Tear proteomic analysis of young glasses, orthokeratology, and soft contact lens wearers.

Contact lens-related ocular surface complications occur more often in teenagers and young adults. The purpose of this study was to determine changes in tear proteome of young patients wearing glasses (GL), orthokeratology lenses (OK), and soft contact lenses (SCL). Twenty-two young subjects (10-26 years of age) who were established GL, OK, and SCL wearers were recruited. Proteomic data were collected using a data-independent acquisition-parallel accumulation serial fragmentation workflow. In total, 3406 protein groups were identified, the highest number of proteins identified in Schirmer strip tears to date. Eight protein groups showed higher abundance, and 11 protein groups showed lower abundance in the SCL group compared to the OK group. In addition, the abundance of 82 proteins significantly differed in children compared to young adult GL wearers, among which 67 proteins were higher, and 15 proteins were lower in children. These 82 proteins were involved in inflammation, immune, and glycoprotein metabolic biological processes. In summary, this work identified over 3000 proteins in Schirmer Strip tears. The results indicated that tear proteomes were altered by orthokeratology and soft contact wear and age, which warrants further larger-scale study on the ocular surface responses of teenagers and young adults separately to contact lens wear. SIGNIFICANCE: In this work, we examined the tear proteomes of young patients wearing glasses, orthokeratology lenses, and soft contact lenses using a data-independent acquisition-parallel accumulation serial fragmentation (diaPASEF) workflow and identified 3406 protein groups in Schirmer strip tears. Nineteen protein groups showed significant abundance changes between orthokeratology and soft contact lens wearers. Moreover, eighty-two protein groups significantly differed in abundance in children and young adult glasses wearers. As a pilot study, this work provides a deep coverage of tear proteome and suggests the need to investigate ocular responses to contact lens wear separately for children and young adults.

Daily versus three-times-weekly azithromycin in Chinese patients with non-cystic fibrosis bronchiectasis: protocol for a prospective, open-label and randomised controlled trial.

INTRODUCTION: Non-cystic fibrosis bronchiectasis (NCFB) brought a heavy healthcare burden worldwide. Macrolide maintenance therapy was proved to be helpful in reducing exacerbation of NCFB. However, the optimal dosing regimens of macrolides have not been determined, and its efficacy in Chinese NCFB population has not been validated. This protocol describes a head-to-head clinical trial designed to compare the efficacy of two dosing regimens of azithromycin in Chinese NCFB population. METHODS AND ANALYSIS: This prospective, open-label and randomised controlled trial will be conducted in the First People's Hospital of Jiashan, China. Eligible patients with high-resolution CT defined NCFB will be randomly divided into three groups, which will receive either 250 mg daily azithromycin, or 500 mg three-times-weekly azithromycin or no treatment for 6 months. They will be followed up for another 6 months without treatment. The primary outcome is the mean rate of protocol-defined pulmonary exacerbation at 6 months. ETHICS AND DISSEMINATION: Ethical approval was obtained from the First People's Hospital of Jiashan Ethics Committee. The findings will be disseminated in peer-reviewed publications. TRIAL REGISTRATION NUMBER: ChiCTR2100052906.

Comprehensive spectral libraries for various rabbit eye tissue proteomes.

Rabbits have been widely used for studying ocular physiology and pathology due to their relatively large eye size and similar structures with human eyes. Various rabbit ocular disease models, such as dry eye, age-related macular degeneration, and glaucoma, have been established. Despite the growing application of proteomics in vision research using rabbit ocular models, there is no spectral assay library for rabbit eye proteome publicly available. Here, we generated spectral assay libraries for rabbit eye compartments, including conjunctiva, cornea, iris, retina, sclera, vitreous humor, and tears using fractionated samples and ion mobility separation enabling deep proteome coverage. The rabbit eye spectral assay library includes 9,830 protein groups and 113,593 peptides. We present the data as a freely available community resource for proteomic studies in the vision field. Instrument data and spectral libraries are available via ProteomeXchange with identifier PXD031194.

