Investigation of obstructive sleep apnea using nonlinear mode interations in nonstationary snore signals

Acoustic studies on snoring sounds have recently drawn attention as a potential alternative to polysomnography in the diagnosis of obstructive sleep apnea (OSA). This paper investigates the feasibility of using nonlinear coupling between frequency modes in snore signals via wavelet bicoherence (WBC) analysis for screening of OSA. Two novel markers (PF1 and PSF), which are frequency modes with high nonlinear coupling strength in their respective WBC spectrum, are proposed to differentiate between apneic and benign snores in same- or both-gender snorers. Snoring sounds were recorded from 40 subjects (30 apneic and 10 benign) by a hanging microphone, and subsequently preprocessed within a wavelet transform domain. Forty inspiratory snores (30 as training and 10 as test data) from each subject were examined. Results demonstrate that nonlinear mode interactions in apneic snores are less self-coupled and usually occupy higher and wider frequency ranges than that of benign snores. PF1 and PSF are indicative of apneic and benign snores (p < 0.0001), with optimal thresholds of PF1 = 285 Hz and PSF = 492 Hz (for both genders combined), as well as sensitivity and specificity values between 85.0 and 90.7%, respectively, outperforming the conventional diagnostic indicator (spectral peak frequency, PF = 243-275 Hz, sensitivity = 77.7-79.7%, specificity = 72.0-78.0%, p < 0.0001). Relationships between apnea-hypopnea index and the proposed markers could likely take the functional form of exponential or power. Perspectives on nonlinear dynamics analysis of snore signals are promising for further research and development of a reliable and inexpensive diagnostic tool for OSA

A review on the current research trends in ductile regime machining

10.1007/s00170-012-3949-yInternational Journal of Advanced Manufacturing Technology635-8465-480IJAT

LD Score regression distinguishes confounding from polygenicity in genome-wide association studies

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size

Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1 7 enrichment; p = 3.7 7 10(-17)) and 38% (SE =4%) of hg(2) from genotyped SNPs (1.6 7 enrichment, p = 1.0 7 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg(2) despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease

Genome-wide association study reveals greater polygenic loading for schizophrenia in cases with a family history of illness

Genome-wide association studies (GWAS) of schizophrenia have yielded more than 100 common susceptibility variants, and strongly support a substantial polygenic contribution of a large number of small allelic effects. It has been hypothesized that familial schizophrenia is largely a consequence of inherited rather than environmental factors. We investigated the extent to which familiality of schizophrenia is associated with enrichment for common risk variants detectable in a large GWAS. We analyzed single nucleotide polymorphism (SNP) data for cases reporting a family history of psychotic illness (N\u2009=\u2009978), cases reporting no such family history (N\u2009=\u20094,503), and unscreened controls (N\u2009=\u20098,285) from the Psychiatric Genomics Consortium (PGC1) study of schizophrenia. We used a multinomial logistic regression approach with model-fitting to detect allelic effects specific to either family history subgroup. We also considered a polygenic model, in which we tested whether family history positive subjects carried more schizophrenia risk alleles than family history negative subjects, on average. Several individual SNPs attained suggestive but not genome-wide significant association with either family history subgroup. Comparison of genome-wide polygenic risk scores based on GWAS summary statistics indicated a significant enrichment for SNP effects among family history positive compared to family history negative cases (Nagelkerke's R(2\u2009) =\u20090.0021; P\u2009=\u20090.00331; P-value threshold <0.4). Estimates of variability in disease liability attributable to the aggregate effect of genome-wide SNPs were significantly greater for family history positive compared to family history negative cases (0.32 and 0.22, respectively; P\u2009=\u20090.031). We found suggestive evidence of allelic effects detectable in large GWAS of schizophrenia that might be specific to particular family history subgroups. However, consideration of a polygenic risk score indicated a significant enrichment among family history positive cases for common allelic effects. Familial illness might, therefore, represent a more heritable form of schizophrenia, as suggested by previous epidemiological studies