A genome-wide association search for type 2 diabetes genes in African Americans

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P<0.0071), were directionally consistent in the Replication cohort and were associated with T2DM in subjects without nephropathy (P<0.05). Meta-analysis in all cases and controls revealed a single SNP reaching genome-wide significance (P<2.5×10⁻⁸). SNP rs7560163 (P = 7.0×10⁻⁹, OR (95% CI) = 0.75 (0.67–0.84)) is located intergenically between RND3 and RBM43. Four additional loci (rs7542900, rs4659485, rs2722769 and rs7107217) were associated with T2DM (P<0.05) and reached more nominal levels of significance (P<2.5×10⁻⁵) in the overall analysis and may represent novel loci that contribute to T2DM. We have identified novel T2DM-susceptibility variants in the African-American population. Notably, T2DM risk was associated with the major allele and implies an interesting genetic architecture in this population. These results suggest that multiple loci underlie T2DM susceptibility in the African-American population and that these loci are distinct from those identified in other ethnic populations.     

GenABEL: an R library for genome-wide association analysis

Here we describe an R library for genome-wide association (GWA) analysis. It implements effective storage and handling of GWA data, fast procedures for genetic data quality control, testing of association of single nucleotide polymorphisms with binary or quantitative traits, visualization of results and also provides easy interfaces to standard statistical and graphical procedures implemented in base R and special R libraries for genetic analysis. We evaluated GenABEL using one simulated and two real data sets. We conclude that GenABEL enables the analysis of GWA data on desktop computers. Availability: http://cran.r-project.org.     

Linkage and association studies identify a novel locus for Alzheimer disease at 7q36 in a Dutch population-based sample

We obtained conclusive linkage of Alzheimer disease (AD) with a candidate region of 19.7 cM at 7q36 in an extended multiplex family, family 1270, ascertained in a population-based study of early-onset AD in the northern Netherlands. Single-nucleotide polymorphism and haplotype association analyses of a Dutch patient-control sample further supported the linkage at 7q36. In addition, we identified a shared haplotype at 7q36 between family 1270 and three of six multiplex AD-affected families from the same geographical region, which is indicative of a founder effect and defines a priority region of 9.3 cM. Mutation analysis of coding exons of 29 candidate genes identified one linked synonymous mutation, g.38030G-->C in exon 10, that affected codon 626 of the PAX transactivation domain interacting protein gene (PAXIP1). It remains to be determined whether PAXIP1 has a functional role in the expression of AD in family 1270 or whether another mutation at this locus explains the observed linkage and sharing. Together, our linkage data from the informative family 1270 and the association data in the population-based early-onset AD patient-control sample strongly support the identification of a novel AD locus at 7q36 and re-emphasize the genetic heterogeneity of AD.     

Heritability of serum iron, ferritin and transferrin saturation in a genetically isolated population, the Erasmus Rucphen Family (ERF) Study

Background: Iron has been implicated in the pathogenesis of various disorders. Mutations in the HFE gene are associated with an increase in serum iron parameters. The aim of this study was to estimate the heritability in serum iron parameters explained by HFE. Methods: Ninety families (980 subjects) were included in the present analysis. Heritability estimation was conducted using the variance component method. The likelihood ratio test was used to compare models. Phenotypic and genetic correlations between serum iron parameters were calculated. Results: The heritability (h(2) +/- SE) estimates were 0.23 +/- 0.07 (p < 0.0001) for iron, 0.29 +/- 0.09 (p < 0.0001) for ferritin and 0.28 +/- 0.07 (p < 0.0001) for transferrin saturation while adjusting for age, age(2) and sex. The HFE genotypes explained between 2 to 6% of the sex and age-adjusted variance in serum iron, ferritin and transferrin saturation. There was a high genetic correlation between serum iron parameters, suggesting pleiotropy between these traits. Conclusion: A substantial proportion of the variance of iron, ferritin and transferrin saturation can be explained by additive genetic effects, independent of sex and age. The HFE genotypes explained a considerable proportion of serum iron parameters and may be an important factor in the complex iron network.     

Detection, imputation, and association analysis of small deletions and null alleles on oligonucleotide arrays

Copy-number variation (CNV) is a major contributor to human genetic variation. Recently, CNV associations with human disease have been reported. Many genome-wide association (GWA) studies in complex diseases have been performed with sets of biallelic single-nucleotide polymorphisms (SNPs), but the available CNV methods are still limited. We present a new method (TriTyper) that can infer genotypes in case-control data sets for deletion CNVs, or SNPs with an extra, untyped allele at a high-resolution single SNP level. By accounting for linkage disequilibrium (LD), as well as intensity data, calling accuracy is improved. Analysis of 3102 unrelated individuals with European descent, genotyped with Illumina Infinium BeadChips, resulted in the identification of 1880 SNPs with a common untyped allele, and these SNPs are in strong LD with neighboring biallelic SNPs. Simulations indicate our method has superior power to detect associations compared to biallelic SNPs that are in LD with these SNPs, yet without increasing type I errors, as shown in a GWA analysis in celiac disease. Genotypes for 1204 triallelic SNPs could be fully imputed, with only biallelic-genotype calls, permitting association analysis of these SNPs in many published data sets. We estimate that 682 of the 1655 unique loci reflect deletions; this is on average 99 deletions per individual, four times greater than those detected by other methods. Whereas the identified loci are strongly enriched for known deletions, 61% have not been reported before. Genes overlapping with these loci more often have paralogs (p = 0.006) and biologically interact with fewer genes than expected (p = 0.004).     

