Gene-based association tests in family samples using GWAS summary statistics.

Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.

A gene-based association test of interactions for maternal-fetal genotypes identifies genes associated with nonsyndromic congenital heart defects.

The risk of congenital heart defects (CHDs) may be influenced by maternal genes, fetal genes, and their interactions. Existing methods commonly test the effects of maternal and fetal variants one-at-a-time and may have reduced statistical power to detect genetic variants with low minor allele frequencies. In this article, we propose a gene-based association test of interactions for maternal-fetal genotypes (GATI-MFG) using a case-mother and control-mother design. GATI-MFG can integrate the effects of multiple variants within a gene or genomic region and evaluate the joint effect of maternal and fetal genotypes while allowing for their interactions. In simulation studies, GATI-MFG had improved statistical power over alternative methods, such as the single-variant test and functional data analysis (FDA) under various disease scenarios. We further applied GATI-MFG to a two-phase genome-wide association study of CHDs for the testing of both common variants and rare variants using 947 CHD case mother-infant pairs and 1306 control mother-infant pairs from the National Birth Defects Prevention Study (NBDPS). After Bonferroni adjustment for 23,035 genes, two genes on chromosome 17, TMEM107 (p = 1.64e-06) and CTC1 (p = 2.0e-06), were identified for significant association with CHD in common variants analysis. Gene TMEM107 regulates ciliogenesis and ciliary protein composition and was found to be associated with heterotaxy. Gene CTC1 plays an essential role in protecting telomeres from degradation, which was suggested to be associated with cardiogenesis. Overall, GATI-MFG outperformed the single-variant test and FDA in the simulations, and the results of application to NBDPS samples are consistent with existing literature supporting the association of TMEM107 and CTC1 with CHDs.

Allostatic load and risk of invasive breast cancer among postmenopausal women in the U.S.

OBJECTIVE: Allostatic load can reflect the body's response to chronic stress. However, little is known about the association between allostatic load and risk of breast cancer in postmenopausal women. This study used a large prospective cohort in the United States to examine the relationship between allostatic load and invasive breast cancer risk, and to evaluate the relationship by racial and ethnic identity and breast cancer subtypes. METHODS: Among 161,808 postmenopausal participants in Women's Health Initiative, eligible were a subsample of 27,393 postmenopausal women aged 50-79 years old, who enrolled from 1993 to 1998, had serum test biomarkers, and were followed for breast cancer incidence through February 2022. Allostatic load at enrollment was computed based on eight biomarkers from lab serum tests and a questionnaire about participants' prescription drug use. The associations between allostatic scores and risk of breast cancer (overall and by subtypes) were assessed using Cox proportional hazards models. The race and ethnic differences were examined. RESULTS: Over a median follow-up time of 17.24 years, 1722 invasive breast cancer cases were identified. High allostatic load was associated with an increased risk of breast cancer (HR = 1.36, 95%CI: 1.20, 1.54 for third tertile vs first tertile, Ptrend < 0.0001). Similar trends were found in White women and non-Hispanic women. Higher allostatic load was associated with hormone receptor-positive and HER2/Neu-negative breast cancer (HR = 1.54, 95%CI: 1.30, 1.80 for third tertile vs first tertile, Ptrend < 0.0001). CONCLUSION: In this study, we found that higher allostatic load was significantly associated with an increased risk of breast cancer in postmenopausal women.

Individualized driver amplitude in liver MR elastography: a linear regression study.

