecular markers of severe falciparum malaria, human gene expression patterns were compared between children aged 6 months to 5 years with severe and uncomplicated malaria who were enrolled in a case-control study in Bandiagara, Mali. Microarrays were used to obtain expression data on severe cases and uncomplicated controls at the time of acute disease presentation (five uncomplicated and five severe), 1 week after presentation (three uncomplicated and three severe) and treatment initiation, and in the subsequent dry season (late convalescence, four uncomplicated and four severe). This is a pilot study for the first use of microarray technology in Mali. Results: Complement and toll-like receptor (TLR) pathways were differentially expressed, with severe cases showing higher expression of the C1q, TLR2, TLR4, TLR8, and CR1 genes. Other genes previously associated with malaria pathogenesis, GZMB, FOS and HSPA6, were also higher among severe cases. TLR2, TLR4, TLR8, CR1, GZMB, FOS, and HSPA6 genes were expressed at lower levels in severe cases at late convalescence. Conclusions: Overexpression of genes previously associated with uncomplicated malaria was associated with severe disease. Low baseline expression of these genes may represent candidate markers for severe malaria. Despite the small sample size, results of this pilot study offer promising targets for follow-up analyses. [ABSTRACT FROM AUTHOR]

Background: We sought to identify and characterize predictors of poor fetal growth among variables extracted from perinatal medical records to gain insight into potential etiologic mechanisms. In this process we reevaluated a previously observed association between poor fetal growth and maternal psychiatric disease. Methods: We evaluated 449 deliveries of >36 weeks gestation that occurred between 9/2008 and 9/2010 at the Women and Infants Hospital in Providence Rhode Island. This study group was oversampled for Small-for- Gestational-Age (SGA) infants and excluded Large-for-Gestational-Age (LGA) infants. We assessed the associations between recorded clinical variables and impaired fetal growth: SGA or Intrauterine Growth Restriction (IUGR) diagnosis. After validating the previously observed association between maternal psychiatric disease and impaired fetal growth we addressed weaknesses in the prior studies by explicitly considering antidepressant use and the timing of symptoms with respect to pregnancy. We then evaluated DNA methylation levels at 27 candidate loci in placenta from a subset of these deliveries (n = 197) to examine if epigenetic variation could provide insight into the mechanisms that cause this co-morbidity. Results: Infants of mothers with prenatal psychiatric disease (Depression, Anxiety, OCD/Panic) had increased odds of poor fetal growth (ORadjusted = 3.36, 95%CI: 1.38-8.14). This relationship was similar among those who were treated with antidepressants (ORadjusted = 3.69, 95%CI: 1.31-10.45) and among those who were not (ORadjusted = 3.19, 95%CI: 1.30-7.83). Among those with a history of psychiatric disease but no active disease in pregnancy the ORadjusted was 0.45 (95%CI: 0.09-2.35). A locus near the transcription start site of the leptin receptor (cg21655790) had methylation levels that were decreased in the presence of: 1) SGA/IUGR, and 2) active but not resolved psychiatric disease (among mothers not on antidepressants). Conclusions: These results validate and further characterize the association between maternal psychiatric disease and poor fetal growth. Because the association appears to depend on active psychiatric disease, this suggests a transient and potentially modifiable pathophysiology. The molecular findings in this study suggest that altered leptin signaling may be involved in the biological mechanisms that link prenatal maternal psychiatric symptoms and poor fetal growth. [ABSTRACT FROM AUTHOR]

Background: Preterm birth is the leading cause of mortality and morbidity in newborn infants. Its etiology is multifactorial with genes and environmental factors, including chronic maternal stress, contributing to its risk. Our objective was to investigate whether single nucleotide polymorphisms (SNPs) in genes involved in the stress response are associated with spontaneous preterm birth using a candidate gene approach. Methods: A total of 210 cases (singleton spontaneous preterm birth at <37 weeks) and 412 controls (singleton term birth at 38-42 weeks without a history of preterm birth) were studied. High quality maternal DNA was available from saliva samples of 190 cases and 369 controls and compared. Sociodemographic and medical data were collected. Sixteen SNPs, either tag SNPs located in key genes involved in the stress response identified in the Preterm Birth Genome Project database or SNPs found to be associated with adverse mental health outcomes in the published literature, were selected for genotyping and sequencing. SNPs were genotyped using Taqman® SNP genotyping assays. Univariate and multivariate logistic regression were performed. Results: Multivariate analysis showed that two SNPs located in the mineralocorticoid receptor gene were significantly associated with spontaneous preterm birth: rs17484063 (OR 0.50, p = 0.038) and rs2883929 (OR 0.49, p = 0.017), regardless of maternal age, smoking, alcohol use, educational status, and history of spontaneous miscarriage. Conclusion: This report demonstrates an association between mineralocorticoid receptor gene polymorphisms, rs17484063 and rs2883929, and preterm birth, supporting a role for genetics in the association between chronic maternal stress and preterm birth. Potentially, this information may be used to predicting the risk of having a preterm delivery. [ABSTRACT FROM AUTHOR]

In omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot completely eliminate false positive conclusions, and it can also mask many true associations that might otherwise advance our understanding of pathology. These issues beg the question: How can we increase the amount of knowledge gained from high throughput genetic data? To address this challenge, we present an approach that complements standard statistical validation methods by drawing attention to both potential false negative and false positive conclusions, as well as providing broad information for directing future research. The Diverse Convergent Evidence approach (DiCE) we propose integrates information from multiple sources (omics, informatics, and laboratory experiments) to estimate the strength of the available corroborating evidence supporting a given association. This process is designed to yield an evidence metric that has utility when etiologic heterogeneity, variable risk factor frequencies, and a variety of observational data imperfections might lead to false conclusions. We provide proof of principle examples in which DiCE identified strong evidence for associations that have established biological importance, when standard validation methods alone did not provide support. If used as an adjunct to standard validation methods this approach can leverage multiple distinct data types to improve genetic risk factor discovery/validation, promote effective science communication, and guide future research directions. [ABSTRACT FROM AUTHOR]

Background Aminoglycosides are a critical component of multidrug-resistant tuberculosis (MDR-TB) treatment but data on their efficacy and adverse effects in this population is scarce. We determined the effect of amikacin over treatment outcomes and development of hearing loss in MDR-TB patients. Methods Patients started on MDR-TB treatment between 2006 and 2012 were included. Multivariate analysis was used to determine the effect of amikacin over treatment outcomes and development of hearing loss. Results 437 MDR-TB patients were included, 288 (66%) of whom were HIV co-infected. 270 (62%) developed hearing loss, of whom 147 (54%) had audiometry. 313 (72%) patients who completed treatment, 228 (73%) had a good outcome (cure or treatment completion). Good outcome was associated with longer amikacin treatment (adjusted OR [aOR] 1.13, 95%CI 1.06 - 1.21) and higher dosage (aOR 1.90, 95%CI 1.12 – 2.99). Longer amikacin duration (aOR 1.98, 95%CI 1.86 – 2.12) and higher dosage per weight per month (aOR 1.15, 95%CI 1.04 – 1.28) were associated with development of hearing loss. Amikacin treatment duration modified the effect of the dosage on the risk of hearing loss, increasing this risk as the duration increased. Conclusions Amikacin is effective for MDR-TB treatment, but is associated with a high incidence of hearing loss. Total treatment duration and average monthly amikacin dose were associated with improved outcomes; however these were also associated with development of hearing loss. [ABSTRACT FROM AUTHOR]

Background: Identifying high-order genetics associations with non-additive (i.e. epistatic) effects in population-based studies of common human diseases is a computational challenge. Multifactor dimensionality reduction (MDR) is a machine learning method that was designed specifically for this problem. The goal of the present study was to apply MDR to mining high-order epistatic interactions in a population-based genetic study of tuberculosis (TB). Results: The study used a previously published data set consisting of 19 candidate single-nucleotide polymorphisms (SNPs) in 321 pulmonary TB cases and 347 healthy controls from Guniea-Bissau in Africa. The ReliefF algorithm was applied first to generate a smaller set of the five most informative SNPs. MDR with 10-fold crossvalidation was then applied to look at all possible combinations of two, three, four and five SNPs. The MDR model with the best testing accuracy (TA) consisted of SNPs rs2305619, rs187084, and rs11465421 (TA = 0.588) in PTX3, TLR9 and DC-Sign, respectively. A general 1000-fold permutation test of the null hypothesis of no association confirmed the statistical significance of the model (p = 0.008). An additional 1000-fold permutation test designed specifically to test the linear null hypothesis that the association effects are only additive confirmed the presence of non-additive (i.e. nonlinear) or epistatic effects (p = 0.013). An independent information-gain measure corroborated these results with a third-order epistatic interaction that was stronger than any lower-order associations. Conclusions: We have identified statistically significant evidence for a three-way epistatic interaction that is associated with susceptibility to TB. This interaction is stronger than any previously described one-way or two-way associations. This study highlights the importance of using machine learning methods that are designed to embrace, rather than ignore, the complexity of common diseases such as TB. We recommend future studies of the genetics of TB take into account the possibility that high-order epistatic interactions might play an important role in disease susceptibility. [ABSTRACT FROM AUTHOR]

