Associations Between Lipoprotein(a) Levels and Cardiovascular Outcomes in Black and White Subjects: The Atherosclerosis Risk in Communities (ARIC) Study

Based on studies with limited statistical power, lipoprotein(a) [Lp(a)] is not considered a risk factor for cardiovascular disease (CVD) in African Americans. We evaluated associations between Lp(a) and incident CVD events in African Americans and Caucasians in the Atherosclerosis Risk in Communities (ARIC) study

Genotype-informed estimation of risk of coronary heart disease based on genome-wide association data linked to the electronic medical record

<p>Abstract</p> <p>Background</p> <p>Susceptibility variants identified by genome-wide association studies (GWAS) have modest effect sizes. Whether such variants provide incremental information in assessing risk for common 'complex' diseases is unclear. We investigated whether measured and imputed genotypes from a GWAS dataset linked to the electronic medical record alter estimates of coronary heart disease (CHD) risk.</p> <p>Methods</p> <p>Study participants (<it>n </it>= 1243) had no known cardiovascular disease and were considered to be at high, intermediate, or low 10-year risk of CHD based on the Framingham risk score (FRS) which includes age, sex, total and HDL cholesterol, blood pressure, diabetes, and smoking status. Of twelve SNPs identified in prior GWAS to be associated with CHD, four were genotyped in the participants as part of a GWAS. Genotypes for seven SNPs were imputed from HapMap CEU population using the program MACH. We calculated a multiplex genetic risk score for each patient based on the odds ratios of the susceptibility SNPs and incorporated this into the FRS.</p> <p>Results</p> <p>The mean (SD) number of risk alleles was 12.31 (1.95), range 6-18. The mean (SD) of the weighted genetic risk score was 12.64 (2.05), range 5.75-18.20. The CHD genetic risk score was not correlated with the FRS (<it>P </it>= 0.78). After incorporating the genetic risk score into the FRS, a total of 380 individuals (30.6%) were reclassified into higher-(188) or lower-risk groups (192).</p> <p>Conclusion</p> <p>A genetic risk score based on measured/imputed genotypes at 11 susceptibility SNPs, led to significant reclassification in the 10-y CHD risk categories. Additional prospective studies are needed to assess accuracy and clinical utility of such reclassification.</p

Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals

Background The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). Results Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. Conclusions These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication

A comparison of genomic profiles of complex diseases under different models

Background: Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task. Results: We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium. Conclusions: In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research.This work was supported by the Spanish Secretary of Research, Development and Innovation [TIN2010-20900-C04-1]; the Spanish Health Institute Carlos III [PI13/02714]and [PI13/01527] and the Andalusian Research Program under project P08-TIC-03717 with the help of the European Regional Development Fund (ERDF). The authors are very grateful to the reviewers, as they believe that their comments have helped to substantially improve the quality of the paper

Demonstration of Protein-Based Human Identification Using the Hair Shaft Proteome

YesHuman identification from biological material is largely dependent on the ability to characterize genetic polymorphisms in DNA. Unfortunately, DNA can degrade in the environment, sometimes below the level at which it can be amplified by PCR. Protein however is chemically more robust than DNA and can persist for longer periods. Protein also contains genetic variation in the form of single amino acid polymorphisms. These can be used to infer the status of non-synonymous single nucleotide polymorphism alleles. To demonstrate this, we used mass spectrometry-based shotgun proteomics to characterize hair shaft proteins in 66 European-American subjects. A total of 596 single nucleotide polymorphism alleles were correctly imputed in 32 loci from 22 genes of subjects’ DNA and directly validated using Sanger sequencing. Estimates of the probability of resulting individual non-synonymous single nucleotide polymorphism allelic profiles in the European population, using the product rule, resulted in a maximum power of discrimination of 1 in 12,500. Imputed non-synonymous single nucleotide polymorphism profiles from European–American subjects were considerably less frequent in the African population (maximum likelihood ratio = 11,000). The converse was true for hair shafts collected from an additional 10 subjects with African ancestry, where some profiles were more frequent in the African population. Genetically variant peptides were also identified in hair shaft datasets from six archaeological skeletal remains (up to 260 years old). This study demonstrates that quantifiable measures of identity discrimination and biogeographic background can be obtained from detecting genetically variant peptides in hair shaft protein, including hair from bioarchaeological contexts.The Technology Commercialization Innovation Program (Contracts #121668, #132043) of the Utah Governors Office of Commercial Development, the Scholarship Activitie

Genetic Testing for Early Detection of Individuals at Risk of Coronary Heart Disease and Monitoring Response to Therapy: Challenges and Promises

Coronary heart disease (CHD) often presents suddenly with little warning. Traditional risk factors are inadequate to identify the asymptomatic high-risk individuals. Early identification of patients with subclinical coronary artery disease using noninvasive imaging modalities would allow the early adoption of aggressive preventative interventions. Currently, it is impractical to screen the entire population with noninvasive coronary imaging tools. The use of relatively simple and inexpensive genetic markers of increased CHD risk can identify a population subgroup in which benefit of atherosclerotic imaging modalities would be increased despite nominal cost and radiation exposure. Additionally, genetic markers are fixed and need only be measured once in a patient’s lifetime, can help guide therapy selection, and may be of utility in family counseling

Phenotypic expansion of the BPTF-related neurodevelopmental disorder with dysmorphic facies and distal limb anomalies

Neurodevelopmental disorder with dysmorphic facies and distal limb anomalies (NEDDFL), defined primarily by developmental delay/intellectual disability, speech delay, postnatal microcephaly, and dysmorphic features, is a syndrome resulting from heterozygous variants in the dosage-sensitive bromodomain PHD finger chromatin remodeler transcription factor BPTF gene. To date, only 11 individuals with NEDDFL due to de novo BPTF variants have been described. To expand the NEDDFL phenotypic spectrum, we describe the clinical features in 25 novel individuals with 20 distinct, clinically relevant variants in BPTF, including four individuals with inherited changes in BPTF. In addition to the previously described features, individuals in this cohort exhibited mild brain abnormalities, seizures, scoliosis, and a variety of ophthalmologic complications. These results further support the broad and multi-faceted complications due to haploinsufficiency of BPTF.Genetics of disease, diagnosis and treatmen