BACKGROUND: Current liver magnetic resonance elastography (MRE) scans often require adjustments to driver amplitude to produce acceptable images. This could lead to time wastage and the potential loss of an opportunity to capture a high-quality image. PURPOSE: To construct a linear regression model of individualized driver amplitude to improve liver MRE image quality. MATERIAL AND METHODS: Data from 95 liver MRE scans of 61 participants, including abdominal missing volume ratio (AMVR), breath-holding status, the distance from the passive driver on the skin surface to the liver edge (Dd-l), body mass index (BMI), and lateral deflection of the passive driver with respect to the human sagittal plane (Angle α), were continuously collected. The Spearman correlation analysis and lasso regression were conducted to screen the independent variables. Multiple linear regression equations were developed to determine the optimal amplitude prediction model. RESULTS: The optimal formula for linear regression models: driver amplitude (%) = -16.80 + 78.59 × AMVR - 11.12 × breath-holding (end of expiration = 1, end of inspiration = 0) + 3.16 × Dd-l + 1.94 × BMI + 0.34 × angle α, with the model passing the F test (F = 22.455, P <0.001) and R2 value of 0.558. CONCLUSION: The individualized amplitude prediction model based on AMVR, breath-holding status, Dd-l, BMI, and angle α is a valuable tool in liver MRE examination.

Detecting methylation quantitative trait loci using a methylation random field method.

DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.

Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci.

MOTIVATION: CpG sites within the same genomic region often share similar methylation patterns and tend to be co-regulated by multiple genetic variants that may interact with one another. RESULTS: We propose a multi-trait methylation random field (multi-MRF) method to evaluate the joint association between a set of CpG sites and a set of genetic variants. The proposed method has several advantages. First, it is a multi-trait method that allows flexible correlation structures between neighboring CpG sites (e.g. distance-based correlation). Second, it is also a multi-locus method that integrates the effect of multiple common and rare genetic variants. Third, it models the methylation traits with a beta distribution to characterize their bimodal and interval properties. Through simulations, we demonstrated that the proposed method had improved power over some existing methods under various disease scenarios. We further illustrated the proposed method via an application to a study of congenital heart defects (CHDs) with 83 cardiac tissue samples. Our results suggested that gene BACE2, a methylation quantitative trait locus (QTL) candidate, colocalized with expression QTLs in artery tibial and harbored genetic variants with nominal significant associations in two genome-wide association studies of CHD. AVAILABILITY AND IMPLEMENTATION: https://github.com/chenlyu2656/Multi-MRF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Progressive reduction in circulating levels of carotenoids and other micronutrients in patients with chronic pancreatitis.

BACKGROUND: Although micronutrients modulate immunity and inflammation, it remains elusive whether they are implicated in the development and progression of chronic pancreatitis (CP). This study aimed to investigate differences in the circulating levels of selected carotenoids and vitamins between CP and controls and trends in the levels of these micronutrients across controls, early CP, and definite CP. METHODS: Demographic and lifestyle data were extracted from medical records for 53 patients with CP (13 early and 38 definite) and obtained using a questionnaire for 52 controls. Plasma ß-carotene, lycopene, cryptoxanthin, zeaxanthin, and α-tocopherol and serum 25(OH)D, folate, IL-6, TNF-α, and MCP-1 were measured with state-of-the-art methods. RESULTS: The levels of all micronutrients (except folate) were significantly lower in CP than in controls. There was a progressive decrease in the levels of these micronutrients across controls, early CP, and definite CP (all p values for trend: ≤0.0012); e.g., plasma lycopene was 36.6, 21.5, and 14.5 µg/dL for controls, early CP, and definite CP, respectively. After adjustment for confounders, there were strong, inverse associations between the levels of all micronutrients (except folate) and CP (e.g., OR (95% CI) for ≥ median vs.

An integrative association method for omics data based on a modified Fisher's method with application to childhood asthma.

