Applied Plasma Research

Contains reports on two research projects.National Science Foundation (Grant GK-37979X)U. S. Army - Research Office - Durham (Contract DAHC04-72-C-0044

Applied Plasma Research

Contains reports on two research projects.National Science Foundation (Grant GK-28282X1)National Science Foundation (Grant GK-33843)U. S. Army - Research Office - Durham (Contract DAHC04-72-C-0044

Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism

A comprehensive genome-scale metabolic network of Chlamydomonas reinhardtii, including a detailed account of light-driven metabolism, is reconstructed and validated. The model provides a new resource for research of C. reinhardtii metabolism and in algal biotechnology

Population Structure of Hispanics in the United States: The Multi-Ethnic Study of Atherosclerosis

Using ∼60,000 SNPs selected for minimal linkage disequilibrium, we perform population structure analysis of 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By projection of principal components (PCs) of ancestry to samples from the HapMap phase III and the Human Genome Diversity Panel (HGDP), we show the first two PCs quantify the Caucasian, African, and Native American origins, while the third and fourth PCs bring out an axis that aligns with known South-to-North geographic location of HGDP Native American samples and further separates MESA Mexican versus Central/South American samples along the same axis. Using k-means clustering computed from the first four PCs, we define four subgroups of the MESA Hispanic cohort that show close agreement with self-identification, labeling the clusters as primarily Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. To demonstrate our recommendations for genetic analysis in the MESA Hispanic cohort, we present pooled and stratified association analysis of triglycerides for selected SNPs in the LPL and TRIB1 gene regions, previously reported in GWAS of triglycerides in Caucasians but as yet unconfirmed in Hispanic populations. We report statistically significant evidence for genetic association in both genes, and we further demonstrate the importance of considering population substructure and genetic heterogeneity in genetic association studies performed in the United States Hispanic population

Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways

Depression is a polygenic trait that causes extensive periods of disability. Previous genetic studies have identified common risk variants which have progressively increased in number with increasing sample sizes of the respective studies. Here, we conduct a genome-wide association study in 322,580 UK Biobank participants for three depression-related phenotypes: broad depression, probable major depressive disorder (MDD), and International Classification of Diseases (ICD, version 9 or 10)-coded MDD. We identify 17 independent loci that are significantly associated (P < 5 × 10−8) across the three phenotypes. The direction of effect of these loci is consistently replicated in an independent sample, with 14 loci likely representing novel findings. Gene sets are enriched in excitatory neurotransmission, mechanosensory behaviour, post synapse, neuron spine and dendrite functions. Our findings suggest that broad depression is the most tractable UK Biobank phenotype for discovering genes and gene sets that further our understanding of the biological pathways underlying depression

Discovery and fine-mapping of loci associated with MUFAs through trans-ethnic meta-analysis in Chinese and European populations

MUFAs are unsaturated FAs with one double bond and are derived from endogenous synthesis and dietary intake. Accumulating evidence has suggested that plasma and erythrocyte MUFA levels are associated with cardiometabolic disorders, including CVD, T2D, and metabolic syndrome (MS). Previous genome-wide association studies (GWASs) have identified seven loci for plasma and erythrocyte palmitoleic and oleic acid levels in populations of European origin. To identify additional MUFA-associated loci and the potential functional variant at each locus, we performed ethnic-specific GWAS meta-analyses and trans-ethnic meta-analyses in more than 15,000 participants of Chinese and European ancestry. We identified novel genome-wide significant associations for vaccenic acid at FADS1/2 and PKD2L1 [log(10)(Bayes factor). >= 8.07] and for gondoic acid at FADS1/2 and GCKR [log(10)(Bayes factor) >= 6.22], and also observed improved fine-mapping resolutions at FADS1/2 and GCKR loci. The greatest improvement was observed at GCKR, where the number of variants in the 99\% credible set was reduced from 16 (covering 94.8 kb) to 5 (covering 19.6 kb, including a missense variant rs1260326) after trans-ethnic meta-analysis. We also confirmed the previously reported associations of PKD2L1, FADS1/2, GCKR, and HIF1AN with palmitoleic acid and of FADS1/2 and LPCAT3 with oleic acid in the Chinese-specific GWAS and the trans-ethnic meta-analyses. Pathway-based analyses suggested that the identified loci were in unsaturated FA metabolism and signaling pathways.(jl) Our findings provide novel insight into the genetic basis relevant to MUFA metabolism and biology.Infrastructure for the CHARGE Consortium was supported in part by the National Heart, Lung, and Blood Institute grant HL105756. The NHAPC study was supported by the major project of the Ministry of Science and Technology of China (2016YFC1304903) and the National Natural Science Foundation of China (81471013, 30930081, 81170734, and 81321062). The ARIC Study was carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C and grants R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. Infrastructure was partly supported by grant UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. The CARDIA study was conducted and supported by the National Heart, Lung, and Blood Institute in collaboration with the University of Alabama at Birmingham (HHSN268201300025C and HHSN268201300026C), Northwestern University (HHSN268201300027C), University of Minnesota (HHSN268201300028C), Kaiser Foundation Research Institute (HHSN268201300029C), and Johns Hopkins University School of Medicine (HHSN268200900041C). CARDIA is also partially supported by the Intramural Research Program of the National Institute on Aging. Genotyping of the CARDIA participants was supported by National Human Genome Research Institute grants U01-HG-004729, U01-HG-004446, and U01-HG-004424. Statistical analyses and FA measures were funded by National Heart, Lung, and Blood Institute grant R01-HL-084099 (M.F.). The CHS was supported by National Heart, Lung, and Blood Institute contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, and N01HC85086; and National Heart, Lung, and Blood Institute grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and R01HL085710, with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided through National Institute on Aging grant R01AG023629. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences CTSI grant UL1TR000124 and the National Institute of Diabetes and Digestive and Kidney Diseases Diabetes Research Center grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The HPFS and NHS were supported by National Institutes of Health research grants UM1 CA186107, R01 HL034594, UM1 CA167552, R01 HL35464, HL60712, and CA055075; National Heart, Lung, and Blood Institute career development award R00HL098459; American Diabetes Association research grant 1-12-JF-13; and American Heart Association grant 11SDG7380016. The MESA study and MESA SHARe were supported by National Heart, Lung, and Blood Institute contracts N01-HC-95159 through N01-HC-95169 and RR-024156. Funding for MESA SHARe genotyping was provided by National Heart, Lung, and Blood Institute contract N02HL64278. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences CTSI grant UL1TR000124 and the National Institute of Diabetes and Digestive and Kidney Diseases Diabetes Research Center grant DK063491 (Southern California Diabetes Endocrinology Research Center).; The GOLDN study was funded by National Heart, Lung, and Blood Institute grants U01HL072524 and HL54776. The InCHIANTI baseline (1998-2000) was supported as a ``targeted project (ICS110.1/RF97.71) by the Italian Ministry of Health and in part by National Institute on Aging contracts 263 MD 9164 and 263 MD 821336. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.S

