Predicting long-term outcome after acute ischemic stroke: a simple index works in patients from controlled clinical trials

Background and Purpose—An early and reliable prognosis for recovery in stroke patients is important for initiation of individual treatment and for informing patients and relatives. We recently developed and validated models for predicting survival and functional independence within 3 months after acute stroke, based on age and the National Institutes of Health Stroke Scale score assessed within 6 hours after stroke. Herein we demonstrate the applicability of our models in an independent sample of patients from controlled clinical trials. Methods—The prognostic models were used to predict survival and functional recovery in 5419 patients from the Virtual International Stroke Trials Archive (VISTA). Furthermore, we tried to improve the accuracy by adapting intercepts and estimating new model parameters. Results—The original models were able to correctly classify 70.4% (survival) and 72.9% (functional recovery) of patients. Because the prediction was slightly pessimistic for patients in the controlled trials, adapting the intercept improved the accuracy to 74.8% (survival) and 74.0% (functional recovery). Novel estimation of parameters, however, yielded no relevant further improvement. Conclusions—For acute ischemic stroke patients included in controlled trials, our easy-to-apply prognostic models based on age and National Institutes of Health Stroke Scale score correctly predicted survival and functional recovery after 3 months. Furthermore, a simple adaptation helps to adjust for a different prognosis and is recommended if a large data set is available. (Stroke. 2008;39:000-000.

Machine learning and data mining in complex genomic data a review on the lessons learned in Genetic Analysis Workshop Nineteen

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data. In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets

Identifying Rare Variants from Exome Scans: The GAW17 Experience

Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the worksho

Machine Learning and Data Mining in Complex Genomic Data - A Review on the Lessons Learned in Genetic Analysis Workshop 19

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data. In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets

Biological effects of the PINK1 c.1366C>T mutation: implications in Parkinson disease pathogenesis.

PINK1 gene mutations are a cause of recessively inherited, early-onset Parkinson's disease. In some patients, a single heterozygous mutation has been identified, including the recurrent c.1366C>T transition. The interpretation of this finding remains controversial. Furthermore, the c.1366C>T mutation is associated with lower levels of PINK1 transcript, raising the question of whether mRNA levels correlate with the clinical status. We sequenced genomic DNA and copy DNA (cDNA) from 20 subjects carrying the c.1366C>T mutation in the homozygous (n = 5) or heterozygous (n = 15) state. In 17 mutation carriers, messenger RNA (mRNA) was quantified by real-time PCR using four different assays (PINK1 exon 5-6 or exon 7-8 relative to control genes SDHA or YWHAZ). Genomic sequencing confirmed the presence and zygosity of PINK1 mutations. cDNA sequencing in heterozygous mutation carriers revealed a strong wild-type and a much weaker or almost absent mutant signal, whereas in the homozygous patients, only the mutant signal was detected. Homozygous and heterozygous carriers showed PINK1 mRNA levels relative to a reference gene in the range of 0.1-0.2 and 0.5-0.6, respectively, compared with values of 0.9-1.0 in mutation-negative individuals. Treatment of lymphoblasts from a heterozygous mutation carrier with cycloheximide markedly increased the mutant transcript signal. We conclude that the recurrent PINK1 c.1366C>T mutation exerts a major effect at the mRNA level (80-90% reduction), most likely via nonsense-mediated mRNA decay. The absence of correlation between PINK1 mRNA levels and clinical status in heterozygous mutation carriers suggests that other genetic or environmental factors play a role in determining the phenotypic variability associated with the c.1366C>T mutation

The adenomatous polyposis Coli protein contributes to normal compaction of mitotic chromatin

The tumour suppressor Adenomatous Polyposis Coli (APC) is required for proper mitosis; however, the exact role of APC in mitosis is not understood. Using demembranated sperm chromatin exposed to meiotic Xenopus egg extract and HeLa cells expressing fluorescently labelled histones, we established that APC contributes to chromatin compaction. Sperm chromatin in APC-depleted Xenopus egg extract frequently formed tight round or elongated structures. Such abnormally compacted chromatin predominantly formed spindles with low microtubule content. Furthermore, in mitotic HeLa cells expressing GFP- and mCherry-labelled H2B histones, depletion of APC caused a decrease in the donor fluorescence lifetime of neighbouring fluorophores, indicative of excessive chromatin compaction. Profiling the chromatin-associated proteome of sperm chromatin incubated with Xenopus egg extracts revealed temporal APC-dependent changes in the abundance of histones, closely mirrored by chromatin-associated Topoisomerase IIa, condensin I complex and Kif4. In the absence of APC these factors initially accumulated on chromatin, but then decreased faster than in controls. We also found and validated significant APC-dependent changes in chromatin modifiers Set-a and Rbbp7. Both were decreased on chromatin in APC-depleted extract; in addition, the kinetics of association of Set-a with chromatin was altered in the absence of APC