The development of high-throughput biotechnologies allows the collection of omics data to study the biological mechanisms underlying complex diseases at different levels, such as genomics, epigenomics, and transcriptomics. However, each technology is designed to collect a specific type of omics data. Thus, the association between a disease and one type of omics data is usually tested individually, but this strategy is suboptimal. To better articulate biological processes and increase the consistency of variant identification, omics data from various platforms need to be integrated. In this report, we introduce an approach that uses a modified Fisher's method (denoted as Omnibus-Fisher) to combine separate p-values of association testing for a trait and SNPs, DNA methylation markers, and RNA sequencing, calculated by kernel machine regression into an overall gene-level p-value to account for correlation between omics data. To consider all possible disease models, we extend Omnibus-Fisher to an optimal test by using perturbations. In our simulations, a usual Fisher's method has inflated type I error rates when directly applied to correlated omics data. In contrast, Omnibus-Fisher preserves the expected type I error rates. Moreover, Omnibus-Fisher has increased power compared to its optimal version when the true disease model involves all types of omics data. On the other hand, the optimal Omnibus-Fisher is more powerful than its regular version when only one type of data is causal. Finally, we illustrate our proposed method by analyzing whole-genome genotyping, DNA methylation data, and RNA sequencing data from a study of childhood asthma in Puerto Ricans.

Mediation analysis of racial disparities in triple-negative breast cancer incidence among postmenopausal women.

BACKGROUND: Triple-negative breast cancer (TNBC) is disproportionately higher in Black women relative to White women. The objective of this study was to examine to what extent the association between race/ethnicity and risk of TNBC is mediated by potentially modifiable factors. METHODS: A total of 128,623 Black and White women aged 50-79 years from the Women's Health Initiative were followed for a mean of 15.8 years. 643 incident TNBC cases (92 Black women and 551 White women) were confirmed by medical record review. Mediation analyses were conducted using an approach under a counterfactual framework. RESULTS: Black women had approximately twofold higher risk of TNBC compared with white women (HR = 1.93, 95% CI 1.52-2.45). We observed that 48% of the racial disparity was mediated by metabolic dysfunction defined by having 3 or more cardiometabolic risk factors including elevated waist circumference, having history of diabetes, high cholesterol and hypertension. The racial disparity was not significantly mediated by other factors studied, including socioeconomic, lifestyle or reproductive factors. CONCLUSION: Our study observed that approximately half of the racial disparity between postmenopausal Black and White women in TNBC incidence was driven by metabolic dysfunction.

Penalized Fieller's confidence interval for the ratio of bivariate normal means.

Constructing a confidence interval for the ratio of bivariate normal means is a classical problem in statistics. Several methods have been proposed in the literature. The Fieller method is known as an exact method, but can produce an unbounded confidence interval if the denominator of the ratio is not significantly deviated from 0; while the delta and some numeric methods are all bounded, they are only first-order correct. Motivated by a real-world problem, we propose the penalized Fieller method, which employs the same principle as the Fieller method, but adopts a penalized likelihood approach to estimate the denominator. The proposed method has a simple closed form, and can always produce a bounded confidence interval by selecting a suitable penalty parameter. Moreover, the new method is shown to be second-order correct under the bivariate normality assumption, that is, its coverage probability will converge to the nominal level faster than other bounded methods. Simulation results show that our proposed method generally outperforms the existing methods in terms of controlling the coverage probability and the confidence width and is particularly useful when the denominator does not have adequate power to reject being 0. Finally, we apply the proposed approach to the interval estimation of the median response dose in pharmacology studies to show its practical usefulness.

Mitochondrial DNA deletion mutations increase exponentially with age in human skeletal muscle.