Variation of rhizosphere microbial community in continuous mono-maize seed production.

Soil microbe is crucial to a healthy soil, therefore its diversities and abundances under different conditions are still need fully understand.The aims of the study were to characterize the community structure and diversity of microbe in the rhizosphere soil after continuous maize seed production, and the relationship between the disease incidence of four diseases and the variation of the rhizosphere microbe. The results showed that different fungal and bacterial species were predominant in different cropping year, and long-term maize seed production had a huge impact on structure and diversity of soil microbial. Ascomycota and Mortierellomycota were the dominant fungal phyla and Mortierella and Ascomycetes represented for a large proportion of genus. A relative increase of Fusarium and Gibberella and a relative decrease of Mortierella, Chrysosporium, Podospora, and Chaetomium were observed with the increase of cropping year. Pathogenic Fusarium, Curvularia, Curvularia-lunata, Cladosporium, Gibberella-baccata, and Plectosphaerellaceae were over-presented and varied at different continuous cropping year, led to different maize disease incidence. Proteobacteria and Actinobacteria ranked in the top two of all bacterial phyla, and genus Pseudarthrobacter, Roseiflexus and RB41 dominated top 3. Haliangium and Streptomyces decreased with the continuous cropping year and mono-cropping of maize seed production increased disease incidence with the increase of cropping year, while the major disease was different. Continuous cropping of maize seed production induced the decrease of protective microbe and biocontrol genera, while pathogenic pathogen increased, and maize are in danger of pathogen invasion. Field management show great effects on soil microbial community.

A likelihood ratio test on temporal trends in age-period-cohort models with applications to the disparities of heart disease mortality among US populations and comparison with Japan.

In this article, we introduce the recently developed intrinsic estimator method in the age-period-cohort (APC) models in examining disease incidence and mortality data, further develop a likelihood ratio (L-R) test for testing differences in temporal trends across populations, and apply the methods to examining temporal trends in the age, period or calendar time, and birth cohort of the US heart disease mortality across racial and sex groups. The temporal trends are estimated with the intrinsic estimator method to address the model identification problem, in which multiple sets of parameter estimates yield the same fitted values for a given dataset, making it difficult to conduct comparison of and hypothesis testing on the temporal trends in the age, period, and cohort across populations. We employ a penalized profile log-likelihood approach in developing the L-R test to deal with the issues of multiple estimators and the diverging number of model parameters. The identification problem also induces overparametrization of the APC model, which requires a correction of the degree of freedom of the L-R test. Monte Carlo simulation studies demonstrate that the L-R test performs well in the Type I error calculation and is powerful to detect differences in the age or period trends. The L-R test further reveals disparities of heart disease mortality among the US populations and between the US and Japanese populations.

Prenatal and early-life exposure to the Great Chinese Famine increased the risk of tuberculosis in adulthood across two generations.

Global food security is a major driver of population health, and food system collapse may have complex and long-lasting effects on health outcomes. We examined the effect of prenatal exposure to the Great Chinese Famine (1958-1962)-the largest famine in human history-on pulmonary tuberculosis (PTB) across consecutive generations in a major center of ongoing transmission in China. We analyzed >1 million PTB cases diagnosed between 2005 and 2018 in Sichuan Province using age-period-cohort analysis and mixed-effects metaregression to estimate the effect of the famine on PTB risk in the directly affected birth cohort (F1) and their likely offspring (F2). The analysis was repeated on certain sexually transmitted and blood-borne infections (STBBI) to explore potential mechanisms of the intergenerational effects. A substantial burden of active PTB in the exposed F1 cohort and their offspring was attributable to the Great Chinese Famine, with more than 12,000 famine-attributable active PTB cases (>1.23% of all cases reported between 2005 and 2018). An interquartile range increase in famine intensity resulted in a 6.53% (95% confidence interval [CI]: 1.19-12.14%) increase in the ratio of observed to expected incidence rate (incidence rate ratio, IRR) in the absence of famine in F1, and an 8.32% (95% CI: 0.59-16.6%) increase in F2 IRR. Increased risk of STBBI was also observed in F2. Prenatal and early-life exposure to malnutrition may increase the risk of active PTB in the exposed generation and their offspring, with the intergenerational effect potentially due to both within-household transmission and increases in host susceptibility.

Total serum bilirubin levels and sensorineural hearing loss in the US adolescents: NHANES 2007-2010.

OBJECTIVE: We aimed to investigate whether current levels of total serum bilirubin are associated with different subtypes of sensorineural hearing loss (SNHL) in adolescents. METHODS: A set of cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) (2007-2010) was used. A subset of 1404 adolescents was sampled for measurements of total serum bilirubin, tympanometry, and average pure tone threshold at low-frequencies (LPTA: 500, 1000, 2000 Hz) or high-frequencies (HPTA: 3000, 4000, 6000, and 8000 Hz). SNHL was defined as the hearing loss that had type A tympanograms with a peak admittance of 0.3 ml or greater. Associations between serum bilirubin (square-root transformed) and different subtypes of SNHL were evaluated using binary or multinomial logistic regression models with 4-year sampling weights. The bootstrap method was used for estimation of variance and 10-fold cross-validation for assessment of overfitting issue. RESULTS: Total serum bilirubin levels were found to be associated with any high-frequency (HPTA>15 dB in at least one ear, adjusted odds-ratio (ORa)(bootstrap 95% confidence interval) = 3.29(1.31-8.19), p = 0.011), but not with any low-frequency (LPTA>15 dB in at least one ear), SNHL in the US adolescents. Furthermore, high-frequency SNHL with HPTA>15 dB in both ears (bilateral) or HPTA≥25 dB in at least one ear, compared to that with HPTA>15 dB in one ear only (unilateral) or HPTA = 15-25 dB in at least one ear, had a stronger association with total serum bilirubin levels (ORa = 5.37(1.27-22.65), p = 0.022 for bilateral; ORa = 2.64(0.84-8.25), p = 0.094 for unilateral; ORa = 5.00(0.95-26.58), p = 0.058 for HPTA≥25 dB in at least one ear; as well as ORa = 3.06(1.15-8.25), p = 0.025 for HPTA = 15-25 dB in at least one ear). No severe overfitting problems were found. CONCLUSION: Our findings suggest that current levels of total serum bilirubin may be informative in predicting and/or targeting high-frequency SNHL in the US adolescents.

Racial-Sex Disparities--A Challenging Battle Against Cancer Mortality in the USA.

Decline in US cancer mortality has recently been reported, based on either pooled mortality of all cancer sites or age-adjusted mortality rates of specific sites. While the former could be dominated by a few cancer sites and would not reflect that of other sites, the latter used the US 2000 Population as reference for age-standardization, which was lack of justification. This study aimed to examine US cancer mortality trend and disparities in sites, races, and sex. We studied cancer incidence-based mortality by race and sex from 1974 to 2008 of cervix, prostate, colon and rectum, lung, leukemia, liver, pancreas, and stomach in the Surveillance, Epidemiology, and End Results database. We developed a model-based mortality rate and examined rate ratio of each calendar period to the first period within each race-sex group. Cancer mortality of cervix, colon and rectum, leukemia, and stomach declined in all groups. Prostate cancer increased first in all racial groups and decreased thereafter at different pace. Lung cancer declined among males of all races but increased among females. Liver cancer increased steadily fast among white and black females, doubled in whites and black males, and climbed slowly in other races. Pancreas cancer declined among black males and females, and changed little among others. Cancer mortality trend presents heterogeneity across sites, races, and sex. Recently observed mortality decline may not reflect every cancer site or group. More effort needs to focus on specific race-sex groups that had increasing lung and liver cancer mortality.

A Single-Array-Based Method for Detecting Copy Number Variants Using Affymetrix High Density SNP Arrays and its Application to Breast Cancer.

Cumulative evidence has shown that structural variations, due to insertions, deletions, and inversions of DNA, may contribute considerably to the development of complex human diseases, such as breast cancer. High-throughput genotyping technologies, such as Affymetrix high density single-nucleotide polymorphism (SNP) arrays, have produced large amounts of genetic data for genome-wide SNP genotype calling and copy number estimation. Meanwhile, there is a great need for accurate and efficient statistical methods to detect copy number variants. In this article, we introduce a hidden-Markov-model (HMM)-based method, referred to as the PICR-CNV, for copy number inference. The proposed method first estimates copy number abundance for each single SNP on a single array based on the raw fluorescence values, and then standardizes the estimated copy number abundance to achieve equal footing among multiple arrays. This method requires no between-array normalization, and thus, maintains data integrity and independence of samples among individual subjects. In addition to our efforts to apply new statistical technology to raw fluorescence values, the HMM has been applied to the standardized copy number abundance in order to reduce experimental noise. Through simulations, we show our refined method is able to infer copy number variants accurately. Application of the proposed method to a breast cancer dataset helps to identify genomic regions significantly associated with the disease.

Catching the genomic wave in oligonucleotide single-nucleotide polymorphism arrays by modeling sequence binding.

The genomic wave has been identified as a major artifact in genome data and is highly correlated with the sequence GC content. Although statistical methods have been developed to filter this artifact, the mechanism underlying the genomic wave has not been studied yet. Understanding of the artifact, specifically the sources of the artifact, may lead to successful separation of biological signals from the artifact and improve array design, modeling, and association studies. We develop an approach to catching the genomic wave in the oligonucleotide single-nucleotide polymorphism (SNP) arrays by separating biological signals from the array baseline background through modeling sequence binding with a newly developed probe intensity composite representation (PICR) model. The PICR model decomposes the probe intensity of each SNP probe set into the target sequence concentrations, SNP-specific background (nonsignal) and measurement error, and identifies the biological signals through the target concentration for each allele. We demonstrate with the Affymetrix GeneChip 500K HapMap data and the Wellcome Trust Case-Control Study data that the genomic wave is captured through the SNP-specific background term of the PICR model, and is separated successfully from the allelic target concentrations-the biological signals. We further identify two important sources of the genomic waves, the GC content and the fragment length (FL) of the sequence, and conclude that (1) the genomic wave artifact can be removed from the genome data with the PICR model, and (2) in addition to the GC content, the genomic wave also has a component of nonlinear effect of the FL.

An imputation approach for oligonucleotide microarrays.

Oligonucleotide microarrays are commonly adopted for detecting and qualifying the abundance of molecules in biological samples. Analysis of microarray data starts with recording and interpreting hybridization signals from CEL images. However, many CEL images may be blemished by noises from various sources, observed as "bright spots", "dark clouds", and "shadowy circles", etc. It is crucial that these image defects are correctly identified and properly processed. Existing approaches mainly focus on detecting defect areas and removing affected intensities. In this article, we propose to use a mixed effect model for imputing the affected intensities. The proposed imputation procedure is a single-array-based approach which does not require any biological replicate or between-array normalization. We further examine its performance by using Affymetrix high-density SNP arrays. The results show that this imputation procedure significantly reduces genotyping error rates. We also discuss the necessary adjustments for its potential extension to other oligonucleotide microarrays, such as gene expression profiling. The R source code for the implementation of approach is freely available upon request.

Inflammation biomarkers in vaginal fluid and preterm delivery.

STUDY QUESTION: Which inflammation biomarkers detected in the vaginal fluid are most informative for identifying preterm delivery (PTD) risk? SUMMARY ANSWER: Elevated interleukin (IL)-6 at mid-trimester was associated with increased odds of spontaneous PTD at <35 weeks and with PTD plus histologic chorioamnionitis (HCA), and had the greatest sensitivity for detecting these two PTD subtypes. WHAT IS KNOWN ALREADY: Maternal and/or fetal inflammation play a role in some preterm deliveries, therefore inflammation biomarkers might help to identify women at greater risk. STUDY DESIGN, SIZE, DURATION: We examined 1115 women from the Pregnancy Outcomes and Community Health Study, a cohort study conducted from September 1998 through June 2004, for whom data were available on mid-pregnancy inflammatory biomarkers. PARTICIPANTS/MATERIALS, SETTING, METHODS: At enrollment at 16-27 weeks gestation, vaginal fluid samples were collected from a swab and 15 eluted biomarkers were measured using the Meso Scale Discovery multiplex electrochemiluminescence platform. Associations of biomarkers with PTD were examined, according to clinical circumstance, week at delivery and presence/absence of HCA. Weighted logistic regression was used to determine odds ratios (OR) and 95% confidence intervals (CI) adjusted for race. Sensitivity and specificity were compared between individual and multiple biomarkers, identified by a bootstrapping method. MAIN RESULTS AND THE ROLE OF CHANCE: Elevated IL-6 (>75th percentile) displayed the strongest association with spontaneous PTD <35 weeks (OR 2.3; CI 1.3-4.0) and PTD with HCA (OR 2.8; CI 1.4-6.0). The sensitivity of IL-6 to detect spontaneous PTD <35 weeks or PTD with HCA was 0.43 and 0.51, respectively, while specificity was 0.74 and 0.75, respectively. IL-6 plus IL1ß, IL-6r, tumor necrosis factor-alpha or granulocyte-macrophage colony-stimulating factor increased specificity (range 0.84-0.88), but decreased sensitivity (range 0.28-0.34) to detect both PTD subtypes. Results were similar when a combination of IL-6 and bacterial vaginosis (BV) was explored. Thus, the use of multiple biomarkers did not detect PTD subtypes with a greater sensitivity than IL-6 alone, and IL-6 is a specific but non-sensitive marker for the detection of spontaneous PTD. LIMITATIONS, REASONS FOR CAUTION: Our ability to find small effect size associations between PTD and inflammation biomarkers (OR <2.0) might have been limited by the modest number of less common PTD subtypes in our population (e.g. spontaneous delivery <35 weeks, PTD accompanied by HCA) and by relatively higher variability for some cytokines, for example tumor necrosis factor-α, IL-12p70, IL-10 and granulocyte-macrophage colony-stimulating factor, that are less stable and commonly undetectable or detectable at low levels in human vaginal secretions. WIDER IMPLICATIONS OF THE FINDINGS: Larger studies are needed to further explore a role of inflammation biomarkers in combination with other risk factors, including specific BV-associated organisms, for the prediction of PTD subtypes. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the National Institute of Child Health and Human Development, National Institute of Nursing, March of Dimes Foundation, Thrasher Research Foundation and Centers for Disease Control and Prevention. The authors have no conflicts of interest.

Gene expression in archived newborn blood spots distinguishes infants who will later develop cerebral palsy from matched controls.

BACKGROUND: Gene expression in archived newborn blood spots remaining from newborn screening may reflect pathophysiological disturbances useful in understanding the etiology of cerebral palsy (CP). METHODS: We quantified the expression of gene sets representing four physiological pathways hypothesized to contribute to CP in archived unfrozen residual newborn blood spot specimens from 53 children with CP and 53 age-, gender-, and gestational age-matched controls. We selected four empirical and three canonical gene sets representing the inflammatory, hypoxic, coagulative, and thyroidal pathways and examined mRNA expression using an 8 × 60,000 oligonucleotide microarray. The log2 fold change of gene expression between matched cases and controls was analyzed using the generally applicable gene set enrichment method. RESULTS: The empirical inflammatory and empirical hypoxic gene sets were significantly downregulated in term-born CP cases (n = 33) as compared with matched controls (P = 0.0007 and 0.0009, respectively), whereas both gene sets were significantly upregulated (P =0.0055 and 0.0223, respectively) in preterm-born CP cases (n = 20). The empirical thyroidal gene set was significantly upregulated in preterm-born CP cases (P = 0.0023). CONCLUSION: The newborn blood spot transcriptome can serve as a platform for investigating distinctive gene expression patterns in children who later develop CP.

Detecting genetic interactions for quantitative traits with U-statistics.

The genetic etiology of complex human diseases has been commonly viewed as a process that involves multiple genetic variants, environmental factors, as well as their interactions. Statistical approaches, such as the multifactor dimensionality reduction (MDR) and generalized MDR (GMDR), have recently been proposed to test the joint association of multiple genetic variants with either dichotomous or continuous traits. In this study, we propose a novel Forward U-Test to evaluate the combined effect of multiple loci on quantitative traits with consideration of gene-gene/gene-environment interactions. In this new approach, a U-Statistic-based forward algorithm is first used to select potential disease-susceptibility loci and then a weighted U-statistic is used to test the joint association of the selected loci with the disease. Through a simulation study, we found the Forward U-Test outperformed GMDR in terms of greater power. Aside from that, our approach is less computationally intensive, making it feasible for high-dimensional gene-gene/gene-environment research. We illustrate our method with a real data application to nicotine dependence (ND), using three independent datasets from the Study of Addiction: Genetics and Environment. Our gene-gene interaction analysis of 155 SNPs in 67 candidate genes identified two SNPs, rs16969968 within gene CHRNA5 and rs1122530 within gene NTRK2, jointly associated with the level of ND (P-value = 5.31e-7). The association, which involves essential interaction, is replicated in two independent datasets with P-values of 1.08e-5 and 0.02, respectively. Our finding suggests that joint action may exist between the two gene products.

The neuropathology of fatal cerebral malaria in malawian children.

We examined the brains of 50 Malawian children who satisfied the clinical definition of cerebral malaria (CM) during life; 37 children had sequestration of infected red blood cells (iRBCs) and no other cause of death, and 13 had a nonmalarial cause of death with no cerebral sequestration. For comparison, 18 patients with coma and no parasitemia were included. We subdivided the 37 CM cases into two groups based on the cerebral microvasculature pathology: iRBC sequestration only (CM1) or sequestration with intravascular and perivascular pathology (CM2). We characterized and quantified the axonal and myelin damage, blood-brain barrier (BBB) disruption, and cellular immune responses and correlated these changes with iRBC sequestration and microvascular pathology. Axonal and myelin damage was associated with ring hemorrhages and vascular thrombosis in the cerebral and cerebellar white matter and brainstem of the CM2 cases. Diffuse axonal and myelin damage were present in CM1 and CM2 cases in areas of prominent iRBC sequestration. Disruption of the BBB was associated with ring hemorrhages and vascular thrombosis in CM2 cases and with sequestration in both CM1 and CM2 groups. Monocytes with phagocytosed hemozoin accumulated within microvessels containing iRBCs in CM2 cases but were not present in the adjacent neuropil. These findings are consistent with a link between iRBC sequestration and intravascular and perivascular pathology in fatal pediatric CM, resulting in myelin damage, axonal injury, and breakdown of the BBB.

Actual causes of death in Chaoyang District of Beijing, China, 2007.

OBJECTIVES: To identify and quantify major external (non-genetic) factors that contribute to death in Chaoyang District of Beijing, China in 2007. METHODS: The death registration data reported to the Center of Disease Control and Prevention of Chaoyang District of Beijing, China, during the year 2007, were obtained. The analysis was conducted in 2009 using the health risk factors identified by the World Health Report 2002 and the population attributable fractions of mortality from Global burden of disease and risk factors. The estimates of actual causes of death attributable to each risk factor were calculated by multiplying the population attributable fractions of mortality by the corresponding number of deaths of the subgroup or total population. RESULTS: The five leading actual causes of death in Chaoyang District of Beijing, China in 2007 were high blood pressure (2159 deaths, 18%), smoking (990, 8%), low fruit and vegetable consumption (968, 8%), high cholesterol (891, 7%), and physical inactivity (629, 5%). The pattern and ordering of these leading causes vary with sex and age specific subgroups. CONCLUSIONS: More than half of the total number of deaths in Chaoyang District in 2007 could be attributed to a few major preventable risk factors. Although the study focused on only one district of Beijing in one single year, and is by no means comprehensive, its findings suggest that public health policies and programmes in China should address these public health concerns by focusing on these largely preventable risk factors for primary prevention.