Loss-of-Function Mutations in APOC3, Triglycerides, and Coronary Disease

Background Plasma triglyceride levels are heritable and are correlated with the risk of coronary heart disease. Sequencing of the protein-coding regions of the human genome (the exome) has the potential to identify rare mutations that have a large effect on phenotype. Methods We sequenced the protein-coding regions of 18,666 genes in each of 3734 participants of European or African ancestry in the Exome Sequencing Project. We conducted tests to determine whether rare mutations in coding sequence, individually or in aggregate within a gene, were associated with plasma triglyceride levels. For mutations associated with triglyceride levels, we subsequently evaluated their association with the risk of coronary heart disease in 110,970 persons. Results An aggregate of rare mutations in the gene encoding apolipoprotein C3 (APOC3) was associated with lower plasma triglyceride levels. Among the four mutations that drove this result, three were loss-of-function mutations: a nonsense mutation (R19X) and two splice-site mutations (IVS2+1G -> A and IVS3+1G -> T). The fourth was a missense mutation (A43T). Approximately 1 in 150 persons in the study was a heterozygous carrier of at least one of these four mutations. Triglyceride levels in the carriers were 39% lower than levels in noncarriers (P<1x10(-20)), and circulating levels of APOC3 in carriers were 46% lower than levels in noncarriers (P = 8x10(-10)). The risk of coronary heart disease among 498 carriers of any rare APOC3 mutation was 40% lower than the risk among 110,472 noncarriers (odds ratio, 0.60; 95% confidence interval, 0.47 to 0.75; P = 4x10(-6)). Conclusions Rare mutations that disrupt APOC3 function were associated with lower levels of plasma triglycerides and APOC3. Carriers of these mutations were found to have a reduced risk of coronary heart disease. (Funded by the National Heart, Lung, and Blood Institute and others.

Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants.

We conducted a genome-wide association study testing single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) for association with early-onset myocardial infarction in 2,967 cases and 3,075 controls. We carried out replication in an independent sample with an effective sample size of up to 19,492. SNPs at nine loci reached genome-wide significance: three are newly identified (21q22 near MRPS6-SLC5A3-KCNE2, 6p24 in PHACTR1 and 2q33 in WDR12) and six replicated prior observations (9p21, 1p13 near CELSR2-PSRC1-SORT1, 10q11 near CXCL12, 1q41 in MIA3, 19p13 near LDLR and 1p32 near PCSK9). We tested 554 common copy number polymorphisms (>1% allele frequency) and none met the pre-specified threshold for replication (P < 10(-3)). We identified 8,065 rare CNVs but did not detect a greater CNV burden in cases compared to controls, in genes compared to the genome as a whole, or at any individual locus. SNPs at nine loci were reproducibly associated with myocardial infarction, but tests of common and rare CNVs failed to identify additional associations with myocardial infarction risk

Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants

We conducted a genome-wide association study testing single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) for association with early-onset myocardial infarction in 2,967 cases ad 3,075 controls. We carried out replication in an independent sample with an effective sample size of up to 19,492. SNPs at nine loci reached genome-wide significance: three are newly identified (21q22 near MRPS6-SLC5A3-KCNE2, 6p24 in PHACTR1 and 2q33 in WDR12) and six replicated prior observations(1-4) (9p21, 1p13 near CERSL2-PSRC1-SORT1, 10q11 near CXCL12, 1q41 in MIA3, 19p13 near LDLR and 1p32 near PCSK9). We tested 554 common copy number polymorphisms (> 1% allele frequency) and none met the pre-specified threshold for replication (P < 10(-3)). We identified 8,065 rare CNVs but did not detect a greater CNV burden in cases compared to controls, in genes compared to the genome as a whole, or at any individual locus. SNPs at nine loci were reproducibly associated with myocardial infarction, but tests of common and rare CNVs failed to identify additional associations with myocardial infarction risk

Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture

Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups