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BackgroundGenome-wide association studies of obesity have typically assumed fixed genetic effects across ethnicities, rarely attempting to thoroughly compare and contrast findings across various ethnic groups. Therefore, our study aimed to identify novel genetic associations with body mass index (BMI), a common measure of obesity, and explore their cross-ethnic generalizability in a multiethnic population. To that end, we conducted ​ethnic-specific genome-wide association analyses among 1235 Hispanic, 706 Asian, 1549 African American, and 2395 European American subjects from the Multi-ethnic Study of Atherosclerosis (MESA). We compared findings ​across ethnicities and investigated single-nucleotide polymorphisms (SNPs) with suggestive BMI-association p-values among 3379 Hispanic and 6871 African American subjects from the Women’s Health Initiative (WHI).ResultsWe identified a genome-wide significant association in MESA Hispanics—rs12253976 in KLF6 (beta = 5.792 kg/m2 per-allele, 95 % confidence interval (CI): 3.885, 7.698; p = 3.43 × 10−9)—and suggestive SNPs with p < 5 × 10−6 in MESA Hispanics, European Americans and African Americans that display ethnic-specific effects on BMI. Of these suggestive SNPs, Hispanic SNP rs12255372 and African American SNP rs6435678 had the most evidence of replication in WHI. rs12255372 (in TCF7L2) was associated with lower BMI in both MESA (beta = −1.111 kg/m2, 95 % CI: −1.578, −0.645; p = 3.33 × 10−6) and WHI Hispanics (beta = −0.304 kg/m2, 95 % CI: −0.613, 0.006; p = 0.054). This TCF7L2 intronic region contains several SNPs (rs7901695, rs4506565, rs4132670, and rs12243326) with low p-values (p < 10−3) in MESA and betas of similar magnitude and direction in MESA and WHI, but only rs12243326 is in strong linkage disequilibrium with rs12255372 in our Hispanic populations, suggesting independent signals in this region. rs6435678 (in ERBB4) was associated with greater BMI in both MESA (beta = 1.104 kg/m2, 95 % CI: 0.643, 1.564; p = 2.85 × 10−6) and WHI African Americans (beta = 0.219 kg/m2, 95 % CI: −0.021, 0.460; p = 0.074).ConclusionsTwo BMI-association signals are present in the TCF7L2 intronic region of Hispanics, one of which is tagged by rs12255372. ERBB4 rs6435678 is a novel BMI-association signal in African Americans. Overall, our data suggest that ethnic-specific associations are involved in the genetic determination of BMI. Ethnic-specificity has potential implications for the development of gene-based therapies for obesity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-016-0387-0) contains supplementary material, which is available to authorized users.

The magnocellular neurons (MCNs) in the supraoptic nucleus (SON) of the hypothalamus selectively express either oxytocin (Oxt) or vasopressin (Avp) neuropeptide genes. In this paper we examine the cis-regulatory domains in the Avp gene promoter that are responsible for its cell-type specific expression. AAV vectors that contain various Avp gene promoter deletion constructs using EGFP as the reporter were stereotaxically injected into the rat SON. Two weeks following the injection immunohistochemical assays of EGFP expression from these constructs were done to determine whether the expressed EGFP reporter co-localizes with either the Oxt- or Avp-immunoreactivity in the MCNs. The results identify three major enhancer domains located at −2.0 to −1.5 kbp, −1.5 to −950 bp, and −950 to −543 bp in the Avp gene promoter that regulate the expression in Avp MCNs. The results also show that cell–type specific expression in Avp MCNs is maintained in constructs containing at least 288 bp of the promoter region upstream of the transcription start site, but this specificity is lost at 116 bp and below. Based on these data, we hypothesize that the −288 bp to −116 bp domain contains an Avp MCN specific activator and a possible repressor that inhibits expression in Oxt-MCNs, thereby leading to the cell-type specific expression of the Avp gene only in the Avp-MCNs.

