Analysis of the genetic diversity in Metopolophium dirhodum (Walker) (Hemiptera, Aphididae) by RAPD markers

The emergence of host-races within aphids may constitute an obstacle to pest management by means of plant resistance. There are examples of host-races within cereals aphids, but their occurrence in Rose Grain Aphid, Metopolophium dirhodum (Walker, 1849), has not been reported yet. In this work, RAPD markers were used to assess effects of the hosts and geographic distance on the genetic diversity of M. dirhodum lineages. Twenty-three clones were collected on oats and wheat in twelve localitites of southern Brazil. From twenty-seven primers tested, only four primers showed polymorphisms. Fourteen different genotypes were revealed by cluster analysis. Five genotypes were collected only on wheat; seven only on oats and two were collected in both hosts. Genetic and geographical distances among all clonal lineages were not correlated. Analysis of molecular variance showed that some molecular markers are not randomly distributed among clonal lineages collected on oats and on wheat. These results suggest the existence of host-races within M. dirhodum, which should be further investigated using a combination of ecological and genetic data.

Genetic Variants of Serum Uric Acid and Gout: An Analysis of > 170,000 Individuals

Background/Purpose: Gout is a common and excruciatingly painful inflammatory arthritis caused by hyperuricemia. In addition to various lifestyle risk factors, a substantial genetic predisposition to gout has long been recognized. The Global Urate Genetics Consortium (GUGC) has aimed to comprehensively investigate the genetics of serum uric acid and gout using data from _ 140,000 individuals of European-ancestry, 8,340 individuals of Indian ancestry, 5,820 African-Americans, and 15,286 Japanese. Methods: We performed discovery GWAS meta-analyses of serum urate levels (n_110,347 individuals) followed by replication analyses (n_32,813 different individuals). Our gout analysis involved 3,151 cases and 68,350 controls, including 1,036 incident gout cases that met the American College of Rheumatology Criteria. We also examined the association of gout with fractional excretion of uric acid (n_6,799). A weighted genetic urate score was constructed based on the number of risk alleles across urate-associated loci, and their association with the risk of gout was evaluated. Furthermore, we examined implicated transcript expression in cis (expression quantitative trait loci databases) for potential insights into the gene underlying the association signal. Finally, in order to further identify urate-associated genomic regions, we performed functional network analyses that incorporated prior knowledge on molecular interactions in which the gene products of implicated genes operate. Results: We identified and replicated 28 genome-wide significant loci in association with serum urate (P 5_10_8), including all previously-reported loci as well as 18 novel genetic loci. Unlike the majority of previouslyidentified loci, none of the novel loci appeared to be obvious candidates for urate transport. Rather, they were mapped to genes that encode for purine production, transcription, or growth factors with broad downstream responses. Besides SLC2A9 and ABCG2, no additional regions contained SNPs that differed significantly (P _ 5_10_8) between sexes. Urateincreasing alleles were associated with an increased risk of gout for all loci. The urate genetic risk score (ranging from 10 to 45) was significantly associated with an increased odds of prevalent gout (OR per unit increase, 1.11; 95% CI, 1.09-1.14) and incident gout (OR, 1.10; 95% CI, 1.08-1.13). Associations for many of the loci were of similar magnitude in individuals of non-European ancestry. Detailed characterization of the loci revealed associations with transcript expression and the fractional excretion of urate. Network analyses implicated the inhibins-activins signaling pathways and glucose metabolism in systemic urate control. Conclusion: The novel genetic candidates identified in this urate/gout consortium study, the largest to date, highlight the importance of metabolic control of urate production and urate excretion. The modulation by signaling processes that influence metabolic pathways such as glycolysis and the pentose phosphate pathway appear to be central mechanisms underpinned by the novel GWAS candidates. These findings may have implications for further research into urate-lowering drugs to treat and prevent gout.

Genetic association tests: a method for the joint analysis of family and case-control data.