Inheritance of Hypodontia in Kerry Blue Terrier Dogs

Complex segregation analysis, which earlier proved efficient in studying complex hereditary diseases in humans and have been introduced in animal genetics for several years, was used to analyze the inheritance of hypodontia by premolars in Kerry Blue Terrier dogs. Dental formulas have been determined in 598 out of 911 animals united into a single large, extended pedigree. The results of integrated segregation analysis indicated heterogeneous genetic control of different forms of hypodontia. The absence of different premolars in dogs has been demonstrated to be described by different models of inheritance: the absence of the second premolars can be described by the recessive major-gene model, whereas the agenesis of the fourth premolars have a more complex genetic mechanism and cannot be described by the model of a simple major-gene control.     

ParallABEL: an R library for generalized parallelization of genome-wide association studies

Background  

Genome-Wide Association (GWA) analysis is a powerful method for identifying loci associated with complex traits and drug response. Parts of GWA analyses, especially those involving thousands of individuals and consuming hours to months, will benefit from parallel computation. It is arduous acquiring the necessary programming skills to correctly partition and distribute data, control and monitor tasks on clustered computers, and merge output files.     

Comparative Performance of Four Methods for High-throughput Glycosylation Analysis of Immunoglobulin G in Genetic and Epidemiological Research

The biological and clinical relevance of glycosylation is becoming increasingly recognized, leading to a growing interest in large-scale clinical and population-based studies. In the past few years, several methods for high-throughput analysis of glycans have been developed, but thorough validation and standardization of these methods is required before significant resources are invested in large-scale studies. In this study, we compared liquid chromatography, capillary gel electrophoresis, and two MS methods for quantitative profiling of N-glycosylation of IgG in the same data set of 1201 individuals. To evaluate the accuracy of the four methods we then performed analysis of association with genetic polymorphisms and age. Chromatographic methods with either fluorescent or MS-detection yielded slightly stronger associations than MS-only and multiplexed capillary gel electrophoresis, but at the expense of lower levels of throughput. Advantages and disadvantages of each method were identified, which should inform the selection of the most appropriate method in future studies.Glycans are important structural and functional components of the majority of proteins, but because of their structural complexity and the absence of a direct genetic template our current understanding of the role of glycans in biological processes lags significantly behind the knowledge about proteins or DNA (1, 2). However, a recent comprehensive report endorsed by the US National Academies concluded that “glycans are directly involved in the pathophysiology of every major disease and that additional knowledge from glycoscience will be needed to realize the goals of personalized medicine” (3).It is estimated that the glycome (defined as the complete set of all glycans) of a eukaryotic cell is composed of more than a million different glycosylated structures (1), which contain up to 10,000 structural glycan epitopes for interaction with antibodies, lectins, receptors, toxins, microbial adhesins, or enzymes (4). Our recent population-based studies indicated that the composition of the human plasma N-glycome varies significantly between individuals (5, 6). Because glycans have important structural and regulatory functions on numerous glycoproteins (7), the observed variability suggests that differences in glycosylation might contribute to a large part of the human phenotypic variability. Interestingly, when the N-glycome of isolated immunoglobulin G (IgG)¹ was analyzed, it was found to be even more variable than the total plasma N-glycome (8), indicating that the combined analysis of all plasma glycans released from many different glycoproteins blurs signals of protein-specific regulation of glycosylation.A number of studies have investigated the role of glycans in human disease, including autoimmune diseases and cancer (9, 10). However, most human glycan studies have been conducted with very small sample sizes. Given the complex causal pathways involved in pathophysiology of common complex disease, and thus the likely modest effect sizes associated with individual factors, the majority of these studies are very likely to be substantially underpowered. In the case of inflammatory bowel disease, only 20% of reported inflammatory bowel disease glycan associations were replicated in subsequent studies, suggesting that most are false positive findings and that there is publication bias favoring the publication of positive findings (11). This situation is similar to that which occurred in the field of genetic epidemiology in the past when many underpowered candidate gene studies were published and were later found to consist of mainly false positive findings (12, 13). It is essential, therefore, that robust and affordable methods for high-throughput analysis are developed so that adequately powered studies can be conducted and the publication of large numbers of small studies reporting false positive results (which could threaten the credibility of glycoscience) be avoided.Rapid advances of technologies for high-throughput genome analysis in the past decade enabled large-scale genome-wide association studies (GWAS). GWAS has become a reliable tool for identification of associations between genetic polymorphisms and various human diseases and traits (14). Thousands of GWAS have been conducted in recent years, but these have not included the study of glycan traits until recently. The main reason was the absence of reliable tools for high-throughput quantitative analysis of glycans that could match the measurements of genomic, biochemical, and other traits in their cost, precision, and reproducibility. However, several promising high-throughput technologies for analysis of N-glycans were developed (8, 15–20) recently. Successful implementation of high-throughput analytical techniques for glycan analysis resulted in publication of four initial GWAS of the human glycome (21–24).In this study, we compared ultra-performance liquid chromatography with fluorescence detection (UPLC-FLR), multiplex capillary gel electrophoresis with laser induced fluorescence detection (xCGE-LIF), matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), and liquid chromatography electrospray mass spectrometry (LC-ESI-MS) as tools for mid-to-high-throughput glycomics and glycoproteomics. We have analyzed IgG N-glycans by all four methods in 1201 individuals from European populations. The analysis of associations between glycans and ∼300,000 single-nucleotide genetic polymorphisms was performed and correlation between glycans and age was studied in all four data sets to identify the analytical method that shows the strongest potential to uncover biological mechanisms underlying protein glycosylation.