A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules

Studies of the relationship between DNA variation and gene expression variation, often referred to as “expression quantitative trait loci (eQTL) mapping”, have been conducted in many species and resulted in many significant findings. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to facilitate the task, in which co-expressed genes mapped to a common set of markers are treated as a module characterized by latent indicator variables. A Markov chain Monte Carlo algorithm is designed to search simultaneously for the module genes and their linked markers. We show by simulations that this method is more powerful for detecting true eQTLs and their target genes than traditional QTL mapping methods. We applied the procedure to a data set consisting of gene expression and genotypes for 112 segregants of S. cerevisiae. Our method identified modules containing genes mapped to previously reported eQTL hot spots, and dissected these large eQTL hot spots into several modules corresponding to possibly different biological functions or primary and secondary responses to regulatory perturbations. In addition, we identified nine modules associated with pairs of eQTLs, of which two have been previously reported. We demonstrated that one of the novel modules containing many daughter-cell expressed genes is regulated by AMN1 and BPH1. In conclusion, the Bayesian partition method which simultaneously considers all traits and all markers is more powerful for detecting both pleiotropic and epistatic effects based on both simulated and empirical data

Genome-wide meta-analyses of stratified depression in Generation Scotland and UK Biobank

Abstract Few replicable genetic associations for Major Depressive Disorder (MDD) have been identified. Recent studies of MDD have identified common risk variants by using a broader phenotype definition in very large samples, or by reducing phenotypic and ancestral heterogeneity. We sought to ascertain whether it is more informative to maximize the sample size using data from all available cases and controls, or to use a sex or recurrent stratified subset of affected individuals. To test this, we compared heritability estimates, genetic correlation with other traits, variance explained by MDD polygenic score, and variants identified by genome-wide meta-analysis for broad and narrow MDD classifications in two large British cohorts - Generation Scotland and UK Biobank. Genome-wide meta-analysis of MDD in males yielded one genome-wide significant locus on 3p22.3, with three genes in this region (CRTAP, GLB1, and TMPPE) demonstrating a significant association in gene-based tests. Meta-analyzed MDD, recurrent MDD and female MDD yielded equivalent heritability estimates, showed no detectable difference in association with polygenic scores, and were each genetically correlated with six health-correlated traits (neuroticism, depressive symptoms, subjective well-being, MDD, a cross-disorder phenotype and Bipolar Disorder). Whilst stratified GWAS analysis revealed a genome-wide significant locus for male MDD, the lack of independent replication, and the consistent pattern of results in other MDD classifications suggests that phenotypic stratification using recurrence or sex in currently available sample sizes is currently weakly justified. Based upon existing studies and our findings, the strategy of maximizing sample sizes is likely to provide the greater gain

Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals

Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93–0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies

MYT1L mutations cause intellectual disability and variable obesity by dysregulating gene expression and development of the neuroendocrine hypothalamus

Deletions at chromosome 2p25.3 are associated with a syndrome consisting of intellectual disability and obesity. The smallest region of overlap for deletions at 2p25.3 contains PXDN and MYT1L. MYT1L is expressed only within the brain in humans. We hypothesized that single nucleotide variants (SNVs) in MYT1L would cause a phenotype resembling deletion at 2p25.3. To examine this we sought MYT1L SNVs in exome sequencing data from 4, 296 parent-child trios. Further variants were identified through a genematcher-facilitated collaboration. We report 9 patients with MYT1L SNVs (4 loss of function and 5 missense). The phenotype of SNV carriers overlapped with that of 2p25.3 deletion carriers. To identify the transcriptomic consequences of MYT1L loss of function we used CRISPR-Cas9 to create a knockout cell line. Gene Ontology analysis in knockout cells demonstrated altered expression of genes that regulate gene expression and that are localized to the nucleus. These differentially expressed genes were enriched for OMIM disease ontology terms “mental retardation”. To study the developmental effects of MYT1L loss of function we created a zebrafish knockdown using morpholinos. Knockdown zebrafish manifested loss of oxytocin expression in the preoptic neuroendocrine area. This study demonstrates that MYT1L variants are associated with syndromic obesity in humans. The mechanism is related to dysregulated expression of neurodevelopmental genes and altered development of the neuroendocrine hypothalamus