Blacks in comparison with whites are at risk for a more serious form of hypertension with high rates of complications. Greater sodium retention is thought to underlie the blood pressure (BP)-determining physiology of blacks, but specific mechanisms have not been identified. In a prospective observational study of BP, 226 black children and 314 white children (mean age, 10.6 years) were enrolled initially. Assessments were repeated in 85 blacks and 136 whites after reaching adulthood (mean age, 31 years). The relationship of BP to plasma aldosterone concentration in the context of the prevailing level of plasma renin activity was studied in blacks and whites. In a secondary interventional study, 9-? fludrocortisone was administered for 2 weeks to healthy adult blacks and whites to simulate hyperaldosteronism. BP responses in the 2 race groups were then compared. Although black children had lower levels of plasma renin activity and plasma aldosterone, their BP was positively associated with the plasma aldosterone concentration, an effect that increased as plasma renin activity decreased (P=0.004). Data from black adults yielded similar results. No similar relationship was observed in whites. In the interventional study, 9-? fludrocortisone increased BP in blacks but not in whites. In conclusion, aldosterone sensitivity is a significant determinant of BP in young blacks. Although its role in establishing the risk of hypertension is not known, it could be as relevant as the actual level of aldosterone.
We previously demonstrated that maternal and fetal genotypes are associated independently with neonatal respiratory distress syndrome. The objective of the current study was to determine the impact of maternal and fetal single-nucleotide polymorphisms (SNPs) in key betamethasone pathways on respiratory outcomes that serve as markers for severity of disease.
Recent studies suggest that many proteins or regions of proteins lack 3D structure. Defined as intrinsically disordered proteins, these proteins/peptides are functionally important. Recent advances in next generation sequencing technologies enable genome-wide identification of novel nucleotide variations in a specific population or cohort.
Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors.
Identifying rare variants that are responsible for complex disease has been promoted by advances in sequencing technologies. However, statistical methods that can handle the vast amount of data generated and that can interpret the complicated relationship between disease and these variants have lagged. We apply a zero-inflated Poisson regression model to take into account the excess of zeros caused by the extremely low frequency of the 24,487 exonic variants in the Genetic Analysis Workshop 17 data. We grouped the 697 subjects in the data set as Europeans, Asians, and Africans based on principal components analysis and found the total number of rare variants per gene for each individual. We then analyzed these collapsed variants based on the assumption that rare variants are enriched in a group of people affected by a disease compared to a group of unaffected people. We also tested the hypothesis with quantitative traits Q1, Q2, and Q4. Analyses performed on the combined 697 individuals and on each ethnic group yielded different results. For the combined population analysis, we found that UGT1A1, which was not part of the simulation model, was associated with disease liability and that FLT1, which was a causal locus in the simulation model, was associated with Q1. Of the causal loci in the simulation models, FLT1 and KDR were associated with Q1 and VNN1 was correlated with Q2. No significant genes were associated with Q4. These results show the feasibility and capability of our new statistical model to detect multiple rare variants influencing disease risk.
Recent evidence suggests that many complex diseases are caused by genetic variations that play regulatory roles in controlling gene expression. Most genetic studies focus on nonsynonymous variations that can alter the amino acid composition of a protein and are therefore believed to have the highest impact on phenotype. Synonymous variations, however, can also play important roles in disease pathogenesis by regulating pre-mRNA processing and translational control. In this study, we systematically survey the effects of single-nucleotide variations (SNVs) on binding affinity of RNA-binding proteins (RBPs). Among the 10,113 synonymous SNVs identified in 697 individuals in the 1,000 Genomes Project and distributed by Genetic Analysis Workshop 17 (GAW17), we identified 182 variations located in alternatively spliced exons that can significantly change the binding affinity of nine RBPs whose binding preferences on 7-mer RNA sequences were previously reported. We found that the minor allele frequencies of these variations are similar to those of nonsynonymous SNVs, suggesting that they are in fact functional. We propose a workflow to identify phenotype-associated regulatory SNVs that might affect alternative splicing from exome-sequencing-derived genetic variations. Based on the affecting SNVs on the quantitative traits simulated in GAW17, we further identified two and four functional SNVs that are predicted to be involved in alternative splicing regulation in traits Q1 and Q2, respectively.
In children, blood pressure (BP) and risk for hypertension are proportional to degree of adiposity. Whether the relationship to BP is similar over the full range of adiposity is less clear. Subjects from a cohort study (n=1111; 50% male and 42% black) contributed 9102 semiannual BP and height/weight assessments. The mean enrollment age was 10.2 years, and mean follow-up was 4.5 years. Adiposity was expressed as body mass index percentile, which accounted for effects of age and sex. The following observations were made. The effect of relative adiposity on BP was minimal until the body mass index percentile reached 85, beginning of the overweight category, at which point the effect of adiposity on BP increased by 4-fold. Similarly intensified adiposity effects on BP were observed in children aged ?10, 11 to 14 years, and ?15 years. Serum levels of the adipose tissue-derived hormone, leptin, together with heart rate, showed an almost identically patterned relation to BP to that of body mass index percentile and BP, thus implicating a possible mediating role for leptin. In conclusion, there is a marked intensification of the influence of adiposity on BP when children reach the categories of overweight and obese. Among the possible pathways, leptin may be a potentially important mediator acting through the sympathetic nervous system (reflected in heart rate). The findings have relevance to interventions designed to prevent or treat adiposity-related increases in BP and to the analytic approaches used in epidemiological studies.
