Genome Association Analysis Research Articles

Genes of the transforming growth factor-beta signalling pathway are associated with pre-implantation embryonic development in cattle

One of the main factors affecting cattle fertility is pre-implantation development of the bovine embryo, which is a complex process regulated by various signal-transduction pathways. The transforming growth factor-β (TGF-β) signalling system, which is responsible for many biological processes including cell proliferation, differentiation and apoptosis, also is involved in embryo development. We hypothesized that altered expression of TGF-β genes in pre-implantation bovine embryos is associated with morphological abnormalities of these embryos. To test this hypothesis, we produced embryos in vitro and classified them at the blastocyst stage as either normally developed blastocysts or degenerates (growth-arrested embryos). The expression patterns of 25 genes from the TGF-β pathway were assessed using quantitative real time PCR. Ten genes showed differential expression between the two embryo groups, four genes displayed similar expressional profiles, and 11 genes had no detectable expression. An altered expression profile was statistically significant for 10 of the 14 expressed genes, and all were up-regulated in degenerate embryos vs. blastocysts. Furthermore, genomic association analysis of the cows from which embryos were produced revealed a significant association of ID3 and BMP4 polymorphisms--two of the most significant differentially expressed genes--with fertilization rate and blastocyst rate, respectively. Taken together, we conclude that TGF-β pathway genes, especially BMP4 and ID3 play a vital function in the regulation of pre-implantation embryo development at both embryo and maternal levels. Hence, these genes may be suitable as genetic markers for embryo development and fertility in cattle.

Association of the genetic variants in the nucleotide excision repair genes XPA and XPC with cisplatin-induced hearing loss in patients with osteosarcoma.

10077 Background: Cisplatin is a widely used and effective chemotherapeutic agent in the treatment of osteosarcoma. However, cisplatin-induced ototoxicity is a serious problem, affecting more than 60% of patients and compromising language and cognitive development. Unfortunately, individuals at risk to develop ototoxicity cannot be identified upfront. Genetic variants in genes involved in the metabolism of cisplatin may predispose to cisplatin-induced hearing loss and help to identify patients at risk. Methods: In a candidate gene pathway approach, we selected 224 SNPs in 30 candidate genes related to Platinum-DNA repair pathways and genotyped for a discovery group of 105 patients with osteosarcoma for these variants. Cisplatin-induced ototoxicity (n = 47), defined as the development of grade 2–4 hearing impairment using Common Terminology Criteria for Adverse Events (CTCAE version 3), showing a hearing loss of >25 dB at frequencies of 4–8 kHz, was associated with genetic variation. A replication study was performed in a independent cohort of 51 patients with osteosarcoma. Genotyping was performed using the Illumina GoldenGate assay. Association analysis and meta-analysis were performed using the whole genome association analysis toolset PLINK. Results: In the discovery cohort a total of 13 SNPs were significantly (p value < 0.05) associated with ototoxicity. Upon meta-analysis, addition of the replication set resulted in lower p-values for 2 SNPs. The two SNPs showing a strong association with hearing loss in patients with osteosarcoma were rs2805835 in the gene XPA (p-value 0.01, OR=2.7 (95%CI: 1.20-6.15) and rs2227999 in XPC (p-value 0.02; OR=3.2 (95% CI:1.19-8.80). Conclusions: The Nucleotide Excision Repair (NER) genes XPA and XPC form an important molecular mechanism by which cisplatin DNA adducts can be repaired and it has recently been shown that these genes have a high expression in the cochlea. Our data suggest that genetic variants in these genes, may contribute to cisplatin ototoxicity. This study should be viewed as the first step in the development of genetic markers to predict cisplatin-induced ototoxicity in individual patients.

Genome Selection Sweep and Association Analysis Shed Light on Future Breeding by Design in Wheat

ABSTRACTTwo sets of bread wheat (Triticum aestivum L.) varieties and their respective founder parents (St2422/464 and Abbondanza) in China were genotyped at 481 microsatellite loci. The selection sweeps detected almost completely overlapped with one another across the two sets on the 21 chromosomes although a cluster analysis showed that they do represent distinct subpopulations with Fst = 0.0734 (minor allele frequency [MAF] = 0.1, –log10p = 5). About 38.7% of the loci appeared to have experienced some selection sweep. Important agronomic traits were usually associated with these selection sweep valleys. The alleles present at key loci in these valleys were often in common between the two founders. Marker–trait association based on three seasons of field data suggested the association of one, four, and six loci with, respectively, the number of fertile tillers per plant, grain number per spike, and thousand grain weight. Five, 14, and 21 loci were associated with the three yield components in two of the three seasons. We suggest that the breeding potential of a line can be predicted from its allelic state at certain critical marker loci, which represent major targets for breeding by design in wheat. Genotypic descriptions of major cultivars and the analytical approach we have described here provide a path toward the a priori selection of favorable breeding parents.

