DNA Copy number variation (CNV) has recently gained considerable interest as a source of genetic variation that likely influences phenotypic differences. Many statistical and computational methods have been proposed and applied to detect CNVs based on data that generated by genome analysis platforms. However, most algorithms are computationally intensive with complexity at least O(n2), where n is the number of probes in the experiments. Moreover, the theoretical properties of those existing methods are not well understood. A faster and better characterized algorithm is desirable for the ultra high throughput data. In this study, we propose the Screening and Ranking algorithm (SaRa) which can detect CNVs fast and accurately with complexity down to O(n). In addition, we characterize theoretical properties and present numerical analysis for our algorithm.
Change-point detection; copy number variations; high dimensional data; screening and ranking algorithm
Acupuncture is an alternative therapy to induce ovulation in women with polycystic ovary syndrome (PCOS), but there is no study reporting the live birth rate following ovulation induction by acupuncture or its potential as an adjuvant treatment to clomiphene citrate (CC). We assess the efficacy of acupuncture with or without CC in achieving live births among 1000 PCOS women in Mainland China. This paper reports the methodology of an ongoing multicenter randomized controlled trial. The randomization scheme is coordinated through the central mechanism and stratified by the participating sites. Participants will be randomized into one of the four treatment arms: (A) true acupuncture and CC, (B) control acupuncture and CC, (C) true acupuncture and placebo CC, and (D) control acupuncture and placebo CC. To ensure the quality and integrity of the trial we have developed a unique multinational team of investigators and Data and Safety Monitoring Board. Up to the end of April 2013, 326 subjects were recruited. In conclusion, the success of this trial will allow us to evaluate the additional benefit of acupuncture beyond the first line medicine for fertility treatment in PCOS women in an unbiased manner.
Infertility afflicts fifteen percent of couples who wish to conceive. Despite intensive evaluation of both male and female partners, the etiology may remain unknown leading to a diagnosis of unexplained infertility. For such couples, treatment often entails ovulation induction (OI) with fertility medications coupled with intrauterine insemination. Complications of this therapy include ovarian hyperstimulation syndrome and creation of multiple gestation pregnancies, which can be complicated by preterm labor and delivery, and the associated neonatal morbidity and expense of care for preterm infants. The Assessment of Multiple Intrauterine Gestations from Ovarian Stimulation (AMIGOS) study is designed to assess whether OI in couples with unexplained infertility with an aromatase inhibitor produces mono-follicular development in most cycles, thereby reducing multiple gestations while maintaining a comparable pregnancy success rate to that achieved by OI with either gonadotropins or clomiphene citrate. These results will provide future guidance of therapy for couples with unexplained infertility, and if comparable pregnancy rates are achieved with a substantial reduction in multiple gestations, the public health benefit will be considerable.
Multiple gestation; ovulation induction; gonadotropins; aromatase inhibitor; unexplained infertility
Many women with polycystic ovary syndrome (PCOS) experience infertility and hirsutism and often seek treatment for both concurrently. We investigated whether women who ovulate in response to treatment with clomiphene citrate), metformin, or both would have greater improvement in hirsutism compared to those who did not ovulate.
This is a secondary analysis evaluating the change in Ferriman-Gallwey score for the hirsute women (n = 505, 80.7%) from the Pregnancy in Polycystic Ovary Syndrome 1 study. This was a prospective, randomized, doubled-blind trial of 626 women with PCOS and infertility recruited from 12 university sites. They were treated with clomiphene citrate, metformin, or both (combination) for up to six cycles, and hirsutism evaluators were blinded to group assignment.
There was a significant decrease in the Ferriman-Gallwey score between baseline and completion of the study in each of the three individual groups (clomiphene citrate, p=0.024; metformin, p=0.005; combination, p<0.001). There was no significant difference in the degree to which the hirsutism score changed when comparing the three groups (p=0.44). The change in hirsutism was not associated with the duration of treatment or with the presence or absence of ovulation.
