Genome-wide identification of pathogenicity factors of the free-living amoeba Naegleria fowleri.

BACKGROUND The free-living amoeba Naegleria fowleri is the causative agent of the rapidly progressing and typically fatal primary amoebic meningoencephalitis (PAM) in humans. Despite the devastating nature of this disease, which results in > 97% mortality, knowledge of the pathogenic mechanisms of the amoeba is incomplete. This work presents a comparative proteomic approach based on an experimental model in which the pathogenic potential of N. fowleri trophozoites is influenced by the compositions of different media. RESULTS As a scaffold for proteomic analysis, we sequenced the genome and transcriptome of N. fowleri. Since the sequence similarity of the recently published genome of Naegleria gruberi was far lower than the close taxonomic relationship of these species would suggest, a de novo sequencing approach was chosen. After excluding cell regulatory mechanisms originating from different media compositions, we identified 22 proteins with a potential role in the pathogenesis of PAM. Functional annotation of these proteins revealed, that the membrane is the major location where the amoeba exerts its pathogenic potential, possibly involving actin-dependent processes such as intracellular trafficking via vesicles. CONCLUSION This study describes for the first time the 30 Mb-genome and the transcriptome sequence of N. fowleri and provides the basis for the further definition of effective intervention strategies against the rare but highly fatal form of amoebic meningoencephalitis.

Evaluation of genome-wide loci of iron metabolism in hereditary hemochromatosis identifies PCSK7 as a host risk factor of liver cirrhosis

Genome-wide association studies (GWAS) have revealed genetic determinants of iron metabolism, but correlation of these with clinical phenotypes is pending. Homozygosity for HFE C282Y is the predominant genetic risk factor for hereditary hemochromatosis (HH) and may cause liver cirrhosis. However, this genotype has a low penetrance. Thus, detection of yet unknown genetic markers that identify patients at risk of developing severe liver disease is necessary for better prevention. Genetic loci associated with iron metabolism (TF, TMPRSS6, PCSK7, TFR2 and Chr2p14) in recent GWAS and liver fibrosis (PNPLA3) in recent meta-analysis were analyzed for association with either liver cirrhosis or advanced fibrosis in 148 German HFE C282Y homozygotes. Replication of associations was sought in additional 499 Austrian/Swiss and 112 HFE C282Y homozygotes from Sweden. Only variant rs236918 in the PCSK7 gene (proprotein convertase subtilisin/kexin type 7) was associated with cirrhosis or advanced fibrosis (P = 1.02 × 10(-5)) in the German cohort with genotypic odds ratios of 3.56 (95% CI 1.29-9.77) for CG heterozygotes and 5.38 (95% CI 2.39-12.10) for C allele carriers. Association between rs236918 and cirrhosis was confirmed in Austrian/Swiss HFE C282Y homozygotes (P = 0.014; ORallelic = 1.82 (95% CI 1.12-2.95) but not in Swedish patients. Post hoc combined analyses of German/Swiss/Austrian patients with available liver histology (N = 244, P = 0.00014, ORallelic = 2.84) and of males only (N = 431, P = 2.17 × 10(-5), ORallelic = 2.54) were consistent with the premier finding. Association between rs236918 and cirrhosis was not confirmed in alcoholic cirrhotics, suggesting specificity of this genetic risk factor for HH. PCSK7 variant rs236918 is a risk factor for cirrhosis in HH patients homozygous for the HFE C282Y mutation.

A Genome-Wide Association Study Reveals Loci Influencing Height and Other Conformation Traits in Horses

The molecular analysis of genes influencing human height has been notoriously difficult. Genome-wide association studies (GWAS) for height in humans based on tens of thousands to hundreds of thousands of samples so far revealed ∼200 loci for human height explaining only 20% of the heritability. In domestic animals isolated populations with a greatly reduced genetic heterogeneity facilitate a more efficient analysis of complex traits. We performed a genome-wide association study on 1,077 Franches-Montagnes (FM) horses using ∼40,000 SNPs. Our study revealed two QTL for height at withers on chromosomes 3 and 9. The association signal on chromosome 3 is close to the LCORL/NCAPG genes. The association signal on chromosome 9 is close to the ZFAT gene. Both loci have already been shown to influence height in humans. Interestingly, there are very large intergenic regions at the association signals. The two detected QTL together explain ∼18.2% of the heritable variation of height in horses. However, another large fraction of the variance for height in horses results from ECA 1 (11.0%), although the association analysis did not reveal significantly associated SNPs on this chromosome. The QTL region on ECA 3 associated with height at withers was also significantly associated with wither height, conformation of legs, ventral border of mandible, correctness of gaits, and expression of the head. The region on ECA 9 associated with height at withers was also associated with wither height, length of croup and length of back. In addition to these two QTL regions on ECA 3 and ECA 9 we detected another QTL on ECA 6 for correctness of gaits. Our study highlights the value of domestic animal populations for the genetic analysis of complex traits.