Meta-Analysis of Two Genome-Wide Association Studies of Bovine Paratuberculosis

BackgroundBovine paratuberculosis (ParaTB) also known as Johne's disease, is a contagious fatal disease resulting from infection by Mycobacterium avium subspecies paratuberculosis (MAP). Previous studies have identified loci associated with ParaTB using different measurements to define cases and controls. The objective of this study was to combine the data from two recent studies to identify genetic loci associated with MAP tissue infection and humoral immune response, defined by MAP ELISA-positive cattle, by comparing cases and control animals for one or both measures of infection.Methodology/Principal FindingsThe two populations used for the association analyses were a cohort of MAP tissue infected animals and control Holstein cows from the USA and the second cohort composed of ELISA-positive and ELISA-negative Holstein cows from Italy. Altogether 1190 cattle were genotyped with the Illumina BovineSNP50 BeadChip. SNP markers were removed if the minor allele frequency <0.01 or genotyping failure was >5%. Animals were removed with >5% genotyping failure. Whole genome association analyses were conducted with the GRAMMAR-CG method using two different definitions of control populations.Conclusion/SignificanceThe analyses identified several loci (P<5 e-05) associated with ParaTB, defined by positive ELISA and presence of bacteria in tissue compared to ELISA and tissue negative animals, on chromosomes 1, 12 and 15 and one unassigned SNP. These results confirmed associations on chromosome 12 and the unassigned SNP with ParaTB which had been found in the Italian population alone. Furthermore, several additional genomic regions were found associated with ParaTB when ELISA and tissue positive animals were compared with tissue negative samples. These loci were on chromosomes 1, 6, 7, 13, 16, 21,23 and 25 (P<5 e-05). The results clearly indicate the importance of the phenotype definition when seeking to identify markers associated with different disease responses.

Studying Gene and Gene-Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression

Genomic association analyses of complex traits demand statistical tools that are capable of detecting small effects of common and rare variants and modeling complex interaction effects and yet are computationally feasible. In this work, we introduce a similarity-based regression method for assessing the main genetic and interaction effects of a group of markers on quantitative traits. The method uses genetic similarity to aggregate information from multiple polymorphic sites and integrates adaptive weights that depend on allele frequencies to accomodate common and uncommon variants. Collapsing information at the similarity level instead of the genotype level avoids canceling signals that have the opposite etiological effects and is applicable to any class of genetic variants without the need fordichotomizing the allele types. To assess gene-trait associations, we regress trait similarities for pairs of unrelated individuals on theirgenetic similarities and assess association by using a score test whose limiting distribution is derived in this work. The proposed regression framework allows for covariates, has the capacity to model both main and interaction effects, can be applied to a mixture of different polymorphism types, and is computationally efficient. These features make it an ideal tool for evaluating associations between phenotype and marker sets defined by linkage disequilibrium (LD) blocks, genes, or pathways in whole-genome analysis.

Genome wide scan for somatic cell counts in holstein bulls

BackgroundMastitis is the most costly disease for dairy production, and control of the disease is often difficult, due to its multi-factorial nature. Susceptibility to mastitis is under partial genetic control and the industry uses indirect selection for decreased concentrations of somatic cells in milk to reduce mastitis.MethodsA genome-wide scan was performed to identify genomic regions associated with deregressed estimated breeding values (EBVs) for somatic cell counts (SCC) in Holstein bulls. In total 1183 proven bulls of the Italian of Holstein population, were genotyped with the BovineSNP50 BeadChip (Illumina, San Diego, CA) and a whole genome association analysis was performed using the R package GenABEL.ResultsTwo chromosomal regions showed association with SCC, a region on chromosome 14 with high significance (P < 5x10-6) and a region on chromosome 6 with moderate significance (P < 5x10-5).ConclusionsTwo regions with effects on SCC have been identified with good statistical support. A further study of these candidate regions will be performed to verify the results and identify the causal mutations.

Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses

Population-based genomic association analyses are more powerful than within-family analyses. However, population stratification (unknown or ignored origin of individuals from multiple source populations) and cryptic relatedness (unknown or ignored covariance between individuals because of their relatedness) are confounding factors in population-based genomic association analyses, which inflate the false-positive rate. As a consequence, false association signals may arise in genomic data association analyses for reasons other than true association between the tested genomic factor (marker genotype, gene or protein expression) and the study phenotype. It is therefore important to correct or account for these confounders in population-based genomic data association analyses. The common correction techniques for population stratification and cryptic relatedness problems are presented here in the phenotype-marker association analysis context, and comments on their suitability for other types of genomic association analyses (for example, phenotype-expression association) are also provided. Even though many of these techniques have originally been developed in the context of human genetics, most of them are also applicable to model organisms and breeding populations.

Statistical estimation of correlated genome associations to a quantitative trait network.

Many complex disease syndromes, such as asthma, consist of a large number of highly related, rather than independent, clinical or molecular phenotypes. This raises a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical framework called graph-guided fused lasso (GFlasso) to directly and effectively incorporate the correlation structure of multiple quantitative traits such as clinical metrics and gene expressions in association analysis. Our approach represents correlation information explicitly among the quantitative traits as a quantitative trait network (QTN) and then leverages this network to encode structured regularization functions in a multivariate regression model over the genotypes and traits. The result is that the genetic markers that jointly influence subgroups of highly correlated traits can be detected jointly with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently and combined the results afterwards, our approach analyzes all of the traits jointly in a single statistical framework. This allows our method to borrow information across correlated phenotypes to discover the genetic markers that perturb a subset of the correlated traits synergistically. Using simulated datasets based on the HapMap consortium and an asthma dataset, we compared the performance of our method with other methods based on single-marker analysis and regression-based methods that do not use any of the relational information in the traits. We found that our method showed an increased power in detecting causal variants affecting correlated traits. Our results showed that, when correlation patterns among traits in a QTN are considered explicitly and directly during a structured multivariate genome association analysis using our proposed methods, the power of detecting true causal SNPs with possibly pleiotropic effects increased significantly without compromising performance on non-pleiotropic SNPs.

Violence and post-traumatic stress disorder in Sao Paulo and Rio de Janeiro, Brazil: the protocol for an epidemiological and genetic survey

Backgroundviolence is a public health major concern, and it is associated with post-traumatic stress disorder and other psychiatric outcomes. Brazil is one of the most violent countries in the world, and has an extreme social inequality. Research on the association between violence and mental health may support public health policy and thus reduce the burden of disease attributable to violence. The main objectives of this project were: to study the association between violence and mental disorders in the Brazilian population; to estimate the prevalence rates of exposure to violence, post-traumatic stress disorder, common metal disorder, and alcohol hazardous use and dependence: and to identify contextual and individual factors, including genetic factors, associated with the outcomes.Methods/designone phase cross-sectional survey carried out in Sao Paulo and Rio de Janeiro, Brazil. A multistage probability to size sampling scheme was performed in order to select the participants (3000 and 1500 respectively). The cities were stratified according to homicide rates, and in Sao Paulo the three most violent strata were oversampled. The measurements included exposure to traumatic events, psychiatric diagnoses (CIDI 2.1), contextual (homicide rates and social indicators), and individual factors, such as demographics, social capital, resilience, help seeking behaviours. The interviews were carried between June/2007 February/2008, by a team of lay interviewers. The statistical analyses will be weight-adjusted in order to take account of the design effects. Standardization will be used in order to compare the results between the two centres. Whole genome association analysis will be performed on the 1 million SNP (single nucleotide polymorphism) arrays, and additional association analysis will be performed on additional phenotypes. The Ethical Committee of the Federal University of Sao Paulo approved the study, and participants who matched diagnostic criteria have been offered a referral to outpatient clinics at the Federal University of Sao Paulo and Federal University of Rio de Janeiro.

