Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.Comment: 12 pages. Statistical Paper

Integrative genomic analysis identifies ancestry-related expression quantitative trait loci on DNA polymerase β and supports the association of genetic ancestry with survival disparities in head and neck squamous cell carcinoma

BACKGROUND: African Americans with head and neck squamous cell carcinoma (HNSCC) have a lower survival rate than whites. This study investigated the functional importance of ancestry-informative single-nucleotide polymorphisms (SNPs) in HNSCC and also examined the effect of functionally important genetic elements on racial disparities in HNSCC survival. METHODS: Ancestry-informative SNPs, RNA sequencing, methylation, and copy number variation data for 316 oral cavity and laryngeal cancer patients were analyzed across 178 DNA repair genes. The results of expression quantitative trait locus (eQTL) analyses were also replicated with a Gene Expression Omnibus (GEO) data set. The effects of eQTLs on overall survival (OS) and disease-free survival (DFS) were evaluated. RESULTS: Five ancestry-related SNPs were identified as cis-eQTLs in the DNA polymerase β (POLB) gene (false discovery rate [FDR] < 0.01). The homozygous/heterozygous genotypes containing the African allele showed higher POLB expression than the homozygous white allele genotype (P < .001). A replication study using a GEO data set validated all 5 eQTLs and also showed a statistically significant difference in POLB expression based on genetic ancestry (P = .002). An association was observed between these eQTLs and OS (P < .037; FDR < 0.0363) as well as DFS (P = .018 to .0629; FDR < 0.079) for oral cavity and laryngeal cancer patients treated with platinum-based chemotherapy and/or radiotherapy. Genotypes containing the African allele were associated with poor OS/DFS in comparison with homozygous genotypes harboring the white allele. CONCLUSIONS: Analyses show that ancestry-related alleles could act as eQTLs in HNSCC and support the association of ancestry-related genetic factors with survival disparities in patients diagnosed with oral cavity and laryngeal cancer. Cancer 2017;123:849-60. © 2016 American Cancer Society

Ancestral-derived effects on the mutational landscape of laryngeal cancer

© 2015 Elsevier Inc.Laryngeal cancer disproportionately affects more African-Americans than European-Americans. Here, we analyze the genome-wide somatic point mutations from the tumors of 13 African-Americans and 57 European-Americans from TCGA to differentiate between environmental and ancestrally-inherited factors. The mean number of mutations was different between African-Americans (151.31) and European-Americans (277.63). Other differences in the overall mutational landscape between African-American and European-American were also found. The frequency of C > A, and C > G were significantly different between the two populations (p-value < 0.05). Context nucleotide signatures for some mutation types significantly differ between these two populations. Thus, the context nucleotide signatures along with other factors could be related to the observed mutational landscape differences between two races. Finally, we show that mutated genes associated with these mutational differences differ between the two populations. Thus, at the molecular level, race appears to be a factor in the progression of laryngeal cancer with ancestral genomic signatures best explaining these differences

Behavioral Genetics: Investigating the genes of a complex phenotype in fruit flies

Synopsis: This laboratory exercise uses both inquiry-based and active-learning approaches to uncover the genetic architecture of behavior in the model organism, Drosophila melanogaster. The exercise can be performed in either a single two-hour or two 60-minute lab periods and requires access to computers with an internet connection to help introduce students to modern genetic and genomic analysis. Students first will quantify behavioral interactions associated with mating in wildtype fruit flies. They will then connect these phenotypic ontologies to individual candidate genes using curated data from Drosophila&apos;s model organism database, FlyBase. Students will explore known characteristics of chosen candidate genes including models of genic structure, genomic context, and known functional attributes including patterns of spatial and temporal gene expression. Introduction

Uji Kinerja Dan Analisis K-Support Vector Nearest Neighbor Terhadap Decision Tree dan Naive Bayes