Magnocellular neurons (MCNs) in the hypothalamo-neurohypophysial system (HNS) are highly specialized to release large amounts of arginine vasopressin (Avp) or oxytocin (Oxt) into the blood stream and play critical roles in the regulation of body fluid homeostasis. The MCNs are osmosensory neurons and are excited by exposure to hypertonic solutions and inhibited by hypotonic solutions. The MCNs respond to systemic hypertonic and hypotonic stimulation with large changes in the expression of their Avp and Oxt genes, and microarray studies have shown that these osmotic perturbations also cause large changes in global gene expression in the HNS. In this paper, we examine gene expression in the rat supraoptic nucleus (SON) under normosmotic and chronic salt-loading SL) conditions by the first time using “new-generation”, RNA sequencing (RNA-Seq) methods. We reliably detect 9,709 genes as present in the SON by RNA-Seq, and 552 of these genes were changed in expression as a result of chronic SL. These genes reflect diverse functions, and 42 of these are involved in either transcriptional or translational processes. In addition, we compare the SON transcriptomes resolved by RNA-Seq methods with the SON transcriptomes determined by Affymetrix microarray methods in rats under the same osmotic conditions, and find that there are 6,466 genes present in the SON that are represented in both data sets, although 1,040 of the expressed genes were found only in the microarray data, and 2,762 of the expressed genes are selectively found in the RNA-Seq data and not the microarray data. These data provide the research community a comprehensive view of the transcriptome in the SON under normosmotic conditions and the changes in specific gene expression evoked by salt loading.

Background Preterm birth (PTB) disproportionately affects African American compared with Caucasian women, although reasons for this disparity remain unclear. Some suggest that a differential effect of maternal age by race/ethnicity, especially at older maternal ages, may explain disparities. Objective To determine whether the relationship between maternal age and preterm birth varies by race/ethnicity among primiparae non‐Hispanic blacks (NHB) and non‐Hispanic whites (NHW). Methods A cross‐sectional study of 367 081 singleton liveborn first births to NHB and NHW women in California from 2008 to 2012 was conducted. Rate ratios (RR) were estimated for PTB and its subtypes—spontaneous and clinician‐initiated—after adjusting for confounders through Poisson regression. Universal age/race reference groups (NHW, 25‐29 years) and race‐specific reference groups (NHW or NHB, 25‐29 years) were used for comparisons. Results Among all women, RR of PTB was highest at the extremes of age (<15 and ≥40 years). Among NHBs, the risk of PTB was higher than among NHWs at all maternal ages (adjusted RR of PTB 1.38‐2.93 vs 0.98‐2.38). However, using race‐specific reference groups, the risk of PTB for NHB women (RR 0.91‐1.88) vs NHW women (RR 0.98‐2.39) was nearly identical at all maternal ages, with overlapping confidence intervals. Analyses did not demonstrate substantial divergence of risk with advancing maternal age. PTB, spontaneous PTB, and clinician‐initiated PTB demonstrated similar risk patterns at younger but not older maternal ages, where risk of clinician‐initiated PTB increased sharply for all women. Conclusions Primiparae NHBs demonstrated increased risk of PTB, spontaneous PTB, and clinician‐initiated PTB compared with NHWs at all maternal ages. However, RRs using race‐specific reference groups converged across maternal ages, indicating a similar independent effect of maternal age on PTB by race/ethnicity. A differential effect of maternal age does not appear to explain disparities in preterm birth by race/ethnicity.

In the context of genetics, pleiotropy refers to the phenomenon in which a single genetic locus affects more than 1 trait or disease. Genetic epidemiologic studies have identified loci associated with multiple phenotypes, and these cross-phenotype associations are often incorrectly interpreted as examples of pleiotropy. Pleiotropy is only one possible explanation for cross-phenotype associations. Cross-phenotype associations may also arise due to issues related to study design, confounder bias, or nongenetic causal links between the phenotypes under analysis. Therefore, it is necessary to dissect cross-phenotype associations carefully to uncover true pleiotropic loci. In this review, we describe statistical methods that can be used to identify robust statistical evidence of pleiotropy. First, we provide an overview of univariate and multivariate methods for discovery of cross-phenotype associations and highlight important considerations for choosing among available methods. Then, we describe how to dissect cross-phenotype associations by using mediation analysis. Pleiotropic loci provide insights into the mechanistic underpinnings of disease comorbidity, and they may serve as novel targets for interventions that simultaneously treat multiple diseases. Discerning between different types of cross-phenotype associations is necessary to realize the public health potential of pleiotropic loci.