Correcting for relatedness in Bayesian models for genomic data association analysis

For small pedigrees, the issue of correcting for known or estimated relatedness structure in population-based Bayesian multilocus association analysis is considered. Two such relatedness corrections: [1] a random term arising from the infinite polygenic model and [2] a fixed covariate following the class D model of Bonney, are compared with the case of no correction using both simulated and real marker and gene-expression data from lymphoblastoid cell lines from four CEPH families. This comparison is performed with clinical quantitative trait locus (cQTL) models-multilocus association models where marker data and expression levels of gene transcripts as well as possible genotype x expression interaction terms are jointly used to explain quantitative trait variation. We found out that regardless of having a correction term in the model, the cQTL-models fit a few extra small-effect components (similar to finite polygenic models) which itself serves as a relatedness correction. For small data and small heritability one may use the covariate model, which clearly outperforms the infinite polygenic model in small data examples.

Genome‐wide Linkage and Association Analyses to Identify Genes Influencing Adiponectin Levels: The GEMS Stud

Adiponectin has a variety of metabolic effects on obesity, insulin sensitivity, and atherosclerosis. To identify genes influencing variation in plasma adiponectin levels, we performed genome-wide linkage and association scans of adiponectin in two cohorts of subjects recruited in the Genetic Epidemiology of Metabolic Syndrome Study. The genome-wide linkage scan was conducted in families of Turkish and southern European (TSE, n = 789) and Northern and Western European (NWE, N = 2,280) origin. A whole genome association (WGA) analysis (500K Affymetrix platform) was carried out in a set of unrelated NWE subjects consisting of approximately 1,000 subjects with dyslipidemia and 1,000 overweight subjects with normal lipids. Peak evidence for linkage occurred at chromosome 8p23 in NWE subjects (lod = 3.10) and at chromosome 3q28 near ADIPOQ, the adiponectin structural gene, in TSE subjects (lod = 1.70). In the WGA analysis, the single-nucleotide polymorphisms (SNPs) most strongly associated with adiponectin were rs3774261 and rs6773957 (P < 10(-7)). These two SNPs were in high linkage disequilibrium (r(2) = 0.98) and located within ADIPOQ. Interestingly, our fourth strongest region of association (P < 2 x 10(-5)) was to an SNP within CDH13, whose protein product is a newly identified receptor for high-molecular-weight species of adiponectin. Through WGA analysis, we confirmed previous studies showing SNPs within ADIPOQ to be strongly associated with variation in adiponectin levels and further observed these to have the strongest effects on adiponectin levels throughout the genome. We additionally identified a second gene (CDH13) possibly influencing variation in adiponectin levels. The impact of these SNPs on health and disease has yet to be determined.

An approach to incorporate linkage disequilibrium structure into genomic association analysis

In this study, we propose to use the principal component analysis (PCA) and regression model to incorporate linkage disequilibrium (LD) in genomic association data analysis. To accommodate LD in genomic data and reduce multiple testing, we suggest performing PCA and extracting the PCA score to capture the variation of genomic data, after which regression analysis is used to assess the association of the disease with the principal component score. An empirical analysis result shows that both genotype-based correlation matrix and haplotype-based LD matrix can produce similar results for PCA. Principal component score seems to be more powerful in detecting genetic association because the principal component score is quantitatively measured and may be able to capture the effect of multiple loci.

Analysis and Application of European Genetic Substructure Using 300 K SNP Information

European population genetic substructure was examined in a diverse set of >1,000 individuals of European descent, each genotyped with >300 K SNPs. Both STRUCTURE and principal component analyses (PCA) showed the largest division/principal component (PC) differentiated northern from southern European ancestry. A second PC further separated Italian, Spanish, and Greek individuals from those of Ashkenazi Jewish ancestry as well as distinguishing among northern European populations. In separate analyses of northern European participants other substructure relationships were discerned showing a west to east gradient. Application of this substructure information was critical in examining a real dataset in whole genome association (WGA) analyses for rheumatoid arthritis in European Americans to reduce false positive signals. In addition, two sets of European substructure ancestry informative markers (ESAIMs) were identified that provide substantial substructure information. The results provide further insight into European population genetic substructure and show that this information can be used for improving error rates in association testing of candidate genes and in replication studies of WGA scans.