Hereditary transthyretin (TTR) amyloidosis is an adult-onset disease characterized mainly by peripheral neuropathy and cardiomyopathy. Although disease progression is usually 5 to 15 years from time of diagnosis to death, no specific measurements of disease progression have been identified. The present study was designed to identify objective parameters to measure progression of hereditary TTR amyloidosis and determine if these parameters would show significant change within 1 year. Nine patients with biopsy-proved TTR amyloidosis and evidence of cardiac involvement were studied at baseline, 6 months, and 12 months by cardiac magnetic resonance imaging (MRI), electrocardiogram, and echocardiogram. Neurologic impairment score and electromyogram were determined at baseline and 12 months. Left ventricular mass determined by MRI and echocardiogram showed significant change at 12-month examination (p = 0.005 and p = 0.0009, respectively). Electrocardiogram and neurologic impairment score did not show significant change at 12 months. Measurement of left ventricular mass by MRI and echocardiographic techniques showed significant change in hereditary TTR cardiac amyloidosis within 1 year. In conclusion, these methods provide a means to clinically monitor progression of hereditary TTR amyloidosis and determine efficacy of therapeutic interventions.
The detection of gene-gene interaction is an important approach to understand the etiology of rheumatoid arthritis (RA). The goal of this study is to identify gene-gene interaction of SNPs at the allelic level contributing to RA using real data sets (Problem 1) of North American Rheumatoid Arthritis Consortium (NARAC) provided by Genetic Analysis Workshop 16 (GAW16). We applied our novel method that can detect the interaction by a definition of nonrandom association of alleles that occurs when the contribution to RA of a particular allele inherited in one gene depends on a particular allele inherited at other unlinked genes. Starting with 639 single-nucleotide polymorphisms (SNPs) from 26 candidate genes, we identified ten two-way interacting genes and one case of three-way interacting genes. SNP rs2476601 on PTPN22 interacts with rs2306772 on SLC22A4, which interacts with rs881372 on TRAF1 and rs2900180 on C5, respectively. SNP rs2900180 on C5 interacts with rs2242720 on RUNX1, which interacts with rs881375 on TRAF1. Furthermore, rs2476601 on PTPN22 also interacts with three SNPs (rs2905325, rs1476482, and rs2106549) in linkage disequilibrium (LD) on IL6. The other three SNPs (rs2961280, rs2961283, and rs2905308) in LD on IL6 interact with two SNPs (rs477515 and rs2516049) on HLA-DRB1. SNPs rs660895 and rs532098 on HLA-DRB1 interact with rs2834779 and four SNPs in LD on RUNX1. Three-way interacting genes of rs10229203 on IL6, rs4816502 on RUNX1, and rs10818500 on C5 were also detected.
Interest is increasing in epistasis as a possible source of the unexplained variance missed by genome-wide association studies. The Genetic Analysis Workshop 16 Group 9 participants evaluated a wide variety of classical and novel analytical methods for detecting epistasis, in both the statistical and machine learning paradigms, applied to both real and simulated data. Because the magnitude of epistasis is clearly relative to scale of penetrance, and therefore to some extent, to the choice of model framework, it is not surprising that strong interactions under one model might be minimized or even disappear entirely under a different modeling framework.
Recent studies have shown that quantitative phenotypes may be influenced not only by multiple single nucleotide polymorphisms (SNPs) within a gene but also by the interaction between SNPs at unlinked genes. We propose a new statistical approach that can detect gene-gene interactions at the allelic level which contribute to the phenotypic variation in a quantitative trait. By testing for the association of allelic combinations at multiple unlinked loci with a quantitative trait, we can detect the SNP allelic interaction whether or not it can be detected as a main effect. Our proposed method assigns a score to unrelated subjects according to their allelic combination inferred from observed genotypes at two or more unlinked SNPs, and then tests for the association of the allelic score with a quantitative trait. To investigate the statistical properties of the proposed method, we performed a simulation study to estimate type I error rates and power and demonstrated that this allelic approach achieves greater power than the more commonly used genotypic approach to test for gene-gene interaction. As an example, the proposed method was applied to data obtained as part of a candidate gene study of sodium retention by the kidney. We found that this method detects an interaction between the calcium-sensing receptor gene (CaSR), the chloride channel gene (CLCNKB) and the Na, K, 2Cl cotransporter gene (CLC12A1) that contributes to variation in diastolic blood pressure.
Otitis media (OM) is a common worldwide pediatric health care problem that is known to be influenced by genetics. The objective of our study was to use linkage analysis to map possible OM susceptibility genes.
In case-control studies identifying disease susceptibility loci, it has been shown that the interaction caused by multiple single nucleotide polymorphisms (SNPs) within a gene as well as by SNPs at unlinked genes plays an important role in influencing risk of a disease. A novel statistical approach is proposed to detect gene-gene interactions at the allelic level contributing to a disease trait. With a new allelic score inferred from the observed genotypes at two or more unlinked SNPs, we derive a score test from logistic regression and test for association of the allelic scores with a disease trait. Furthermore, F and likelihood ratio tests are derived from Cochran-Armitage regression. By testing for the association, the interaction can be assessed both in cases where the SNP association can be detected and cannot be detected as a main effect in single SNP approach. The analytical power and type I error rates over 6 two-way interaction models are investigated based on the non-centrality parameter approximation of the score test. Simulation studies demonstrate that (1) the power of the score test is asymptotically equivalent to that of the test statistics by the Cochran-Armitage method and (2) the allelic based method provides higher power than two genotypic based methods.
Calcium binding to the Ca-sensing receptor (CASR) expressed in thick ascending limb inhibits the Na,K,2Cl cotransporter, which decreases sodium reabsorption and secondarily decreases Ca reabsorption. CASR gene variants could influence blood pressure (BP) by affecting Na retention.
To determine the impact of maternal and fetal single nucleotide polymorphisms in key betamethasone pathways on neonatal outcomes.
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