BACKGROUND: Mitochondrial DNA (mtDNA) deletion mutations lead to electron transport chain-deficient cells and age-induced cell loss in multiple tissues and mammalian species. Accurate quantitation of somatic mtDNA deletion mutations could serve as an index of age-induced cell loss. Quantitation of mtDNA deletion molecules is confounded by their low abundance in tissue homogenates, the diversity of deletion breakpoints, stochastic accumulation in single cells, and mosaic distribution between cells. AIMS: Translate a pre-clinical assay to quantitate mtDNA deletions for use in human DNA samples, with technical and biological validation, and test this assay on human subjects of different ages. METHODS: We developed and validated a high-throughput droplet digital PCR assay that quantitates human mtDNA deletion frequency. RESULTS: Analysis of human quadriceps muscle samples from 14 male subjects demonstrated that mtDNA deletion frequency increases exponentially with age-on average, a 98-fold increase from age 20-80. Sequence analysis of amplification products confirmed the specificity of the assay for human mtDNA deletion breakpoints. Titration of synthetic mutation mixtures found a lower limit of detection of at least 0.6 parts per million. Using muscle DNA from 6-month-old mtDNA mutator mice, we measured a 6.4-fold increase in mtDNA deletion frequency (i.e., compared to wild-type mice), biologically validating the approach. DISCUSSION/CONCLUSIONS: The exponential increase in mtDNA deletion frequency is concomitant with the known muscle fiber loss and accelerating mortality that occurs with age. The improved assay permits the accurate and sensitive quantification of deletion mutations from DNA samples and is sufficient to measure changes in mtDNA deletion mutation frequency in healthy individuals across the lifespan and, therefore, patients with suspected mitochondrial diseases.

Gut microbiota diversity is associated with cardiorespiratory fitness in post-primary treatment breast cancer survivors.

NEW FINDINGS: What is the central question of this study? Does the link between cardiorespiratory fitness and gut microbiota diversity persist after adjusting for the potential effects of percentage body fat and activity-related energy expenditure (AEE)? What is the main finding and its importance? This is the first study to examine the link between cardiorespiratory fitness and gut microbiota diversity while accounting for the underlying effects of percentage body fat and free-living AEE. Results from the present work suggest that cardiorespiratory fitness, not physical activity, is a superior correlate of gut microbiota diversity among post-primary treatment, non-metastatic breast cancer survivors. ABSTRACT: Cancer treatment uniquely triggers multiple physiological shifts detrimental to overall health. Although previous research indicates a link between the gut microbiota and cardiorespiratory fitness, it is unclear whether these findings are attributable to potential underlying effects of percentage body fat or free-living activity energy expenditure (AEE). The microbe composition of faecal specimens from 37 breast cancer survivors was determined using 16S microbiome analyses. Individual-sample microbiota diversity (α-diversity) and between-sample community differences (ß-diversity) were examined. Peak oxygen uptake ( V̇O2peak ) was estimated from a graded exercise test consistent with the modified Naughton protocol, in which exercise terminates at 85% of age-predicted maximal heart rate. The AEE was measured over 10 days using doubly labelled water, wherein the percentage body fat was calculated from total body water. Pearson correlations revealed α-diversity indices (Chao1, observed species, PD whole tree and Shannon) to be positively associated with V̇O2peak (r = 0.34-0.51; P < 0.05), whereas the percentage of maximal heart rate during stages 1-4 of the graded exercise test (r = -0.34 to -0.50; P < 0.05) and percentage body fat (r = -0.32 to -0.41; P < 0.05) were negatively associated with the same α-diversity indices. Multiple linear regression models showed that V̇O2peak accounted for 22 and 26% of the variance in taxonomic richness (observed species) and phylogenic diversity after adjustment for percentage body fat and menopausal status. Unweighted UniFrac (ß-diversity) was significant for several outcomes involving cardiorespiratory fitness, and significant taxa comparisons were found. Associations between gut microbiota and free-living AEE were not found. Results from the present work suggest that cardiorespiratory fitness, not physical activity, is a superior correlate of gut microbiota diversity.

Quality of anticoagulation control and hemorrhage risk among African American and European American warfarin users.