In infertile hirsute women with PCOS, treatment with clomiphene citrate, metformin, or both for up to 6 cycles does not alter hirsutism.
Clinical Trial Registration
ClinicalTrials.gov, www.clinicaltrials.gov, NCT00068861.
The present study searched for replicable risk genomic regions for alcohol and nicotine co-dependence using a genome-wide association strategy. The data contained a total of 3,143 subjects including 818 European-American (EA) cases with alcohol and nicotine co-dependence, 1,396 EA controls, 449 African-American (AA) cases and 480 AA controls. We performed separate genome-wide association analyses in EAs and AAs and a meta-analysis to derive combined p values, and calculated the genome-wide false discovery rate (FDR) for each SNP. Regions with p<5×10-7 together with FDR<0.05 in the meta-analysis were examined to detect all replicable risk SNPs across EAs, AAs and meta-analysis. These SNPs were followed with a series of functional expression quantitative trait locus (eQTL) analyses. We found a unique genome-wide significant gene region – SH3BP5-NR2C2 – that was enriched with 11 replicable risk SNPs for alcohol and nicotine co-dependence. The distributions of -log(p) values for all SNP-disease associations within this region were consistent across EAs, AAs, and meta-analysis (0.315≤r≤0.868; 8.1×10-52≤p≤3.6×10-5). In the meta-analysis, this region was the only association peak throughout chromosome 3 at p<0.0001. All replicable risk markers available for eQTL analysis had nominal cis- and trans-acting regulatory effects on gene expression. The transcript expression of the genes in this region was regulated partly by several nicotine dependence-related genes and significantly correlated with transcript expression of many alcohol and nicotine dependence-related genes. We concluded that the SH3BP5-NR2C2 region on Chromosome 3 might harbor causal loci for alcohol and nicotine co-dependence.
GWAS; alcohol and nicotine co-dependence
Polycystic Ovary Syndrome (PCOS) is a common cause of female infertility and first line treatment is currently oral clomiphene citrate, a selective estrogen receptor modulator, which results in both a high nonresponse rate and multiple pregnancy rate. Aromatase inhibitors such as letrozole may have more favorable ovarian and endometrial effects. The goal of the Pregnancy in Polycystic Ovary Syndrome II (PPCOSII) study is to determine the safety and efficacy of clomiphene citrate (CC) compared to letrozole, in achieving live birth in infertile women with PCOS. The population will consist of 750 infertile women with PCOS. Additionally, the couple will have no other major infertility factor. This will be a multi-center, prospective, double-blind clinical trial of CC vs. letrozole for 5 treatment cycles (or approximately up to 25 weeks). The randomization scheme will be coordinated through the central data coordinating center (DCC) and the randomization is stratified by each participating site. After progestin withdrawal as needed, 750 women will be equally randomized to two different treatment arms: A) CC 50 mg every day for 5 days (day 3–7 of cycle), or B) letrozole 2.5 mg every day for 5 days (day 3–7 of cycle), for a total of 5 cycles or 25 weeks. The dose will be increased in subsequent cycles in both treatment groups for non-response or poor ovulatory response up to a maximum of 150 mg of CC a day (× 5 days) or 7.5 mg of letrozole a day (× 5 days). The primary analysis will use an intent-to-treat approach to examine differences in the live birth rate in the two treatment arms.
Polycystic Ovary Syndrome; Infertility; Ovulation Induction; Hyperandrogenism; Clomiphene Citrate; Letrozole
To identify risk factors for pregnancy outcomes in couples treated with intracervical or intrauterine insemination, with or without superovulation for unexplained or male-factor infertility. The treatment continued for four cycles unless pregnancy was achieved.
Secondary analysis of data from a randomized superovulation and intrauterine insemination trial.
Academic medical centers.
