An evaluation of the genetic-matched pair study design using genome-wide SNP data from the European population

The more the merrier? How a few SNPs predict pigmentation phenotypes in the Northern German population

Human pigmentation traits are of great interest to many research areas, from ancient DNA analysis to forensic science. We developed a gene-based predictive model for pigmentation phenotypes in a realistic target population for forensic case work from Northern Germany and compared our model with those brought forth by previous studies of genetically more heterogeneous populations. In doing so, we aimed at answering the following research questions: (1) do existing models allow good prediction of high-quality phenotypes in a genetically similar albeit more homogeneous population? (2) Would a model specifically set up for the more homogeneous population perform notably better than existing models? (3) Can the number of markers included in existing models be reduced without compromising their predictive capability in the more homogenous population? We investigated the association between eye, hair and skin colour and 12 candidate single-nucleotide polymorphisms (SNPs) from six genes. Our study comprised two samples of 300 and 100 individuals from Northern Germany. SNP rs12913832 in HERC2 was found to be strongly associated with blue eye colour (odds ratio=40.0, P<1.2 × 10(-4)) and to yield moderate predictive power (AUC: 77%; sensitivity: 90%, specificity: 63%, both at a 0.5 threshold for blue eye colour probability). SNP associations with hair and skin colour were weaker and genotypes less predictive. A comparison with two recently published sets of markers to predict eye and hair colour revealed that the consideration of additional SNPs with weak-to-moderate effect increased the predictive power for eye colour, but not for hair colour.

GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans

Attempts to detect genetic population substructure in humans are troubled by the fact that the vast majority of the total amount of observed genetic variation is present within populations rather than between populations. Here we introduce a new algorithm for transforming a genetic distance matrix that reduces the within-population variation considerably. Extensive computer simulations revealed that the transformed matrix captured the genetic population differentiation better than the original one which was based on the T1 statistic. In an empirical genomic data set comprising 2,457 individuals from 23 different European subpopulations, the proportion of individuals that were determined as a genetic neighbour to another individual from the same sampling location increased from 25% with the original matrix to 52% with the transformed matrix. Similarly, the percentage of genetic variation explained between populations by means of Analysis of Molecular Variance (AMOVA) increased from 1.62% to 7.98%. Furthermore, the first two dimensions of a classical multidimensional scaling (MDS) using the transformed matrix explained 15% of the variance, compared to 0.7% obtained with the original matrix. Application of MDS with Mclust, SPA with Mclust, and GemTools algorithms to the same dataset also showed that the transformed matrix gave a better association of the genetic clusters with the sampling locations, and particularly so when it was used in the AMOVA framework with a genetic algorithm. Overall, the new matrix transformation introduced here substantially reduces the within population genetic differentiation, and can be broadly applied to methods such as AMOVA to enhance their sensitivity to reveal population substructure. We herewith provide a publically available (http://www.erasmusmc.nl/fmb/resources/GAGA) model-free method for improved genetic population substructure detection that can be applied to human as well as any other species data in future studies relevant to evolutionary biology, behavioural ecology, medicine, and forensics.

Rare and low frequency variant stratification in the UK population: description and impact on association tests

Although variations in allele frequencies at common SNPs have been extensively studied in different populations, little is known about the stratification of rare variants and its impact on association tests. In this paper, we used Affymetrix 500K genotype data from the WTCCC to investigate if variants in three different frequency categories (below 1%, between 1 and 5%, above 5%) show different stratification patterns in the UK population. We found that these patterns are indeed different. The top principal component extracted from the rare variant category shows poor correlations with any principal component or combination of principal components from the low frequency or common variant categories. These results could suggest that a suitable solution to avoid false positive association due to population stratification would involve adjusting for the respective PCs when testing for variants in different allele frequency categories. However, we found this was not the case both on type 2 diabetes data and on simulated data. Indeed, adjusting rare variant association tests on PCs derived from rare variants does no better to correct for population stratification than adjusting on PCs derived from more common variants. Mixed models perform slightly better for low frequency variants than PC based adjustments but less well for the rarest variants. These results call for the need of new methodological developments specifically devoted to address rare variant stratification issues in association tests.