The oxytocin (Oxt) and vasopressin (Avp) magnocellular neurons (MCNs) in the hypothalamus are the only neuronal phenotypes that are present in the supraoptic nucleus (SON), and are characterized by their robust and selective expression of either the Oxt or Avp genes. In this paper, we take advantage of the differential expression of these neuropeptide genes to identify and isolate these two individual phenotypes from the rat SON by laser capture microdissection (LCM), and to analyze the differential expression of several of their transcription factor mRNAs by qRT-PCR. We identify these neuronal phenotypes by stereotaxically injecting recombinant Adeno-Associated Viral (rAAV) vectors which contain cell-type specific Oxt or Avp promoters that drive expression of EGFP selectively in either the Oxt or Avp MCNs into the SON. The fluorescent MCNs are then dissected by LCM using a novel Cap Road Map protocol described in this paper, and the purified MCNs are extracted for their RNAs. qRT-PCR of these RNAs show that some transcription factors (RORA and c-jun) are differentially expressed in the Oxt and Avp MCNs.

There are a number of errors in the captions for S1 to S9 Fig Step 1, "Sequence Adaptor Clipping". Read pairs, 101bp in length, were adaptor clipped via FASTQ/A Clipper (http://hannonlab.cshl.edu). Broken read pairs as a result of clipping were discarded.Step 2, "Sequence Quality Inspection". Intact read pairs remaining post-clipping were import into CLCbio and the "Create Sequencing QC Report" tool used to generate one report per sample. These reports, each containing per-sequence and per-base quality statistics, were individually inspected and cross-compared to define universal hard-trimming rules to be applied to all samples.Step 3, "Sequence Quality Trimming & Filtering". The CLCbio "Trim Sequences" tool was used to hard-remove the first 15nt from the 5' end and the last 1nt from the 3' end of each read pair. The tool was also used to dynamically-trim away nucleotides having a call accuracy rate less than 95%. Read pairs having at least one sequence containing more than two ambiguities were also discarded as part of this Step as were read pairs having at least one sequence with a post trimmed length less than 15 nucleotides.Step 4, "Sequence Alignment and Enumeration". The CLCbio "RNA-Seq Analysis" tool was used to align read pairs to the Rat Genome (RN5) by sample using default parameters. Output provided by the tool included a Reads per kilo base per million (RPKM) expression value for 26,313 genes.Step 5, "RPKM Expression Pedelstalling". Output from Step 4 was imported into R (http://www.r-project.org/) and a value of two added to each RPKM expression value per sample. Step 6, "RPKM Expression Transformation". Pedestalled values from Step 5 (RPKM+2) were Log2 transformed using standard commands in R then filtered to keep only those genes having a post-transformed expression value (Log2(RPKM+2)) >1 for at least one sample.Step 7, "RPKM Expression Normalization". Transformed values from Step 5 were quantile normalized using standard commands in R.Step 8, "Exploratory Analysis". Normalized values from Step 7 (Quantile(Log2 (RPKM+2))) were interrogated in R by Tukey box plot, covariance-based principal component analysis (PCA) scatter plot and Pearson correlation-based heat map to confirm absence of outliers.Step 9, "Noise Analysis". For each gene, the coefficient of variation (CV) and mean expression was calculated by sample class using standard commands in R then modeled by sample class using the lowess() command.Step 10, "Confidence Criterion Selection". Lowess fits from Step 9 were visually inspected to define the mean expression value across sample PLOS ONE |