Genetic variants associated with type 2 diabetes and adiposity and risk of intracranial and abdominal aortic aneurysms.

Epidemiological studies show that type 2 diabetes (T2D) is inversely associated with intracranial aneurysms (IA) and abdominal aortic aneurysms (AAA). Although adiposity has not been considered a risk factor for IA, there have been inconsistent reports relating adiposity to AAA risk. We assessed whether these observations have a genetic, causal basis. To this end, we extracted genotypes of validated single-nucleotide polymorphisms associated with T2D (n=65), body mass index (BMI) (n=97) and waist-hip ratio adjusted for BMI (WHRadjBMI) (n=47) from genotype data collected in 717 IA cases and 1988 controls, and in 818 AAA cases and 3004 controls, all of Dutch descent. For each of these three traits, we computed genetic risk scores (GRS) for each individual in these case-control data sets by summing the number of risk alleles weighted by their published effect size, and tested whether these GRS were associated with risk of aneurysm. We divided the cohorts into GRS quartiles, and compared IA and AAA risk in the highest with the lowest GRS quartile using logistic regression. We found no evidence for association in IA or AAA risk between top and bottom quartiles for the genetic risk scores for T2D, BMI and WHRadjBMI. However, additional Mendelian randomization analyses suggested a trend to potentially causal associations between BMI and WHRadjBMI and risk of AAA. Overall, our results do not support epidemiological observations relating T2D to aneurysm risk, but may indicate a potential role of adiposity in AAA that requires further investigation.

Missing heritability: is the gap closing? An analysis of 32 complex traits in the Lifelines Cohort Study.

Despite the recent explosive rise in number of genetic markers for complex disease traits identified in genome-wide association studies, there is still a large gap between the known heritability of these traits and the part explained by these markers. To gauge whether this 'heritability gap' is closing, we first identified genome-wide significant SNPs from the literature and performed replication analyses for 32 highly relevant traits from five broad disease areas in 13 436 subjects of the Lifelines Cohort. Next, we calculated the variance explained by multi-SNP genetic risk scores (GRSs) for each trait, and compared it to their broad- and narrow-sense heritabilities captured by all common SNPs. The majority of all previously-associated SNPs (median=75%) were significantly associated with their respective traits. All GRSs were significant, with unweighted GRSs generally explaining less phenotypic variance than weighted GRSs, for which the explained variance was highest for height (15.5%) and varied between 0.02 and 6.7% for the other traits. Broad-sense common-SNP heritability estimates were significant for all traits, with the additive effect of common SNPs explaining 48.9% of the variance for height and between 5.6 and 39.2% for the other traits. Dominance effects were uniformly small (0-1.5%) and not significant. On average, the variance explained by the weighted GRSs accounted for only 10.7% of the common-SNP heritability of the 32 traits. These results indicate that GRSs may not yet be ready for accurate personalized prediction of complex disease traits limiting widespread adoption in clinical practice.

A framework for the detection of de novo mutations in family-based sequencing data.

Germline mutation detection from human DNA sequence data is challenging due to the rarity of such events relative to the intrinsic error rates of sequencing technologies and the uneven coverage across the genome. We developed PhaseByTransmission (PBT) to identify de novo single nucleotide variants and short insertions and deletions (indels) from sequence data collected in parent-offspring trios. We compute the joint probability of the data given the genotype likelihoods in the individual family members, the known familial relationships and a prior probability for the mutation rate. Candidate de novo mutations (DNMs) are reported along with their posterior probability, providing a systematic way to prioritize them for validation. Our tool is integrated in the Genome Analysis Toolkit and can be used together with the ReadBackedPhasing module to infer the parental origin of DNMs based on phase-informative reads. Using simulated data, we show that PBT outperforms existing tools, especially in low coverage data and on the X chromosome. We further show that PBT displays high validation rates on empirical parent-offspring sequencing data for whole-exome data from 104 trios and X-chromosome data from 249 parent-offspring families. Finally, we demonstrate an association between father's age at conception and the number of DNMs in female offspring's X chromosome, consistent with previous literature reports.

A genetic risk score is associated with statin-induced low-density lipoprotein cholesterol lowering.

AIM: To find new genetic loci associated with statin response, and to investigate the association of a genetic risk score (GRS) with this outcome. PATIENTS & METHODS: In a discovery meta-analysis (five studies, 1991 individuals), we investigated the effects of approximately 50000 single nucleotide polymorphisms on statin response, following up associations with p < 1 × 10(-4) (three independent studies, 5314 individuals). We further assessed the effect of a GRS based on SNPs in ABCG2, LPA and APOE. RESULTS: No new SNPs were found associated with statin response. The GRS was associated with reduced statin response: 0.0394 mmol/l per allele (95% CI: 0.0171-0.0617, p = 5.37 × 10(-4)). CONCLUSION: The GRS was associated with statin response, but the small effect size (˜2% of the average low-density lipoprotein cholesterol reduction) limits applicability.

The HLA-DQß1 insertion is a strong achalasia risk factor and displays a geospatial north-south gradient among Europeans.

Idiopathic achalasia is a severe motility disorder of the esophagus and is characterized by a failure of the lower esophageal sphincter to relax due to a loss of neurons in the myenteric plexus. Most recently, we identified an eight-amino-acid insertion in the cytoplasmic tail of HLA-DQß1 as strong achalasia risk factor in a sample set from Central Europe, Italy and Spain. Here, we tested whether the HLA-DQß1 insertion also confers achalasia risk in the Polish and Swedish population. We could replicate the initial findings and the insertion shows strong achalasia association in both samples (Poland P=1.84 × 10(-04), Sweden P=7.44 × 10(-05)). Combining all five European data sets - Central Europe, Italy, Spain, Poland and Sweden - the insertion is achalasia associated with Pcombined=1.67 × 10(-35). In addition, we observe that the frequency of the insertion shows a geospatial north-south gradient. The insertion is less common in northern (around 6-7% in patients and 2% in controls from Sweden and Poland) compared with southern Europeans (~16% in patients and 8% in controls from Italy) and shows a stronger attributable risk in the southern European population. Our study provides evidence that the prevalence of achalasia may differ between populations.

Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data.

BACKGROUND: Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed. METHODS: We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool. RESULTS: In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years). CONCLUSIONS: The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics.

Comparative modelling by restraint-based conformational sampling.

BACKGROUND: Although comparative modelling is routinely used to produce three-dimensional models of proteins, very few automated approaches are formulated in a way that allows inclusion of restraints derived from experimental data as well as those from the structures of homologues. Furthermore, proteins are usually described as a single conformer, rather than an ensemble that represents the heterogeneity and inaccuracy of experimentally determined protein structures. Here we address these issues by exploring the application of the restraint-based conformational space search engine, RAPPER, which has previously been developed for rebuilding experimentally defined protein structures and for fitting models to electron density derived from X-ray diffraction analyses. RESULTS: A new application of RAPPER for comparative modelling uses positional restraints and knowledge-based sampling to generate models with accuracies comparable to other leading modelling tools. Knowledge-based predictions are based on geometrical features of the homologous templates and rules concerning main-chain and side-chain conformations. By directly changing the restraints derived from available templates we estimate the accuracy limits of the method in comparative modelling. CONCLUSION: The application of RAPPER to comparative modelling provides an effective means of exploring the conformational space available to a target sequence. Enhanced methods for generating positional restraints can greatly improve structure prediction. Generation of an ensemble of solutions that are consistent with both target sequence and knowledge derived from the template structures provides a more appropriate representation of a structural prediction than a single model. By formulating homologous structural information as sets of restraints we can begin to consider how comparative models might be used to inform conformer generation from sparse experimental data.