OBJECTIVE: We evaluated whether percent time in target range (PTTR), risk of over-anticoagulation [international normalized ratio (INR)>4], and risk of hemorrhage differ by race. As PTTR is a strong predictor of hemorrhage risk, we also determined the influence of PTTR on the risk of hemorrhage by race. PARTICIPANTS AND METHODS: Among 1326 warfarin users, PTTR was calculated as the percentage of interpolated INR values within the target range of 2.0-3.0. PTTR was also categorized as poor (PTTR<60%), good (60≤PTTR<70%), or excellent (PTTR≥70%) anticoagulation control. Over-anticoagulation was defined as INR more than 4 and major hemorrhages included serious, life-threatening, and fatal bleeding episodes. Logistic regression and survival analyses were carried out to evaluate the association of race with PTTR (≥60 vs. <60) and major hemorrhages, respectively. RESULTS: Compared with African Americans, European Americans had higher PTTR (57.6 vs. 49.1%; P<0.0001) and were more likely to attain 60≤PTTR<70% (22.9 vs. 13.1%; P<0.001) or PTTR of at least 70% (26.9 vs. 18.2%; P=0.001). Older (>65 years) patients without venous thromboembolism indication and chronic kidney disease were more likely to attain PTTR of at least 60%. After accounting for clinical and genetic factors, and PTTR, African Americans had a higher risk of hemorrhage [hazard ratio (HR)=1.58; 95% confidence interval (CI): 1.04-2.41; P=0.034]. Patients with 60≤PTTR<70% (HR=0.62; 95% CI: 0.38-1.02; P=0.058) and PTTR of at least 70% (HR=0.27; 95% CI: 0.15-0.49; P<0.001) had a lower risk of hemorrhage compared with those with PTTR less than 60%. CONCLUSION: Despite the provision of warfarin management through anticoagulation clinics, African Americans achieve a lower overall PTTR and have a significantly higher risk of hemorrhage. Personalized medicine interventions tailored to African American warfarin users need to be developed.

Dense Genotyping of Immune-Related Regions Identifies Loci for Rheumatoid Arthritis Risk and Damage in African Americans.

Over 100 risk loci for rheumatoid arthritis (RA) have been identified in individuals of European and Asian descent, but the genetic basis for RA in African Americans is less well understood. We genotyped 610 African Americans with autoantibody positive RA and 933 African American controls on the ImmunoChip (iChip) array. Using multivariable regression we evaluated the association between iChip markers and the risk of RA and radiographic severity. The single nucleotide polymorphism (SNP) rs1964995 (OR = 1.97, p = 1.28 × 10-15) near HLA-DRB1 was the most strongly associated risk SNP for RA susceptibility; SNPs in AFF3, TNFSF11, and TNFSF18 loci were suggestively associated (10-4 < p < 3.1 × 10-6). Trans-ethnic fine mapping of AFF3 identified a 90% credible set containing previously studied variants including rs9653442, rs7608424, and rs6712515 as well as the novel candidate variant rs11681966; several of these likely influence AFF3 gene expression level. Variants in TNFRSF9, CTLA4, IL2RA, C5/TRAF1, and ETS1 - but no variants within the major histocompatibility complex - were associated with RA radiographic severity. Conditional regression and pairwise linkage disequilibrium (LD) analyses suggest that additional pathogenic variants may be found in ETS1 and IL2RA beyond those found in other ethnicities. In summary, we use the dense genotyping of the iChip array and unique LD structure of African Americans to validate known risk loci for RA susceptibility and radiographic severity, and to better characterize the associations of AFF3, ETS1, and IL2RA.

Race influences warfarin dose changes associated with genetic factors.

