Mutation update for the SATB2 gene

SATB2-associated syndrome (SAS) is an autosomal dominant neurodevelopmental disorder caused by alterations in the SATB2 gene. Here we present a review of published pathogenic variants in the SATB2 gene to date and report 38 novel alterations found in 57 additional previously unreported individuals. Overall, we present a compilation of 120 unique variants identified in 155 unrelated families ranging from single nucleotide coding variants to genomic rearrangements distributed throughout the entire coding region of SATB2. Single nucleotide variants predicted to result in the occurrence of a premature stop codon were the most commonly seen (51/120=42.5%) followed by missense variants (31/120=25.8%). We review the rather limited functional characterization of pathogenic variants and discuss current understanding of the consequences of the different molecular alterations. We present an expansive phenotypic review along with novel genotype-phenotype correlations. Lastly, we discuss current knowledge on animal models and present future prospects. This review should help provide better guidance for the care of individuals diagnosed with SAS

The contribution of a 9p21.3 variant, a KIF6 variant, and C-reactive protein to predicting risk of myocardial infarction in a prospective study

<p>Abstract</p> <p>Background</p> <p>Genetic risk factors might improve prediction of coronary events. Several variants at chromosome 9p21.3 have been widely reported to be associated with coronary heart disease (CHD) in prospective and case-control studies. A variant of <it>KIF6 </it>(719Arg) has also been reported to be associated with increased risk of CHD in large prospective studies, but not in case-control studies. We asked whether the addition of genetic information (the 9p21.3 or <it>KIF6 </it>variants) or a well-established non-genetic risk factor (C-reactive protein [CRP]) can improve risk prediction by the Framingham Risk Score (FRS) in the Cardiovascular Health Study (CHS)--a prospective observational study of risk factors for cardiovascular disease among > 5,000 participants aged 65 or older.</p> <p>Methods</p> <p>Improvement of risk prediction was assessed by change in the area under the receiver-operator characteristic curve (AUC) and by net reclassification improvement (NRI).</p> <p>Results</p> <p>Among white participants the FRS was improved by addition of <it>KIF6 </it>719Arg carrier status among men as assessed by the AUC (from 0.581 to 0.596, P = 0.03) but not by NRI (NRI = 0.027, P = 0.32). Adding both CRP and 719Arg carrier status to the FRS improved risk prediction by the AUC (0.608, P = 0.02) and NRI (0.093, P = 0.008) in men, but not women (P ≥ 0.24).</p> <p>Conclusions</p> <p>While none of these risk markers individually or in combination improved risk prediction among women, a combination of <it>KIF6 </it>719Arg carrier status and CRP levels modestly improved risk prediction among white men; although this improvement is not significant after multiple-testing correction. These observations should be investigated in other prospective studies.</p

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

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

Sublinear Algorithms for Approximating String Compressibility

We raise the question of approximating the compressibility of a string with respect to a fixed compression scheme, in sublinear time. We study this question in detail for two popular lossless compression schemes: run-length encoding (RLE) and a variant of Lempel-Ziv (LZ77), and present sublinear algorithms for approximating compressibility with respect to both schemes. We also give several lower bounds that show that our algorithms for both schemes cannot be improved significantly. Our investigation of LZ77 yields results whose interest goes beyond the initial questions we set out to study. In particular, we prove combinatorial structural lemmas that relate the compressibility of a string with respect to LZ77 to the number of distinct short substrings contained in it (its ℓth subword complexity , for small ℓ). In addition, we show that approximating the compressibility with respect to LZ77 is related to approximating the support size of a distribution.National Science Foundation (U.S.) (Award CCF-1065125)National Science Foundation (U.S.) (Award CCF-0728645)Marie Curie International Reintegration Grant PIRG03-GA-2008-231077Israel Science Foundation (Grant 1147/09)Israel Science Foundation (Grant 1675/09

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

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

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