Out of 932 couples randomized to four treatment groups, 664 couples who had completed the lifestyle questionnaires were assessed for occurrence of pregnancy and live birth.
Main outcome measure(s)
pregnancy and live birth.
The pregnancy and live birth rates were significantly higher in couples in which the female partners reported that they had consumed coffee or tea in the past or drank alcoholic beverages in the past (past users) when compared to those who had never consumed coffee or tea (4.0, 1.6–10.2 for pregnancy; 3.1, 1.2–8.1 for live birth) or alcoholic beverages (1.9, 1.1–3.3 for pregnancy; 2.1, 1.2–3.7 for live birth) (data are adjusted odds ratio and 95% confidence interval). Past users also had significantly higher pregnancy and live birth rates than those who were currently consuming coffee or tea or alcoholic beverages. Demographic, occupational exposures and other lifestyle factors were not significant.
Couples in which the female partners drank coffee, tea, or alcoholic beverages in the past had higher pregnancy and live birth rates when compared to never or current users. When discontinuing these habits, they might have made other lifestyle changes to improve the pregnancy outcome.
Infertility; lifestyle; pregnancy; live birth; insemination; superovulation
Substance dependence is a complex environmental and genetic disorder with significant social and medical concerns. Understanding the etiology of substance dependence is imperative to the development of effective treatment and prevention strategies. To this end, substantial effort has been made to identify genes underlying substance dependence, and in recent years, genome-wide association studies (GWASs) have led to discoveries of numerous genetic variants for complex diseases including substance dependence. Most of the GWAS discoveries were only based on single nucleotide polymorphisms (SNPs) and a single dichotomized outcome. By employing both SNP- and gene-based methods of analysis, we identified a strong (odds ratio = 13.87) and significant (P value = 1.33E − 11) association of an SNP in the NCK2 gene on chromosome 2 with opiates addiction in African-origin men. Codependence analysis also identified a genome-wide significant association between NCK2 and comorbidity of substance dependence (P value = 3.65E − 08) in African-origin men. Furthermore, we observed that the association between the NCK2 gene (P value = 3.12E − 10) and opiates addiction reached the gene-based genome-wide significant level. In summary, our findings provided the first evidence for the involvement of NCK2 in the susceptibility to opiates addiction and further revealed the racial and gender specificities of its impact.
Identifying the risk factors for comorbidity is important in psychiatric research. Empirically, studies have shown that testing multiple, correlated traits simultaneously is more powerful than testing a single trait at a time in association analysis. Furthermore, for complex diseases, especially mental illnesses and behavioral disorders, the traits are often recorded in different scales such as dichotomous, ordinal and quantitative. In the absence of covariates, nonparametric association tests have been developed for multiple complex traits to study comorbidity. However, genetic studies generally contain measurements of some covariates that may affect the relationship between the risk factors of major interest (such as genes) and the outcomes. While it is relatively easy to adjust these covariates in a parametric model for quantitative traits, it is challenging for multiple complex traits with possibly different scales. In this article, we propose a nonparametric test for multiple complex traits that can adjust for covariate effects. The test aims to achieve an optimal scheme of adjustment by using a maximum statistic calculated from multiple adjusted test statistics. We derive the asymptotic null distribution of the maximum test statistic, and also propose a resampling approach, both of which can be used to assess the significance of our test. Simulations are conducted to compare the type I error and power of the nonparametric adjusted test to the unadjusted test and other existing adjusted tests. The empirical results suggest that our proposed test increases the power through adjustment for covariates when there exist environmental effects, and is more robust to model misspecifications than some existing parametric adjusted tests. We further demonstrate the advantage of our test by analyzing a data set on genetics of alcoholism.