Algoritma K-Support Vector Nearest Neighbor (K-SVNN) menjadi salah satu alternative metode hasil evolusi K-Nearest Neighbor (K-NN) yang bertujuan untuk mengurangi waktu yang digunakan pada saat prediksi tetapi diharapkan dapat tetap mempertahankan akurasi prediksi. Metode ini masih relatif muda sehingga baru dibandingkan hanya dengan metode-metode berbasis K-NN lainnya. Dalam penelitian ini dilakukan analisis perbandingan kesamaan, perbedaan, dan kinerja terhadap metode Decision Tree (DT) dan Naïve Bayes (NB). Pengujian dengan perbandingan ini penting untuk mengetahui keunggulan dan kelemahan relatif yang dimiliki oleh K-SVNN. Dengan mengetahui keunggulan dan kelemahan maka metode tersebut dapat dibuktikan baik tidaknya ketika diimplementasikan. Pengujian dilakukan baik pada saat pelatihan maupun prediksi. Kinerja pelatihan diukur dalam hal waktu yang digunakan untuk pelatihan, kinerja prediksi diukur dalam hal waktu yang digunakan untuk prediksi dan akurasi prediksi yang didapat. Hasil pengujian menunjukkan bahwa K-SVNN mempunyai akurasi yang lebih baik daripada DT dan NB. Sedangkan waktu yang digunakan untuk pelatihan dan prediksi K-SVNN lebih lama disbanding DT dan NB

The Genomics of Speciation in Drosophila: Diversity, Divergence, and Introgression Estimated Using Low-Coverage Genome Sequencing

In nature, closely related species may hybridize while still retaining their distinctive identities. Chromosomal regions that experience reduced recombination in hybrids, such as within inversions, have been hypothesized to contribute to the maintenance of species integrity. Here, we examine genomic sequences from closely related fruit fly taxa of the Drosophila pseudoobscura subgroup to reconstruct their evolutionary histories and past patterns of genic exchange. Partial genomic assemblies were generated from two subspecies of Drosophila pseudoobscura (D. ps.) and an outgroup species, D. miranda. These new assemblies were compared to available assemblies of D. ps. pseudoobscura and D. persimilis, two species with overlapping ranges in western North America. Within inverted regions, nucleotide divergence among each pair of the three species is comparable, whereas divergence between D. ps. pseudoobscura and D. persimilis in non-inverted regions is much lower and closer to levels of intraspecific variation. Using molecular markers flanking each of the major chromosomal inversions, we identify strong crossover suppression in F1 hybrids extending over 2 megabase pairs (Mbp) beyond the inversion breakpoints. These regions of crossover suppression also exhibit the high nucleotide divergence associated with inverted regions. Finally, by comparison to a geographically isolated subspecies, D. ps. bogotana, our results suggest that autosomal gene exchange between the North American species, D. ps. pseudoobscura and D. persimilis, occurred since the split of the subspecies, likely within the last 200,000 years. We conclude that chromosomal rearrangements have been vital to the ongoing persistence of these species despite recent hybridization. Our study serves as a proof-of-principle on how whole genome sequencing can be applied to formulate and test hypotheses about species formation in lesser-known non-model systems

Lack of association between polymorphisms of the IL18R1 and IL18RAP genes and cardiovascular risk: the MORGAM Project

<p>Abstract</p> <p>Background</p> <p>Interleukin-18 is a pro-inflammatory cytokine suspected to be associated with atherosclerosis and its complications. We had previously shown that one single nucleotide polymorphism (SNP) of the <it>IL18 </it>gene was associated with cardiovascular disease (CVD) through an interaction with smoking. As a further step for elucidating the contribution of the IL-18 pathway to the etiology of CVD, we here investigated the association between the genetic variability of two IL-18 receptor genes, <it>IL18R1 </it>and <it>IL18RAP</it>, with the risk of developing CVD.</p> <p>Methods</p> <p>Eleven tagging SNPs, 5 in <it>IL18R1 </it>and 6 in <it>IL18RAP</it>, characterizing the haplotypic variability of the corresponding genes; were genotyped in 5 European prospective CVD cohorts including 1416 cases and 1772 non-cases, as part of the MORGAM project. Both single-locus and haplotypes analyses were carried out to investigate the association of these SNPs with CVD.</p> <p>Results</p> <p>We did not find any significant differences in allele, genotype and haplotype frequencies between cases and non-cases for either of the two genes. Moreover, the search for interactions between SNPs located in different genes, including 5 <it>IL18 </it>SNPs previously studied in the MORGAM project, and between SNPs and environmental factors remained unfruitful.</p> <p>Conclusion</p> <p>Our analysis suggests that the variability of <it>IL18R1 </it>and <it>IL18RAP </it>genes are unlikely to contribute to modulate the risk of CVD.</p