Genomewide Analysis of Secretory Activation in Mouse Models

Mouse models have been widely used to elucidate the biology of mammary gland development and secretory activation. Recent advances in the availability of genomic resources for mice will generate a renewed effort to define the genetic basis of lactation phenotypes and help identify candidate gene pathways. Specific aspects of these advances are relevant to the dairy industry and may provide a rationale for improving milk production in the dairy cow. Differences are evident in mammary gland morphology and various characteristics of milk production of inbred mouse strains, but few studies have undertaken any systematic phenotypic analysis of the different inbred strains of mice for lactation performance. Whole genome association analysis using recent strain-specific genotype data and detailed phenotype measurements from available inbred strains, along with transcript profiling of divergent inbred strains for lactation performance, provides a valuable approach to identify putative candidate genes and associated pathways underlying dairy QTL intervals. Here we discuss the utility of integrating mouse phenomic and genomic resources for understanding secretory activation in the mammary gland.

Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis

Genome-wide association studies can help identify multi-gene contributions to disease. As the number of high-density genomic markers tested increases, however, so does the number of loci associated with disease by chance. Performing a brute-force test for the interaction of four or more high-density genomic loci is unfeasible given the current computational limitations. Heuristics must be employed to limit the number of statistical tests performed. In this paper we explore the use of biological domain knowledge to supplement statistical analysis and data mining methods to identify genes and pathways associated with disease. We describe Pathway/SNP, a software application designed to help evaluate the association between pathways and disease. Pathway/SNP integrates domain knowledge—SNP, gene and pathway annotation from multiple sources—with statistical and data mining algorithms into a tool that can be used to explore the etiology of complex diseases.

The IMAGE project: methodological issues for the molecular genetic analysis of ADHD

The genetic mechanisms involved in attention deficit hyperactivity disorder (ADHD) are being studied with considerable success by several centres worldwide. These studies confirm prior hypotheses about the role of genetic variation within genes involved in the regulation of dopamine, norepinephrine and serotonin neurotransmission in susceptibility to ADHD. Despite the importance of these findings, uncertainties remain due to the very small effects sizes that are observed. We discuss possible reasons for why the true strength of the associations may have been underestimated in research to date, considering the effects of linkage disequilibrium, allelic heterogeneity, population differences and gene by environment interactions.With the identification of genes associated with ADHD, the goal of ADHD genetics is now shifting from gene discovery towards gene functionality – the study of intermediate phenotypes ('endophenotypes'). We discuss methodological issues relating to quantitative genetic data from twin and family studies on candidate endophenotypes and how such data can inform attempts to link molecular genetic data to cognitive, affective and motivational processes in ADHD.The International Multi-centre ADHD Gene (IMAGE) project exemplifies current collaborative research efforts on the genetics of ADHD. This European multi-site project is well placed to take advantage of the resources that are emerging following the sequencing of the human genome and the development of international resources for whole genome association analysis. As a result of IMAGE and other molecular genetic investigations of ADHD, we envisage a rapid increase in the number of identified genetic variants and the promise of identifying novel gene systems that we are not currently investigating, opening further doors in the study of gene functionality.

Human nuclear transcription factor gene CREM: genomic organization, mutation screening, and association analysis in panic disorder.

Panic disorder is an anxiety disorder with an estimated heritability of 48%. Variation in the gene of the nuclear transcription factor "cAMP-responsive element modulator" (CREM) might contribute to its pathogenesis. CREM knock-out mice exhibit significantly less anxiety behavior than wild-type mice and the alternative CREM gene product "inducible cAMP early repressor" (ICER) plays a pivotal role in the hypothalamo-pituitary-adrenal (HPA) axis, which is disturbed in panic disorder. We characterized the genomic organization of the human CREM gene and performed a systematic mutation screening by means of single stranded conformational analysis (SSCA) in a sample of 40 German patients with panic disorder (DSM-III-R). Four novel single nucleotide polymorphisms in CREM promoters P 1 and P 4, one trinucleotide (ATT)-repeat polymorphism in CREM promoter P 2-generating the ICER isoform-and a rare amino acid substitution in CREM exon glut 2 were identified. Association analysis in an extended sample of German patients (n = 88) revealed a significant excess of the shorter CREM P 2 promoter eight-repeat trinucleotide allele and of genotypes containing the eight-repeat trinucleotide allele in panic disorder (P = 0.02), in particular in panic disorder without agoraphobia (P = 0.001). A replication study in independent Italian (n = 76) and Spanish (n = 62) samples, however, failed to confirm this observation. This suggests that the CREM P 2 promoter trinucleotide polymorphism is not a major susceptibility factor in the pathogenesis of panic disorder. Functional analysis of the observed CREM P 2 promoter polymorphism as well as studies in independent panic disorder samples are necessary.