With the trend in molecular epidemiology towards both genome-wide association studies and complex modelling, the need for large sample sizes to detect small effects and to allow for the estimation of many parameters within a model continues to increase. Unfortunately, most methods of association analysis have been restricted to either a family-based or a case-control design, resulting in the lack of synthesis of data from multiple studies. Transmission disequilibrium-type methods for detecting linkage disequilibrium from family data were developed as an effective way of preventing the detection of association due to population stratification. Because these methods condition on parental genotype, however, they have precluded the joint analysis of family and case-control data, although methods for case-control data may not protect against population stratification and do not allow for familial correlations. We present here an extension of a family-based association analysis method for continuous traits that will simultaneously test for, and if necessary control for, population stratification. We further extend this method to analyse binary traits (and therefore family and case-control data together) and accurately to estimate genetic effects in the population, even when using an ascertained family sample. Finally, we present the power of this binary extension for both family-only and joint family and case-control data, and demonstrate the accuracy of the association parameter and variance components in an ascertained family sample.

A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control.

HIV-1 sequence diversity is affected by selection pressures arising from host genomic factors. Using paired human and viral data from 1071 individuals, we ran >3000 genome-wide scans, testing for associations between host DNA polymorphisms, HIV-1 sequence variation and plasma viral load (VL), while considering human and viral population structure. We observed significant human SNP associations to a total of 48 HIV-1 amino acid variants (p<2.4 × 10(-12)). All associated SNPs mapped to the HLA class I region. Clinical relevance of host and pathogen variation was assessed using VL results. We identified two critical advantages to the use of viral variation for identifying host factors: (1) association signals are much stronger for HIV-1 sequence variants than VL, reflecting the 'intermediate phenotype' nature of viral variation; (2) association testing can be run without any clinical data. The proposed genome-to-genome approach highlights sites of genomic conflict and is a strategy generally applicable to studies of host-pathogen interaction. DOI:http://dx.doi.org/10.7554/eLife.01123.001.

Assessment of genetic diversity using RAPD analysis in a germplasm collection of sea buckthorn

Selostus: Tyrnin geneettisen monimuotoisuuden arviointi RAPD analyysillä

Genetic polymorphisms of the main transcription factors for adiponectin gene promoter in regulation of adiponectin levels: association analysis in three European cohorts.

Adiponectin serum concentrations are an important biomarker in cardiovascular epidemiology with heritability etimates of 30-70%. However, known genetic variants in the adiponectin gene locus (ADIPOQ) account for only 2%-8% of its variance. As transcription factors are thought to play an under-acknowledged role in carrying functional variants, we hypothesized that genetic polymorphisms in genes coding for the main transcription factors for the ADIPOQ promoter influence adiponectin levels. Single nucleotide polymorphisms (SNPs) at these genes were selected based on the haplotype block structure and previously published evidence to be associated with adiponectin levels. We performed association analyses of the 24 selected SNPs at forkhead box O1 (FOXO1), sterol-regulatory-element-binding transcription factor 1 (SREBF1), sirtuin 1 (SIRT1), peroxisome-proliferator-activated receptor gamma (PPARG) and transcription factor activating enhancer binding protein 2 beta (TFAP2B) gene loci with adiponectin levels in three different European cohorts: SAPHIR (n = 1742), KORA F3 (n = 1636) and CoLaus (n = 5355). In each study population, the association of SNPs with adiponectin levels on log-scale was tested using linear regression adjusted for age, sex and body mass index, applying both an additive and a recessive genetic model. A pooled effect size was obtained by meta-analysis assuming a fixed effects model. We applied a significance threshold of 0.0033 accounting for the multiple testing situation. A significant association was only found for variants within SREBF1 applying an additive genetic model (smallest p-value for rs1889018 on log(adiponectin) = 0.002, β on original scale = -0.217 µg/ml), explaining ∼0.4% of variation of adiponectin levels. Recessive genetic models or haplotype analyses of the FOXO1, SREBF1, SIRT1, TFAPB2B genes or sex-stratified analyses did not reveal additional information on the regulation of adiponectin levels. The role of genetic variations at the SREBF1 gene in regulating adiponectin needs further investigation by functional studies.

Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies.