Background: It is increasingly clear that common human diseases have a complex genetic architecture characterized by both additive and nonadditive genetic effects. The goal of the present study was to determine whether patterns of both additive and nonadditive genetic associations aggregate in specific functional groups as defined by the Gene Ontology (GO). Results: We first estimated all pairwise additive and nonadditive genetic effects using the multifactor dimensionality reduction (MDR) method that makes few assumptions about the underlying genetic model. Statistical significance was evaluated using permutation testing in two genome-wide association studies of ALS. The detection data consisted of 276 subjects with ALS and 271 healthy controls while the replication data consisted of 221 subjects with ALS and 211 healthy controls. Both studies included genotypes from approximately 550,000 single nucleotidepolymorphisms (SNPs). Each SNP was mapped to a gene if it was within 500 kb of the start or end. Each SNP was assigned a p-value based on its strongest joint effect with the other SNPs. We then used the Exploratory Visual Analysis (EVA) method and software to assign a p-value to each gene based on the overabundance of significant SNPs at the α = 0.05 level in the gene. We also used EVA to assignp-values to each GO group based on the overabundance of significant genes at the α = 0.05 level. A GO category was determined to replicate if that category was significant at the α = 0.05 level in both studies. We found two GO categories that replicated in both studies. The first, 'Regulation of Cellular Component Organization and Biogenesis', a GO Biological Process, had p-values of 0.010 and 0.014 in the detection and replication studies, respectively. The second, 'Actin Cytoskeleton', a GO Cellular Component, had p-values of 0.040 and 0.046 in the detection and replication studies, respectively. Conclusions: Pathway analysis of pairwise genetic associations in two GWAS of sporadic ALS revealed a set of genes involved in cellular component organization and actin cytoskeleton, more specifically, that were not reported by prior GWAS. However, prior biological studies have implicated actin cytoskeleton in ALS and other motor neuron diseases. This study supports the idea that pathway-level analysis of GWAS data may discover important associations not revealed using conventional one-SNP-at-a-time approaches [ABSTRACT FROM AUTHOR]

Background: The interleukin-1 (IL-1) family of cytokines is involved in the inflammatory and repair reactions of skeletal muscle during and after exercise. Specifically, plasma levels of the IL-1 receptor antagonist (IL-1ra) increase dramatically after intense exercise, and accumulating evidence points to an effect of genetic polymorphisms on athletic phenotypes. Therefore, the IL-1 family cytokine genes are plausible candidate genes for athleticism. We explored whether IL-1 polymorphisms are associated with athlete status in European subjects. Methods: Genomic DNA was obtained from 205 (53 professional and 152 competitive non-professional) Italian athletes and 458 non-athlete controls. Two diallelic polymorphisms in the IL-1β gene (IL-1B) at -511 and +3954 positions, and a variable number tandem repeats (VNTR) in intron 2 of the IL-1ra gene (IL-1RN) were assessed. Results: We found a 2-fold higher frequency of the IL-1RN ½ genotype in athletes compared to non-athlete controls (OR = 1.93, 95% CI = 1.37-2.74, 41.0% vs. 26.4%), and a lower frequency of the 1/1 genotype (OR = 0.55, 95% CI = 0.40-0.77, 43.9% vs. 58.5%). Frequency of the IL-1RN 2/2 genotype did not differ between groups. No significant differences between athletes and controls were found for either -511 or +3954 IL-1B polymorphisms. However, the haplotype (-511)C-(+3954)T-(VNTR)2 was 3-fold more frequent in athletes than in non-athletes (OR = 3.02, 95% CI = 1.16-7.87). Interestingly, the IL-1RN 1/2 genotype was more frequent in professional than in nonprofessional athletes (OR = 1.92, 95% CI = 1.02-3.61, 52.8% vs. 36.8%). Conclusions: Our study found that variants at the IL-1ra gene associate with athletic status. This confirms the crucial role that cytokine IL-1ra plays in human physical exercise. The VNTR IL-1RN polymorphism may have implications for muscle health, performance, and/or recovery capacities. Further studies are needed to assess these specific issues. As VNTR IL-1RN polymorphism is implicated in several disease conditions, athlete status may constitute a confounding variable that will need to be accounted for when examining associations of this polymorphism with disease risk. [ABSTRACT FROM AUTHOR]