Comorbidity; Environmental factor; Family-based association test; Maximum test statistic; Multiple traits; Ordinal traits
Many genetic association studies used single nucleotide polymorphisms (SNPs) data to identify genetic variants for complex diseases. Although SNP-based associations are most common in genome-wide association studies (GWAS), gene-based association analysis has received increasing attention in understanding genetic etiologies for complex diseases. While both methods have been used to analyze the same data, few genome-wide association studies compare the results or observe the connection between them. We performed a comprehensive analysis of the data from the Study of Addiction: Genetics and Environment (SAGE) and compared the results from the SNP-based and gene-based analyses. Our results suggest that the gene-based method complements the individual SNP-based analysis, and conceptually they are closely related. In terms of gene findings, our results validate many genes that were either reported from the analysis of the same dataset or based on animal studies for substance dependence.
To review reasons for suboptimal recruitment for a randomized controlled trial (RCT) of varicocelectomy vs. intrauterine insemination for treatment of male infertility, and suggest means to improve future study recruitment.
A survey of RMN participating sites.
The Reproductive Medicine Network.
Main Outcome Measures
Ascertain reasons for inadequate recruitment and suggest improvements for future varicocelectomy trails.
This study screened 7 and enrolled 3 couples with the first couple randomized on 6/30/2010. The study was subsequently stopped on 03/30/2011. The following themes were cited most frequently by sites and therefore determined to be most likely to have played a role in suboptimal recruitment: (1) men must be screened at the beginning of a couple's infertility evaluation, (2) inclusion of infertile women who have failed previous fertility interventions appeared to be associated with couple intolerance of a placebo arm, and (3) there appeared to be bias against the use of unstimulated IUI cycles, indicating a prejudicial preference for surgical intervention in the male partner.
Improved recruitment may be realized through screening infertile men as early as possible while minimizing study-related time commitments. Focused patient education may promote improved ‘equipoise’ and acceptance of a placebo arm in male infertility studies. Lastly, creative approaches to implementing varicocelectomy trials must be considered in addition to having a network of motivated researchers who carry a high volume of possible study participants, as screening of very large numbers may be needed to complete clinical trial enrollment. Clinicaltrials.gov: NCT00767338.
Recruitment; consent; randomization; accrual; enrollment; prospective; varicocele; varicocelectomy
The individual research group or independent investigator often requires access to samples from a unique well characterized subject population. Cohorts of such samples from a well-defined comparative population are rare and limited access can impede progress. This bottleneck can be removed by accessing the samples provided by biorepositories such as the NIH/NICHD Cooperative Reproductive Medicine Network (RMN) Biorepository (detailed in the accompanying manuscript in this issue. In those cases where the individual research group or independent investigator already has access to a unique population, comparisons between well-defined groups are often sought to contextualize the data. In both cases seamless integration of data resources associated with the samples is required to ensure optimal comparisons. At the most basic level this requires standardization of sample collection and storage, as well as a de-identified data base containing demographic, clinical, and laboratory values. To facilitate such interoperability, the reagents and protocols that have been adopted by the RMN Biorepository for the collection and storage of serum, blood, saliva and sperm are described.
biorepository; repository; Reproductive Medicine Network
Analysis of data from twin and family studies provides the foundation for studies of disease inheritance. The development of advanced theory and computational software for general linear models has generated considerable interest for using mixed-effect models to analyze twin and family data, as a computationally more convenient and theoretically more sound alternative to the classical structure equation modeling. Despite the long history of twin and family data analysis, some fundamental questions remain unanswered. We addressed two important issues. One is to determine the necessary and sufficient conditions for the identifiability in the mixed effects models for twin and family data. The other is to derive the asymptotic distribution of the likelihood ratio test, which is novel due to the fact that the standard regularity conditions are not satisfied. We considered a series of specific yet important examples in which we demonstrated how to formulate mixed-effect models to appropriately reflect the data, and our key idea is the use of the Cholesky decomposition. Finally, we applied our method and theory to provide a more precise estimate of the heritability of two data sets than the previously reported estimate.