Assessment of research models for testing gene-environment interactions

Throughout the last century, possible effects of exposure to toxicants, nutrients or drugs were examined primarily by studies of groups or populations. Individual variation in responses was acknowledged but could not be analyzed due to lack of information or tools to analyze individual genetic make-ups and lifestyle factors such as diet and activity. The Human Genome, Haplotype Map, 1000Genomes, and Human Variome Projects are identifying and cataloging the variation found within humans. Advances in DNA sequencing technologies will soon permit the characterization of individual genomes in clinical and basic research studies, thus allowing associations to be made between an individual genotype and the response to a particular exposure. Such knowledge and tools have generated a significant challenge for scientists: to design and conduct research studies that account for individual genetic variation. However, before these studies are done in humans, they will be performed in various in vivo and in vitro models. The advantages and disadvantages of some of the model test systems that are being used or developed in relation to individual genetic make-up and responses to xenobiotics are discussed.

Characteristics of patients prescribed sitagliptin and other oral antihyperglycaemic agents in a large US claims database

BACKGROUND AND AIMS

Non-randomised comparative studies of pharmacological agents can be biased because of differences in baseline demographics, medical history and health status of patients prescribed different therapies. Characteristics of patients with type 2 diabetes mellitus (T2DM) taking sitagliptin were compared with patients taking other oral antihyperglycaemic agents (OAHA) in a large US insurance claims database.

MATERIALS AND METHODS

Using the United Health Care database, we identified T2DM patients with at least one OAHA prescription, and at least 1 year prior enrollment. Patients were classified into subcohorts including sitagliptin or other OAHA, add-on to monotherapy, and triple or more therapy. Comorbidities 12 months before the first OAHA prescription in study window were based on ICD-9 diagnostic codes and NDC codes for prescriptions.

RESULTS

Prevalence of comorbidities was consistently higher for patients with sitagliptin prescriptions across most comorbidities (p < 0.05 for 20 of 30 assessed comorbidities). Overall, baseline differences were apparent (p < 0.0001) for retinopathy (5.7% vs. 3.4%), renal failure (5.1% vs. 2.6%), proteinuria (2.8% vs. 2.0%), hypertension (76.9% vs. 68.2%), congestive heart failure (3.4% vs. 2.6%), myocardial infarction (18.0% vs. 14.4%) and chronic neurological conditions (8.1% vs. 6.6%). Differences were most pronounced for initial monotherapy subcohorts. A higher proportion of sitagliptin users had prescriptions for cardiovascular medication (84.2% vs. 74.9%).

CONCLUSION

Sitagliptin users had higher proportions of comorbidities and greater use of prescription medications and physician visits. Researchers should be aware that sitagliptin is prescribed to patients with seemingly worse health status. Ability to analyse observational, non-randomised studies may be limited by substantial differences in patient characteristics between different treatments.

LINGO1 is not associated with Parkinson's disease in German patients

Essential tremor (ET) and Parkinson's disease (PD) are the most common movement disorders and show clinical, genetic, and pathophysiological overlap. Single-nucleotide polymorphisms (SNPs) in the leucine-rich repeat (LRR) and immunoglobulin (Ig) domain-containing, Nogo receptor-interacting protein gene (LINGO1) are associated with ET. LINGO1 is overexpressed in the substantia nigra (SN) of PD patients and inhibition of LINGO1 confers neuroprotection in a rodent model of PD. In this study we test the hypothesis whether SNPs in the LINGO1 gene that are associated with ET are also associated with PD. Three large German case-control samples from Kiel, Lübeck, and Tübingen (total: 1,798 cases and 1,482 controls) were genotyped for the three LINGO1 SNPs associated with ET. Association was assessed using allele- and genotype-based tests in each of the three samples separately, in the combined sample, and in subsets of patients with early-onset PD (<50 years) and of patients with a positive family history of PD. Neither of the three samples alone nor the combined sample showed evidence for association between LINGO1 SNPs and PD. The allele-based test showed a trend toward nominal association for all three SNPs in the Kiel sample. The subsets with early-onset PD or a positive family history did also not reveal evidence for association. SNPs in the LINGO1 gene associated with ET could not be shown to be associated with PD in our study population, despite a postulated overlap between both diseases.