There are many challenges and opportunities for Africans in the emerging area of genome medicine. In particular, there is a need for investment in local education using real-world African genetic data sets. Cloud-based computing platforms offer one solution for engaging the next generation of biomedical scientists in tackling disease in Africa, and by extension, the world. [ABSTRACT FROM AUTHOR]

There are many challenges and opportunities for Africans in the emerging area of genome medicine. In particular, there is a need for investment in local education using real-world African genetic data sets. Cloud-based computing platforms offer one solution for engaging the next generation of biomedical scientists in tackling disease in Africa, and by extension, the world. [ABSTRACT FROM AUTHOR]

The authors focus on the use and analysis of big data (BD) in the field of biological sciences. Topics discussed include shift in research practices due to the influx of BD in biology, reductionist approach to BD use and the disadvantages of relying on automated process of data analysis. The need to refocus the approach to BD and synergy between biologists and BD researchers is also discussed.

An editorial is presented which examines the role of biological data mining in understanding nonlinear relationships between variables of linear regression models used for the study of human genetics. According to the author, heritability is used to examine the relationship between variations in cholesterol levels or blood pressure and variations in the deoxyribonucleic acid (DNA) sequence.

Background Recent advances in genotyping techniques and genomic knowledge via the Hap Map and Human Genome projects allow for true Genome-Wide Association (GWA) analysis for common complex diseases such as heart disease, diabetes, and Alzheimer's. A major obstacle in GWA analysis is the prohibitively high cost of genotyping the possibly thousands of individuals necessary to achieve statistical significance of results. One potential solution is to pool the DNA of case and control populations and to determine the genotype allele frequency differences in these populations by pooled allelotyping. While pooling can dramatically save time and money, it also adds sources of error. Our work has created a system process that allows for direct evaluation and comparison of pooled allelotyping to individual genotyping for GWA association analysis of complex disease. Materials and methods Complex disease penetrance functions were calculated for a 3 locus bi-allelic model with additive or multiplicative allelic spectrums using GenomeSIM software [1]. Penetrance probabilities were calculated for genotypes having from 0 to 6 disease-associated alleles. All probability functions used a base penetrance probability of 10% disease risk to account for environmental influences on disease risk. A total of 25,000 individual genotype files were created, each comprised of 10,000 SNPs with 3 disease-associated loci imbedded within. Custom MATLAB scripts were used to make in silico "pseudo-pools" for pooled allelotyping from the individual genotype files. HAPLOVIEW software was used to conduct individual genotyping association analysis [2]. A modified version of the Pooled DNA Analyzer (PDA) program was used for pooled association analysis [3]. Conclusion Power analysis was conducted for individual genotyping and pooled allelotyping with allele frequency estimation error from levels from 1% to 5% (see Figure 1). Our results show that pooling errors have a very large effect on the overall statistical significance of a pooled GWA study. Even a pooling error of 1% shifted the minimum resolvable relative risk (RR) with 80% power from (1.33-1.5) in individual genotyping to (1.5-1.67) in pooling. Pooling with 2% error had a minimum resolvable RR of (1.67-1.83). Pooling with 3% error resolved at RR (2.0-2.33). Further, pooling with 4% and 5% error of was not able to achieve 80% power at any of the levels of relative risk tested. Thus, pooled GWA studies may be limited to resolving complex disease associated variants with medium to high relative risks ratios. [ABSTRACT FROM AUTHOR]

Genetic studies of human diseases have identified many variants associated with pathogenesis and severity. However, most studies have used only statistical association to assess putative relationships to disease, and ignored other factors for evaluation. For example, evolution is a factor that has shaped disease risk, changing allele frequencies as human populations migrated into and inhabited new environments. Since many common variants differ among populations in frequency, as does disease prevalence, we hypothesized that patterns of disease and population structure, taken together, will inform association studies. Thus, the population distributions of allelic risk variants should reflect the distributions of their associated diseases. Evolutionary Triangulation (ET) exploits this evolutionary differentiation by comparing population structure among three populations with variable patterns of disease prevalence. By selecting populations based on patterns where two have similar rates of disease that differ substantially from a third, we performed a proof of principle analysis for this method. We examined three disease phenotypes, lactase persistence, melanoma, and Type 2 diabetes mellitus. We show that for lactase persistence, a phenotype with a simple genetic architecture, ET identifies the key gene, lactase. For melanoma, ET identifies several genes associated with this disease and/or phenotypes related to it, such as skin color genes. ET was less obviously successful for Type 2 diabetes mellitus, perhaps because of the small effect sizes in known risk loci and recent environmental changes that have altered disease risk. Alternatively, ET may have revealed new genes involved in conferring disease risk for diabetes that did not meet nominal GWAS significance thresholds. We also compared ET to another method used to filter for phenotype associated genes, population branch statistic (PBS), and show that ET performs better in identifying genes known to associate with diseases appropriately distributed among populations. Our results indicate that ET can filter association results to improve our ability to discover disease loci.

Background: The worldwide administration of bivalent and quadrivalent HPV vaccines has resulted in cross-protection against non-vaccine HPV types. Infection with multiple HPV types may offer similar cross-protection in the natural setting. We hypothesized that infections with two or more HPV types from the same species, and independently, infections with two or more HPV types from different species, associate with protection from high-grade lesions., Findings: We recruited a cohort of 94 HIV, HPV-positive women from Botswana, with Grade 2 or higher cervical intraepithelial neoplasia. Infections with 2 or more HPV types from a single species associated with reduced lesion severity in univariate analysis (OR = 0.41, 95% CI 0.18-0.97, p = 0.042), when adjusted for the presence of HPV 16 or 18 types (OR = 0.41, 95% CI 0.17-1.00, p = 0.049), or all high-risk HPV type infections (OR = 0.38, 95% CI 0.16-0.90, p = 0.028). Infections with 2 or more HPV types from different species did not associate (OR = 0.68, 95% CI 0.25-1.81, p = 0.435)., Conclusions: Our findings show that co-infections with genetically similar HPV types reduce the likelihood of progression to high-grade lesions in HIV positive women, an effect not observed in co-infections with taxonomically different HPV types. This observation is possibly caused by an immune cross-protection through a similar mechanism to that observed after HPV vaccination.

Background: Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes., Results: The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle., Conclusions: We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases' common biology, and in the elaboration of diagnosis and treatments.

Genomic regions of interest can be narrowed by studying populations that have patterns of low linkage disequilibrium. A recent study of body mass index in African Americans demonstrated this point and, through cross-population analyses, revealed additional genomic associations. This comparative analysis showed how rare alleles that associate with traits in specific populations can be detected in cohorts where the same alleles are not rare, and highlights how population diversity can aid genetic analyses.

Background: Spontaneous preterm delivery (PTD) has a multifactorial etiology with evidence of a genetic contribution to its pathogenesis. A number of candidate gene case-control studies have been performed on spontaneous PTD, but the results have been inconsistent, and do not fully assess the role of how two genotypes can impact outcome. To elucidate this latter point we re-analyzed data from a previously published case-control candidate gene study, using a case-parent triad design and a hybrid design combining case-parent triads and control-mother dyads. These methods offer a robust approach to genetic association studies for PTD compared to traditional case-control designs., Methods: The study participants were obtained from the Norwegian Mother and Child Cohort Study (MoBa). A total of 196 case triads and 211 control dyads were selected for the analysis. A case-parent triad design as well as a hybrid design was used to analyze 1,326 SNPs from 159 candidate genes. We compared our results to those from a previous case-control study on the same samples. Haplotypes were analyzed using a sliding window of three SNPs and a pathway analysis was performed to gain biological insight into the pathophysiology of preterm delivery., Results: The most consistent significant fetal gene across all analyses was COL5A2. The functionally similar COL5A1 was significant when combining fetal and maternal genotypes. PON1 was significant with analytical approaches for single locus association of fetal genes alone, but was possibly confounded by maternal effects. Focal adhesion (hsa04510), Cell Communication (hsa01430) and ECM receptor interaction (hsa04512) were the most constant significant pathways., Conclusion: This study suggests a fetal association of COL5A2 and a combined fetal-maternal association of COL5A1 with spontaneous PTD. In addition, the pathway analysis implied interactions of genes affecting cell communication and extracellular matrix.

A report on the African Society of Human Genetics meeting, Cape Town, South Africa, 6-9 March 2011.

Background: The interleukin-1 (IL-1) family of cytokines is involved in the inflammatory and repair reactions of skeletal muscle during and after exercise. Specifically, plasma levels of the IL-1 receptor antagonist (IL-1ra) increase dramatically after intense exercise, and accumulating evidence points to an effect of genetic polymorphisms on athletic phenotypes. Therefore, the IL-1 family cytokine genes are plausible candidate genes for athleticism. We explored whether IL-1 polymorphisms are associated with athlete status in European subjects., Methods: Genomic DNA was obtained from 205 (53 professional and 152 competitive non-professional) Italian athletes and 458 non-athlete controls. Two diallelic polymorphisms in the IL-1beta gene (IL-1B) at -511 and +3954 positions, and a variable number tandem repeats (VNTR) in intron 2 of the IL-1ra gene (IL-1RN) were assessed., Results: We found a 2-fold higher frequency of the IL-1RN 1/2 genotype in athletes compared to non-athlete controls (OR = 1.93, 95% CI = 1.37-2.74, 41.0% vs. 26.4%), and a lower frequency of the 1/1 genotype (OR = 0.55, 95% CI = 0.40-0.77, 43.9% vs. 58.5%). Frequency of the IL-1RN 2/2 genotype did not differ between groups. No significant differences between athletes and controls were found for either -511 or +3954 IL-1B polymorphisms. However, the haplotype (-511)C-(+3954)T-(VNTR)2 was 3-fold more frequent in athletes than in non-athletes (OR = 3.02, 95% CI = 1.16-7.87). Interestingly, the IL-1RN 1/2 genotype was more frequent in professional than in non-professional athletes (OR = 1.92, 95% CI = 1.02-3.61, 52.8% vs. 36.8%)., Conclusions: Our study found that variants at the IL-1ra gene associate with athletic status. This confirms the crucial role that cytokine IL-1ra plays in human physical exercise. The VNTR IL-1RN polymorphism may have implications for muscle health, performance, and/or recovery capacities. Further studies are needed to assess these specific issues. As VNTR IL-1RN polymorphism is implicated in several disease conditions, athlete status may constitute a confounding variable that will need to be accounted for when examining associations of this polymorphism with disease risk.

Objective: To study pathophysiologic pathways in spontaneous preterm birth and possibly the racial disparity associating with maternal and fetal genetic variations, using bioinformatics tools., Methods: A large scale candidate gene association study was performed on 1442 SNPs in 130 genes in a case (preterm birth < 36 weeks) control study (term birth > 37 weeks). Both maternal and fetal DNA from Caucasians (172 cases and 198 controls) and 279 African-Americans (82 cases and 197 controls) were used. A single locus association (genotypic) analysis followed by hierarchical clustering was performed, where clustering was based on p values for significant associations within each race. Using Ingenuity Pathway Analysis (IPA) software, known pathophysiologic pathways in both races were determined., Results: From all SNPs entered into the analysis, the IPA mapped genes to specific disease functions. Gene variants in Caucasians were implicated in disease functions shared with other known disorders; specifically, dermatopathy, inflammation, and hematological disorders. This may reflect abnormal cervical ripening and decidual hemorrhage. In African-Americans inflammatory pathways were the most prevalent. In Caucasians, maternal gene variants showed the most prominent role in disease functions, whereas in African Americans it was fetal variants. The IPA software was used to generate molecular interaction maps that differed between races and also between maternal and fetal genetic variants., Conclusion: Differences at the genetic level revealed distinct disease functions and operational pathways in African Americans and Caucasians in spontaneous preterm birth. Differences in maternal and fetal contributions in pregnancy outcome are also different between African Americans and Caucasians. These results present a set of explicit testable hypotheses regarding genetic associations with preterm birth in African Americans and Caucasians.