Mixed-effects models; Parent-twin quartet; Likelihood ratio test; Cholesky decomposition; SAS PROC MIXED
Many clinical investigators feel that the burden of institutional review board (IRB) requirements has been consistently increasing over recent years, though there are few objective data describing these trends. Over a period of 7 years the Reproductive Medicine Network observed a significant increase in the size and requirements of IRB submissions, and significant variability of IRB performance in reviewing multicenter trials. These additional regulatory and administrative demands represent substantial burdens to researchers and to the IRBs themselves. It is timely to consider whether these changes better protect the interests and safety of human research participants.
multicenter clinical trials; ethical review; institutional review boards; human experimentation
We used longitudinal magnetic resonance imaging (MRI) data to determine whether there are any gender differences in grey matter atrophy patterns over time in 197 individuals with probable Alzheimer’s disease (AD) and 266 with amnestic mild cognitive impairment (aMCI), compared with 224 healthy controls participating in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). While previous research has differentiated probable AD and aMCI groups from controls in brain atrophy, it is unclear whether and how sex plays a role in patterns of change over time. Using regional volumetric maps, we fit longitudinal models to the grey matter data collected at repeated occasions, seeking differences in patterns of volume change over time by sex and diagnostic group in a voxel-wise analysis. Additionally, using a region-of-interest approach, we fit longitudinal models to the global volumetric data of predetermined brain regions to determine whether this more conventional approach is sufficient for determining sex and group differences in atrophy. Our longitudinal analyses revealed that, of the various grey matter regions investigated, males and females in the AD group and the aMCI group showed different patterns of decline over time compared to controls in the bilateral precuneus, bilateral caudate nucleus, right entorhinal gyrus, bilateral thalamus, bilateral middle temporal gyrus, left insula, and right amygdala. As one of the first investigation to model more than two time points of structural MRI data over time, our findings add insight into how AD and aMCI males and females differ from controls and from each other over time.
Alzheimer’s disease; mild cognitive impairment; sex differences; longitudinal MRI; GEE
Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors.
rare variants; LASSO; machine learning; random forests; logic regression; binary trees; Poisson regression; ISIS; classification trees; meta-analysis; extreme sampling
Classification trees are non-parametric statistical learning methods that incorporate feature selection and interactions, possess intuitive interpretability, are efficient, and have high prediction accuracy when used in ensembles. This paper provides a brief introduction to the classification tree-based methods, a review of the recent developments, and a survey of the applications in bioinformatics and statistical genetics.
We evaluated the association of alcohol consumption and depression, and their effects on HIV disease progression among women with HIV. The study included 871 women with HIV who were recruited from 1993–1995 in four US cities. The participants had physical examination, medical record extraction, and venipuncture, CD4+ T-cell counts determination, measurement of depression symptoms (using the self-report Center for Epidemiological Studies-Depression Scale), and alcohol use assessment at enrollment, and semiannually until March 2000. Multilevel random coefficient ordinal models as well as multilevel models with joint responses were used in the analysis. There was no significant association between level of alcohol use and CD4+ T-cell counts. When participants were stratified by antiretroviral therapy (ART) use, the association between alcohol and CD4+ T-cell did not reach statistical significance. The association between alcohol consumption and depression was significant (p<0.001). Depression had a significant negative effect on CD4+ T-cell counts over time regardless of ART use. Our findings suggest that alcohol consumption has a direct association with depression. Moreover, depression is associated with HIV disease progression. Our findings have implications for the provision of alcohol use interventions and psychological resources to improve the health of women with HIV.
alcohol use; HIV/AIDS; multilevel longitudinal models; CD4+T-cells; depression
To assess the genetic contribution to late-onset sepsis in twins in the newborn intensive care unit (NICU).
A retrospective cohort analysis of twins born from 1994 to 2009 was performed on data collected from the NICUs at Yale University and the University of Connecticut. Sepsis concordance rates were compared between monozygotic and dizygotic twins. Mixed effects logistic regression (MELR) analysis was performed to determine the impact of selected non-genetic factors on late-onset sepsis. The influence of additive genetic and common and residual environmental effects were analyzed and quantified.