Genome-wide association study of survival from sepsis due to pneumonia: an observational cohort study.

BACKGROUND Sepsis continues to be a major cause of death, disability, and health-care expenditure worldwide. Despite evidence suggesting that host genetics can influence sepsis outcomes, no specific loci have yet been convincingly replicated. The aim of this study was to identify genetic variants that influence sepsis survival. METHODS We did a genome-wide association study in three independent cohorts of white adult patients admitted to intensive care units with sepsis, severe sepsis, or septic shock (as defined by the International Consensus Criteria) due to pneumonia or intra-abdominal infection (cohorts 1-3, n=2534 patients). The primary outcome was 28 day survival. Results for the cohort of patients with sepsis due to pneumonia were combined in a meta-analysis of 1553 patients from all three cohorts, of whom 359 died within 28 days of admission to the intensive-care unit. The most significantly associated single nucleotide polymorphisms (SNPs) were genotyped in a further 538 white patients with sepsis due to pneumonia (cohort 4), of whom 106 died. FINDINGS In the genome-wide meta-analysis of three independent pneumonia cohorts (cohorts 1-3), common variants in the FER gene were strongly associated with survival (p=9·7 × 10(-8)). Further genotyping of the top associated SNP (rs4957796) in the additional cohort (cohort 4) resulted in a combined p value of 5·6 × 10(-8) (odds ratio 0·56, 95% CI 0·45-0·69). In a time-to-event analysis, each allele reduced the mortality over 28 days by 44% (hazard ratio for death 0·56, 95% CI 0·45-0·69; likelihood ratio test p=3·4 × 10(-9), after adjustment for age and stratification by cohort). Mortality was 9·5% in patients carrying the CC genotype, 15·2% in those carrying the TC genotype, and 25·3% in those carrying the TT genotype. No significant genetic associations were identified when patients with sepsis due to pneumonia and intra-abdominal infection were combined. INTERPRETATION We have identified common variants in the FER gene that associate with a reduced risk of death from sepsis due to pneumonia. The FER gene and associated molecular pathways are potential novel targets for therapy or prevention and candidates for the development of biomarkers for risk stratification. FUNDING European Commission and the Wellcome Trust.

Genome-Wide Analyses Suggest Mechanisms Involving Early B-Cell Development in Canine IgA Deficiency.

Immunoglobulin A deficiency (IgAD) is the most common primary immune deficiency disorder in both humans and dogs, characterized by recurrent mucosal tract infections and a predisposition for allergic and other immune mediated diseases. In several dog breeds, low IgA levels have been observed at a high frequency and with a clinical resemblance to human IgAD. In this study, we used genome-wide association studies (GWAS) to identify genomic regions associated with low IgA levels in dogs as a comparative model for human IgAD. We used a novel percentile groups-approach to establish breed-specific cut-offs and to perform analyses in a close to continuous manner. GWAS performed in four breeds prone to low IgA levels (German shepherd, Golden retriever, Labrador retriever and Shar-Pei) identified 35 genomic loci suggestively associated (p <0.0005) to IgA levels. In German shepherd, three genomic regions (candidate genes include KIRREL3 and SERPINA9) were genome-wide significantly associated (p <0.0002) with IgA levels. A ~20kb long haplotype on CFA28, significantly associated (p = 0.0005) to IgA levels in Shar-Pei, was positioned within the first intron of the gene SLIT1. Both KIRREL3 and SLIT1 are highly expressed in the central nervous system and in bone marrow and are potentially important during B-cell development. SERPINA9 expression is restricted to B-cells and peaks at the time-point when B-cells proliferate into antibody-producing plasma cells. The suggestively associated regions were enriched for genes in Gene Ontology gene sets involving inflammation and early immune cell development.

Salivary Pellicle vs Whole Saliva: The Response of Oral Fibroblasts Based on a Genome-Wide Microarray.

PURPOSE Whole saliva comprises components of the salivary pellicle that spontaneously forms on surfaces of implants and teeth. However, there are no studies that functionally link the salivary pellicle with a possible change in gene expression. MATERIALS AND METHODS This study examined the genetic response of oral fibroblasts exposed to the salivary pellicle and whole saliva. Oral fibroblasts were seeded onto a salivary pellicle and the respective untreated surface. Oral fibroblasts were also exposed to freshly harvested sterile-filtered whole saliva. A genome-wide microarray of oral fibroblasts was performed, followed by gene ontology screening with DAVID functional annotation clustering, KEGG pathway analysis, and the STRING functional protein association network. RESULTS Exposure of oral fibroblasts to saliva caused 61 genes to be differentially expressed (P < .05). Gene ontology screening assigned the respective genes into 262 biologic processes, 3 cellular components, 13 molecular functions, and 7 pathways. Most remarkable was the enrichment in the inflammatory response. None of the genes regulated by whole saliva was significantly changed when cells were placed onto a salivary pellicle. CONCLUSION The salivary pellicle per se does not provoke a significant inflammatory response of oral fibroblasts in vitro, whereas sterile-filtered whole saliva does produce a strong inflammatory response.

Detection of convergent genome-wide signals of adaptation to tropical forests in humans

Tropical forests are believed to be very harsh environments for human life. It is unclear whether human beings would have ever subsisted in those environments without external resources. It is therefore possible that humans have developed recent biological adaptations in response to specific selective pressures to cope with this challenge. To understand such biological adaptations we analyzed genome-wide SNP data under a Bayesian statistics framework, looking for outlier markers with an overly large extent of differentiation between populations living in a tropical forest, as compared to genetically related populations living outside the forest in Africa and the Americas. The most significant positive selection signals were found in genes related to lipid metabolism, the immune system, body development, and RNA Polymerase III transcription initiation. The results are discussed in the light of putative tropical forest selective pressures, namely food scarcity, high prevalence of pathogens, difficulty to move, and inefficient thermoregulation. Agreement between our results and previous studies on the pygmy phenotype, a putative prototype of forest adaptation, were found, suggesting that a few genetic regions previously described as associated with short stature may be evolving under similar positive selection in Africa and the Americas. In general, convergent evolution was less pervasive than local adaptation in one single continent, suggesting that Africans and Amerindians may have followed different routes to adapt to similar environmental selective pressures.

Genome-wide association studies based on sequence-derived genotypes reveal new QTL associated with conformation and performance traits in the Franches-Montagnes horse breed

To identify novel quantitative trait loci (QTL) within horses, we performed genome-wide association studies (GWAS) based on sequence-level genotypes for conformation and performance traits in the Franches-Montagnes (FM) horse breed. Sequence-level genotypes of FM horses were derived by re-sequencing 30 key founders and imputing 50K data of genotyped horses. In total, we included 1077 FM horses genotyped for ~4 million SNPs and their respective de-regressed breeding values of the traits in the analysis. Based on this dataset, we identified a total of 14 QTL associated with 18 conformation traits and one performance trait. Therefore, our results suggest that the application of sequence-derived genotypes increases the power to identify novel QTL which were not identified previously based on 50K SNP chip data.

Genome-wide Association Studies Identify Genetic Loci Associated with Albuminuria in Diabetes

Elevated concentrations of albumin in the urine, albuminuria, are a hallmark of diabetic kidney disease and associate with increased risk for end-stage renal disease and cardiovascular events. To gain insight into the pathophysiological mechanisms underlying albuminuria, we conducted meta-analyses of genome-wide association studies and independent replication in up to 5,825 individuals of European ancestry with diabetes mellitus and up to 46,061 without diabetes, followed by functional studies. Known associations of variants in CUBN, encoding cubilin, with the urinary albumin-to-creatinine ratio (UACR) were confirmed in the overall sample (p=2.4*10(-10)). Gene-by-diabetes interactions were detected and confirmed for variants in HS6ST1 and near RAB38/CTSC. SNPs at these loci demonstrated a genetic effect on UACR in individuals with but not without diabetes. The change in average UACR per minor allele was 21% for HS6ST1 and 13% for RAB38/CTSC (p=6.3*10(-7) and 5.8*10(-7), respectively). Experiments using streptozotocin-treated diabetic Rab38 knockout and control rats showed higher urinary albumin concentrations and reduced amounts of megalin and cubilin at the proximal tubule cell surface in Rab38 knockout vs. control rats. Relative expression of RAB38 was higher in tubuli of patients with diabetic kidney disease compared to controls. The loci identified here confirm known and highlight novel pathways influencing albuminuria.