MA-SNP--A new genotype calling method for oligonucleotide SNP arrays modeling the batch effect with a normal mixture model.

Genome-wide association studies hold great promise in identifying disease-susceptibility variants and understanding the genetic etiology of complex diseases. Microarray technology enables the genotyping of millions of single nucleotide polymorphisms. Many factors in microarray studies, such as probe selection, sample quality, and experimental process and batch, have substantial effect on the genotype calling accuracy, which is crucial for downstream analyses. Failure to account for the variability of these sources may lead to inaccurate genotype calls and false positive and false negative findings. In this study, we develop a SNP-specific genotype calling algorithm based on the probe intensity composite representation (PICR) model, while using a normal mixture model to account for the variability of batch effect on the genotype calls. We demonstrate our method with SNP array data in a few studies, including the HapMap project, the coronary heart disease and the UK Blood Service Control studies by the Wellcome Trust Case-Control Consortium, and a methylation profiling study. Our single array based approach outperforms PICR and is comparable to the best multi-array genotype calling methods.

An aggregating U-Test for a genetic association study of quantitative traits.

We propose a novel aggregating U-test for gene-based association analysis. The method considers both rare and common variants. It adaptively searches for potential disease-susceptibility rare variants and collapses them into a single "supervariant." A forward U-test is then used to assess the joint association of the supervariant and other common variants with quantitative traits. Using 200 simulated replicates from the Genetic Analysis Workshop 17 mini-exome data, we compare the performance of the proposed method with that of a commonly used approach, QuTie. We find that our method has an equivalent or greater power than QuTie to detect nine genes that influence the quantitative trait Q1. This new approach provides a powerful tool for detecting both common and rare variants associated with quantitative traits.

Mapping haplotype-haplotype interactions with adaptive LASSO.

BACKGROUND: The genetic etiology of complex diseases in human has been commonly viewed as a complex process involving both genetic and environmental factors functioning in a complicated manner. Quite often the interactions among genetic variants play major roles in determining the susceptibility of an individual to a particular disease. Statistical methods for modeling interactions underlying complex diseases between single genetic variants (e.g. single nucleotide polymorphisms or SNPs) have been extensively studied. Recently, haplotype-based analysis has gained its popularity among genetic association studies. When multiple sequence or haplotype interactions are involved in determining an individual's susceptibility to a disease, it presents daunting challenges in statistical modeling and testing of the interaction effects, largely due to the complicated higher order epistatic complexity. RESULTS: In this article, we propose a new strategy in modeling haplotype-haplotype interactions under the penalized logistic regression framework with adaptive L1-penalty. We consider interactions of sequence variants between haplotype blocks. The adaptive L1-penalty allows simultaneous effect estimation and variable selection in a single model. We propose a new parameter estimation method which estimates and selects parameters by the modified Gauss-Seidel method nested within the EM algorithm. Simulation studies show that it has low false positive rate and reasonable power in detecting haplotype interactions. The method is applied to test haplotype interactions involved in mother and offspring genome in a small for gestational age (SGA) neonates data set, and significant interactions between different genomes are detected. CONCLUSIONS: As demonstrated by the simulation studies and real data analysis, the approach developed provides an efficient tool for the modeling and testing of haplotype interactions. The implementation of the method in R codes can be freely downloaded from http://www.stt.msu.edu/~cui/software.html.