170 monozygotic and 665 dizygotic twin pairs were analyzed and sepsis identified in 8.9%. Mean gestational age and birth weight of the cohort was 31.1 weeks and 1637 grams, respectively. MELR determined birth weight (regression coefficient=−0.001; 95% CI: −0.003–0.000; p=0.028), respiratory distress syndrome (regression coefficient=1.769; 95% CI: 0.943–2.596; p<0.001) and duration of total parenteral nutrition (regression coefficient=0.041; 95% CI: 0.017–0.064; p<0.001) as significant non-genetic factors. Further analysis determined 49.0% (p=0.002) of the variance in liability to late-onset sepsis was due to genetic factors alone, and 51.0% (p=0.001) the result of residual environmental factors.
Our data support significant genetic susceptibility to late-onset sepsis in the NICU population.
premature newborn; infection; twins
Association analysis has led to identification of many genetic variants for complex diseases. While assessing the association between genes and a disease, other factors can play an important role. The consequence of not considering covariates (such as population stratification and environmental factors) is well-documented in genetic studies. We introduce a nonparametric test of association that adjusts for covariate effects. Specifically, the adjustment is realized through weights that are constructed from genomic propensity scores that summarize the contribution of all covariates. The benefit of our test is demonstrated through an important dataset on bipolar disorder (BD) collected by the Wellcome Trust Case Control Consortium (WTCCC). When compared to other tests, our test identified an un-reported region with three single nucleotide polymorphisms (SNPs) on chromosome 16 that show strong evidence of association (p-value < 5×10−7). This region is near the RPGRIP1L gene known to be associated with bipolar disorder. A haplotype block including these three SNPs was further discovered to be strongly associated with bipolar disorder. It is also interesting to note that our nonparametric test did not reveal strong signals at two SNPs that were detected by a covariate-adjusted parametric test. This suggests that different methods of covariate adjustment can complement each other. Thus, we recommend using both parametric and nonparametric testing. Additionally, we performed simulation studies to compare our proposed test with the unadjusted test and an adjusted parametric test. Our finding underscores the importance of accommodating and controlling for covariate effects in discovering genetic variants associated with complex disorders.
bipolar disorder; covariate adjustment; haplotype analysis; propensity score
Lactobacillus plantarum IMAU10014 was isolated from koumiss that produces a broad spectrum of antifungal compounds, all of which were active against plant pathogenic fungi in an agar plate assay. Two major antifungal compounds were extracted from the cell-free supernatant broth of L. plantarum IMAU10014. 3-phenyllactic acid and Benzeneacetic acid, 2-propenyl ester were carried out by HPLC, LC-MS, GC-MS, NMR analysis. It is the first report that lactic acid bacteria produce antifungal Benzeneacetic acid, 2-propenyl ester. Of these, the antifungal products also have a broad spectrum of antifungal activity, namely against Botrytis cinerea, Glomerella cingulate, Phytophthora drechsleri Tucker, Penicillium citrinum, Penicillium digitatum and Fusarium oxysporum, which was identified by the overlay and well-diffusion assay. F. oxysporum, P. citrinum and P. drechsleri Tucker were the most sensitive among molds.
Identifying the risk factors for mental illnesses is of significant public health importance. Diagnosis, stigma associated with mental illnesses, comorbidity, and complex etiologies, among others, make it very challenging to study mental disorders. Genetic studies of mental illnesses date back at least a century ago, beginning with descriptive studies based on Mendelian laws of inheritance. A variety of study designs including twin studies, family studies, linkage analysis, and more recently, genomewide association studies have been employed to study the genetics of mental illnesses, or complex diseases in general. In this paper, I will present the challenges and methods from a statistical perspective and focus on genetic association studies.