Whole-grain foods are touted for multiple health benefits, including enhancing insulin sensitivity and reducing type 2 diabetes risk. Recent genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with fasting glucose and insulin concentrations in individuals free of diabetes. We tested the hypothesis that whole-grain food intake and genetic variation interact to influence concentrations of fasting glucose and insulin. Via meta-analysis of data from 14 cohorts comprising ∼ 48,000 participants of European descent, we studied interactions of whole-grain intake with loci previously associated in GWAS with fasting glucose (16 loci) and/or insulin (2 loci) concentrations. For tests of interaction, we considered a P value <0.0028 (0.05 of 18 tests) as statistically significant. Greater whole-grain food intake was associated with lower fasting glucose and insulin concentrations independent of demographics, other dietary and lifestyle factors, and BMI (β [95% CI] per 1-serving-greater whole-grain intake: -0.009 mmol/l glucose [-0.013 to -0.005], P < 0.0001 and -0.011 pmol/l [ln] insulin [-0.015 to -0.007], P = 0.0003). No interactions met our multiple testing-adjusted statistical significance threshold. The strongest SNP interaction with whole-grain intake was rs780094 (GCKR) for fasting insulin (P = 0.006), where greater whole-grain intake was associated with a smaller reduction in fasting insulin concentrations in those with the insulin-raising allele. Our results support the favorable association of whole-grain intake with fasting glucose and insulin and suggest a potential interaction between variation in GCKR and whole-grain intake in influencing fasting insulin concentrations.

Evolutionary analysis of genetic variants involved in rare diseases

Next-generation sequencing techniques such as exome sequencing can successfully detect all genetic variants in a human exome and it has been useful together with the implementation of variant filters to identify causing-disease mutations. Two filters aremainly used for the mutations identification: low allele frequency and the computational annotation of the genetic variant. Bioinformatic tools to predict the effect of a givenvariant may have errors due to the existing bias in databases and sometimes show a limited coincidence among them. Advances in functional and comparative genomics are needed in order to properly annotate these variants.The goal of this study is to: first, functionally annotate Common Variable Immunodeficiency disease (CVID) variants with the available bioinformatic methods in order to assess the reliability of these strategies. Sencondly, as the development of new methods to reduce the number of candidate genetic variants is an active and necessary field of research, we are exploring the utility of gene function information at organism level as a filter for rare disease genes identification. Recently, it has been proposed that only 10-15% of human genes are essential and therefore we would expect that severe rare diseases are mostly caused by mutations on them. Our goal is to determine whether or not these rare and severe diseases are caused by deleterious mutations in these essential genes. If this hypothesis were true, taking into account essential genes as a filter would be an interesting parameter to identify causingdisease mutations.

Genetic divergence among tomato leafminer populations based on AFLP analysis

The objective of this work was to determine the genetic differences among eight Brazilian populations of the tomato leafminer Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae), from the states of Espírito Santo (Santa Tereza), Goiás (Goianápolis), Minas Gerais (Uberlândia and Viçosa), Pernambuco (Camocim de São Félix), Rio de Janeiro (São João da Barra) and São Paulo (Paulínia and Sumaré), using the amplified fragment length polymorphism (AFLP) technique. Fifteen combinations of EcoRI and MseI primers were used to assess divergence among populations. The data were analyzed using unweighted pair-group method, based on arithmetic averages (UPGMA) bootstrap analysis and principal coordinate analysis. Using a multilocus approach, these populations were divided in two groups, based on genetic fingerprints. Populations from Goianápolis, Santa Tereza, and Viçosa formed one group. Populations from Camocim de São Félix, Paulínia, São João da Barra, Sumaré, and Uberlândia fitted in the second group. These results were congruent with differences in susceptibility of this insect to insecticides, previously identified by other authors.

Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.

Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture.

Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

Analysis of the genetic basis of periodic fever with aphthous stomatitis, pharyngitis, and cervical adenitis (PFAPA) syndrome.

PFAPA syndrome is the most common autoinflammatory syndrome in children from Western countries. In spite of its strong familial clustering, its genetic basis and inheritance pattern are still unknown. We performed a comprehensive genetic study on 68 individuals from 14 families. Linkage analysis suggested a susceptibility locus on chromosome 8, but direct molecular sequencing did not support this initial statistical finding. Exome sequencing revealed the absence of any gene that was mutated in all patients. Exhaustive screening of genes involved in other autoinflammatory syndromes or encoding components of the human inflammasome showed no DNA variants that could be linked to PFAPA molecular pathology. Among these, the previously-reported missense mutation V198M in the NLRP3 gene was clearly shown not to co-segregate with PFAPA. Our results on this relatively large cohort indicate that PFAPA syndrome is unlikely to be a monogenic condition. Moreover, none of the several genes known to be involved in inflammation or in autoinflammatory disorders seem to be relevant, alone, to its etiology, suggesting that PFAPA results from oligogenic or complex inheritance of variants in multiple disease genes and/or non-genetic factors.

Genetic Variation in the TP53 Pathway and Bladder Cancer Risk. A Comprehensive Analysis

Introduction: Germline variants in TP63 have been consistently associated with several tumors, including bladder cancer, indicating the importance of TP53 pathway in cancer genetic susceptibility. However, variants in other related genes, including TP53 rs1042522 (Arg72Pro), still present controversial results. We carried out an in depth assessment of associations between common germline variants in the TP53 pathway and bladder cancer risk. Material and Methods: We investigated 184 tagSNPs from 18 genes in 1,058 cases and 1,138 controls from the Spanish Bladder Cancer/EPICURO Study. Cases were newly-diagnosed bladder cancer patients during 1998–2001. Hospital controls were age-gender, and area matched to cases. SNPs were genotyped in blood DNA using Illumina Golden Gate and TaqMan assays. Cases were subphenotyped according to stage/grade and tumor p53 expression. We applied classical tests to assess individual SNP associations and the Least Absolute Shrinkage and Selection Operator (LASSO)-penalized logistic regression analysis to assess multiple SNPs simultaneously. Results: Based on classical analyses, SNPs in BAK1 (1), IGF1R (5), P53AIP1 (1), PMAIP1 (2), SERINPB5 (3), TP63 (3), and TP73 (1) showed significant associations at p-value#0.05. However, no evidence of association, either with overall risk or with specific disease subtypes, was observed after correction for multiple testing (p-value$0.8). LASSO selected the SNP rs6567355 in SERPINB5 with 83% of reproducibility. This SNP provided an OR = 1.21, 95%CI 1.05–1.38, p-value = 0.006, and a corrected p-value = 0.5 when controlling for over-estimation. Discussion: We found no strong evidence that common variants in the TP53 pathway are associated with bladder cancer susceptibility. Our study suggests that it is unlikely that TP53 Arg72Pro is implicated in the UCB in white Europeans. SERPINB5 and TP63 variation deserve further exploration in extended studies.

Gene Set Analysis for improving genetic association studies

Introduction. Genetic epidemiology is focused on the study of the genetic causes that determine health and diseases in populations. To achieve this goal a common strategy is to explore differences in genetic variability between diseased and nondiseased individuals. Usual markers of genetic variability are single nucleotide polymorphisms (SNPs) which are changes in just one base in the genome. The usual statistical approach in genetic epidemiology study is a marginal analysis, where each SNP is analyzed separately for association with the phenotype. Motivation. It has been observed, that for common diseases the single-SNP analysis is not very powerful for detecting genetic causing variants. In this work, we consider Gene Set Analysis (GSA) as an alternative to standard marginal association approaches. GSA aims to assess the overall association of a set of genetic variants with a phenotype and has the potential to detect subtle effects of variants in a gene or a pathway that might be missed when assessed individually. Objective. We present a new optimized implementation of a pair of gene set analysis methodologies for analyze the individual evidence of SNPs in biological pathways. We perform a simulation study for exploring the power of the proposed methodologies in a set of scenarios with different number of causal SNPs under different effect sizes. In addition, we compare the results with the usual single-SNP analysis method. Moreover, we show the advantage of using the proposed gene set approaches in the context of an Alzheimer disease case-control study where we explore the Reelin signal pathway.