H19 lncRNA alters DNA methylation genome wide by regulating S-adenosylhomocysteine hydrolase

DNA methylation is essential for mammalian development and physiology. Here we report that the developmentally regulated H19 lncRNA binds to and inhibits S-adenosylhomocysteine hydrolase (SAHH), the only mammalian enzyme capable of hydrolysing S-adenosylhomocysteine (SAH). SAH is a potent feedback inhibitor of S-adenosylmethionine (SAM)-dependent methyltransferases that methylate diverse cellular components, including DNA, RNA, proteins, lipids and neurotransmitters. We show that H19 knockdown activates SAHH, leading to increased DNMT3B-mediated methylation of an lncRNA-encoding gene Nctc1 within the Igf2-H19-Nctc1 locus. Genome-wide methylation profiling reveals methylation changes at numerous gene loci consistent with SAHH modulation by H19. Our results uncover an unanticipated regulatory circuit involving broad epigenetic alterations by a single abundantly expressed lncRNA that may underlie gene methylation dynamics of development and diseases and suggest that this mode of regulation may extend to other cellular components.

A genome-wide association study confirms PNPLA3 and identifies TM6SF2 and MBOAT7 as risk loci for alcohol-related cirrhosis

Alcohol misuse is the leading cause of cirrhosis and the second most common indication for liver transplantation in the Western world. We performed a genome-wide association study for alcohol-related cirrhosis in individuals of European descent (712 cases and 1,426 controls) with subsequent validation in two independent European cohorts (1,148 cases and 922 controls). We identified variants in the MBOAT7 (P = 1.03 × 10(-9)) and TM6SF2 (P = 7.89 × 10(-10)) genes as new risk loci and confirmed rs738409 in PNPLA3 as an important risk locus for alcohol-related cirrhosis (P = 1.54 × 10(-48)) at a genome-wide level of significance. These three loci have a role in lipid processing, suggesting that lipid turnover is important in the pathogenesis of alcohol-related cirrhosis.

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Identification of diabetic retinopathy genes through a genome-wide association study among Mexican-Americans from Starr County, Texas

To identify genetic susceptibility loci for severe diabetic retinopathy, 286 Mexican-Americans with type 2 diabetes from Starr County, Texas completed detailed physical and ophthalmologic examinations including fundus photography for diabetic retinopathy grading. 103 individuals with moderate-to-severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy were defined as cases for this study. DNA samples extracted from study subjects were genotyped using the Affymetrix GeneChip® Human Mapping 100K Set, which includes 116,204 single nucleotide polymorphisms (SNPs) across the whole genome. Single-marker allelic tests and 2- to 8-SNP sliding-window Haplotype Trend Regression implemented in HelixTreeTM were first performed with these direct genotypes to identify genes/regions contributing to the risk of severe diabetic retinopathy. An additional 1,885,781 HapMap Phase II SNPs were imputed from the direct genotypes to expand the genomic coverage for a more detailed exploration of genetic susceptibility to diabetic retinopathy. The average estimated allelic dosage and imputed genotypes with the highest posterior probabilities were subsequently analyzed for associations using logistic regression and Fisher's Exact allelic tests, respectively. To move beyond these SNP-based approaches, 104,572 directly genotyped and 333,375 well-imputed SNPs were used to construct genetic distance matrices based on 262 retinopathy candidate genes and their 112 related biological pathways. Multivariate distance matrix regression was then used to test hypotheses with genes and pathways as the units of inference in the context of susceptibility to diabetic retinopathy. This study provides a framework for genome-wide association analyses, and implicated several genes involved in the regulation of oxidative stress, inflammatory processes, histidine metabolism, and pancreatic cancer pathways associated with severe diabetic retinopathy. Many of these loci have not previously been implicated in either diabetic retinopathy or diabetes. In summary, CDC73, IL12RB2, and SULF1 had the best evidence as candidates to influence diabetic retinopathy, possibly through novel biological mechanisms related to VEGF-mediated signaling pathway or inflammatory processes. While this study uncovered some genes for diabetic retinopathy, a comprehensive picture of the genetic architecture of diabetic retinopathy has not yet been achieved. Once fully understood, the genetics and biology of diabetic retinopathy will contribute to better strategies for diagnosis, treatment and prevention of this disease.^

Genome-wide algorithm for detecting CNV associations with diseases

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). The quality of the inferences about copy number can be affected by many factors including batch effects, DNA sample preparation, signal processing, and analytical approach. Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP genotyping data. However, these algorithms lack specificity to detect small CNVs due to the high false positive rate when calling CNVs based on the intensity values. Association tests based on detected CNVs therefore lack power even if the CNVs affecting disease risk are common. In this research, by combining an existing Hidden Markov Model (HMM) and the logistic regression model, a new genome-wide logistic regression algorithm was developed to detect CNV associations with diseases. We showed that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than an existing popular algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.^