Comorbidity; Covariate adjusted association test; FBAT; Kendall’s tau; Multiple traits; Ordinal traits
Impaired consciousness requires altered cortical function. This can occur either directly from disorders that impair widespread bilateral regions of the cortex or indirectly through effects on subcortical arousal systems. It has therefore long been puzzling why focal temporal lobe seizures so often impair consciousness. Early work suggested that altered consciousness may occur with bilateral or dominant temporal lobe seizure involvement. However, other bilateral temporal lobe disorders do not impair consciousness. More recent work supports a ‘network inhibition hypothesis’ in which temporal lobe seizures disrupt brainstem–diencephalic arousal systems, leading indirectly to depressed cortical function and impaired consciousness. Indeed, prior studies show subcortical involvement in temporal lobe seizures and bilateral frontoparietal slow wave activity on intracranial electroencephalography. However, the relationships between frontoparietal slow waves and impaired consciousness and between cortical slowing and fast seizure activity have not been directly investigated. We analysed intracranial electroencephalography recordings during 63 partial seizures in 26 patients with surgically confirmed mesial temporal lobe epilepsy. Behavioural responsiveness was determined based on blinded review of video during seizures and classified as impaired (complex-partial seizures) or unimpaired (simple-partial seizures). We observed significantly increased delta-range 1–2 Hz slow wave activity in the bilateral frontal and parietal neocortices during complex-partial compared with simple-partial seizures. In addition, we confirmed prior work suggesting that propagation of unilateral mesial temporal fast seizure activity to the bilateral temporal lobes was significantly greater in complex-partial than in simple-partial seizures. Interestingly, we found that the signal power of frontoparietal slow wave activity was significantly correlated with the temporal lobe fast seizure activity in each hemisphere. Finally, we observed that complex-partial seizures were somewhat more common with onset in the language-dominant temporal lobe. These findings provide direct evidence for cortical dysfunction in the form of bilateral frontoparietal slow waves associated with impaired consciousness in temporal lobe seizures. We hypothesize that bilateral temporal lobe seizures may exert a powerful inhibitory effect on subcortical arousal systems. Further investigations will be needed to fully determine the role of cortical-subcortical networks in ictal neocortical dysfunction and may reveal treatments to prevent this important negative consequence of temporal lobe epilepsy.
cortex; EEG; seizures; temporal lobe epilepsy; consciousness
Genetic markers with rare variants are spread out in the genome, making it necessary and difficult to consider them in genetic association studies. Consequently, wisely combining rare variants into “composite” markers may facilitate meaningful analyses. In this paper, we propose a novel approach of analyzing rare variant data by incorporating the least absolute shrinkage and selection operator technique. We applied this method to the Genetic Analysis Workshop 17 data, and our results suggest that this new approach is promising. In addition, we took advantage of having 200 phenotype replications and assessed the performance of our approach by means of repeated classification tree analyses. Our method and analyses were performed without knowledge of the underlying simulating model. Our method identified 38 markers (in 65 genes) that are significantly associated with the phenotype Affected and correctly identified two causal genes, SIRT1 and PDGFD.
Existing methods for analyzing rare variant data focus on collapsing a group of rare variants into a single common variant; collapsing is based on an intuitive function of the rare variant genotype information, such as an indicator function or a weighted sum. It is more natural, however, to take into account the single-nucleotide polymorphism (SNP) interactions informed directly by the data. We propose a novel tree-based method that automatically detects SNP interactions and generates candidate markers from the original pool of rare variants. In addition, we utilize the advantage of having 200 phenotype replications in the Genetic Analysis Workshop 17 data to assess the candidate markers by means of repeated logistic regressions. This new approach shows potential in the rare variant analysis. We correctly identify the association between gene FLT1 and phenotype Affect, although there exist other false positives in our results. Our analyses are performed without knowledge of the underlying simulating model.