Evaluating the use of ABBA-BABA statistics to locate introgressed loci

Several methods have been proposed to test for introgression across genomes. One method tests for a genome-wide excess of shared derived alleles between taxa using Patterson’s D statistic, but does not establish which loci show such an excess or whether the excess is due to introgression or ancestral population structure. Several recent studies have extended the use of D by applying the statistic to small genomic regions, rather than genome-wide. Here, we use simulations and whole-genome data from Heliconius butterflies to investigate the behavior of D in small genomic regions. We find that D is unreliable in this situation as it gives inflated values when effective population size is low, causing D outliers to cluster in genomic regions of reduced diversity. As an alternative, we propose a related statistic f ̂ d, a modified version of a statistic originally developed to estimate the genome-wide fraction of admixture. f ̂ d is not subject to the same biases as D, and is better at identifying introgressed loci. Finally, we show that both D and f ̂ d outliers tend to cluster in regions of low absolute divergence (dXY), which can confound a recently proposed test for differentiating introgression from shared ancestral variation at individual loci

Population Genomic Inferences from Sparse High-Throughput Sequencing of Two Populations of Drosophila melanogaster

Short-read sequencing techniques provide the opportunity to capture genome-wide sequence data in a single experiment. A current challenge is to identify questions that shallow-depth genomic data can address successfully and to develop corresponding analytical methods that are statistically sound. Here, we apply the Roche/454 platform to survey natural variation in strains of Drosophila melanogaster from an African (n = 3) and a North American (n = 6) population. Reads were aligned to the reference D. melanogaster genomic assembly, single nucleotide polymorphisms were identified, and nucleotide variation was quantified genome wide. Simulations and empirical results suggest that nucleotide diversity can be accurately estimated from sparse data with as little as 0.2× coverage per line. The unbiased genomic sampling provided by random short-read sequencing also allows insight into distributions of transposable elements and copy number polymorphisms found within populations and demonstrates that short-read sequencing methods provide an efficient means to quantify variation in genome organization and content. Continued development of methods for statistical inference of shallow-depth genome-wide sequencing data will allow such sparse, partial data sets to become the norm in the emerging field of population genomics

Determinants of successful clinical networks : The conceptual framework and study protocol

Background Clinical networks are increasingly being viewed as an important strategy for increasing evidence-based practice and improving models of care, but success is variable and characteristics of networks with high impact are uncertain. This study takes advantage of the variability in the functioning and outcomes of networks supported by the Australian New South Wales (NSW) Agency for Clinical Innovation's non-mandatory model of clinical networks to investigate the factors that contribute to the success of clinical networks. Methods/Design The objective of this retrospective study is to examine the association between external support, organisational and program factors, and indicators of success among 19 clinical networks over a three-year period (2006-2008). The outcomes (health impact, system impact, programs implemented, engagement, user perception, and financial leverage) and explanatory factors will be collected using a web-based survey, interviews, and record review. An independent expert panel will provide judgements about the impact or extent of each network's initiatives on health and system impacts. The ratings of the expert panel will be the outcome used in multivariable analyses. Following the rating of network success, a qualitative study will be conducted to provide a more in-depth examination of the most successful networks. Discussion This is the first study to combine quantitative and qualitative methods to examine the factors that contribute to the success of clinical networks and, more generally, is the largest study of clinical networks undertaken. The adaptation of expert panel methods to rate the impacts of networks is the methodological innovation of this study. The proposed project will identify the conditions that should be established or encouraged by agencies developing clinical networks and will be of immediate use in forming strategies and programs to maximise the effectiveness of such networks