Warfarin dosing algorithms adjust for race, assigning a fixed effect size to each predictor, thereby attenuating the differential effect by race. Attenuation likely occurs in both race groups but may be more pronounced in the less-represented race group. Therefore, we evaluated whether the effect of clinical (age, body surface area [BSA], chronic kidney disease [CKD], and amiodarone use) and genetic factors (CYP2C9*2, *3, *5, *6, *11, rs12777823, VKORC1, and CYP4F2) on warfarin dose differs by race using regression analyses among 1357 patients enrolled in a prospective cohort study and compared predictive ability of race-combined vs race-stratified models. Differential effect of predictors by race was assessed using predictor-race interactions in race-combined analyses. Warfarin dose was influenced by age, BSA, CKD, amiodarone use, and CYP2C9*3 and VKORC1 variants in both races, by CYP2C9*2 and CYP4F2 variants in European Americans, and by rs12777823 in African Americans. CYP2C9*2 was associated with a lower dose only among European Americans (20.6% vs 3.0%, P < .001) and rs12777823 only among African Americans (12.3% vs 2.3%, P = .006). Although VKORC1 was associated with dose decrease in both races, the proportional decrease was higher among European Americans (28.9% vs 19.9%, P = .003) compared with African Americans. Race-stratified analysis improved dose prediction in both race groups compared with race-combined analysis. We demonstrate that the effect of predictors on warfarin dose differs by race, which may explain divergent findings reported by recent warfarin pharmacogenetic trials. We recommend that warfarin dosing algorithms should be stratified by race rather than adjusted for race.

Gut microbiota composition associated with alterations in cardiorespiratory fitness and psychosocial outcomes among breast cancer survivors.

PURPOSE: In this proof-of-concept pilot study, our purpose was to determine correlations between gut microbiota composition and alterations in cardiorespiratory fitness and psychosocial outcomes among post-primary treatment breast cancer survivors (BCS). METHODS: Composition of the gut microbiota in BCS (n = 12) was assessed at baseline (M0) and at the end of 3 months (M3) using Illumina MiSeq DNA Sequencing of the 16S rRNA gene. Gut microbiota composition was analyzed using the QIIME bioinformatics software and represented through diversity metrics and taxa analyses. Cardiorespiratory fitness, fatigue, anxiety, depression, and sleep dysfunction were assessed at M0 and M3 via the submaximal treadmill test, Fatigue Symptom Inventory, Hospital Anxiety and Depression Scale, and Pittsburgh Sleep Quality Index, respectively. RESULTS: Increased fatigue interference in BCS was associated with increased mean within-sample Shannon diversity (organism richness and evenness) (p = 0.009). Weighted UniFrac analysis (shifts in taxa relative abundance) revealed significant differences in between-sample (beta) diversity for changes in fatigue interference (p = 0.01) and anxiety (p = 0.022), with a trend observed for fatigue intensity and sleep dysfunction (p < 0.1). Unweighted UniFrac analysis (shifts in taxa types) found significant beta diversity differences for cardiorespiratory fitness (p = 0.026). Prior to false discovery correction (FDR), changes in fitness, fatigue, anxiety, and sleep dysfunction were associated with the frequency of certain gut bacteria genera (e.g., Faecalibacterium, Prevotella, Bacteroides) (p < 0.05). CONCLUSIONS: Correlations may exist between alterations in gut microbiota composition and longitudinal changes in cardiorespiratory fitness, fatigue, and anxiety in BCS. Further research examining the role of the microbiota-gut-brain axis in exercise-induced effects on psychosocial outcomes in BCS is warranted.

Ser(P)-1292 LRRK2 in urinary exosomes is elevated in idiopathic Parkinson's disease.

BACKGROUND: Mutations in Leucine-rich repeat kinase 2 (LRRK2) enhance levels of the autophosphorylated LRRK2 protein and are the most common known cause of inherited Parkinson's disease (PD). LRRK2 has been further implicated in susceptibility to idiopathic PD in genetic association studies. OBJECTIVE: The objective of this study was to compare autophosphorylated Ser(P)-1292 LRRK2 levels from biobanked urine samples with clinical data in PD patients and controls. METHODS: Ser(P)-1292 LRRK2 levels were measured from urine exosome fractions from 79 PD patients and 79 neurologically healthy controls enrolled in the Parkinson Disease Biomarker Program at the University of Alabama at Birmingham. RESULTS: Ser(P)-1292 LRRK2 levels were higher in men than women (P < .0001) and elevated in PD patients when compared with controls (P = .0014). Ser(P)-1292 LRRK2 levels were higher in PD cases with worse cognition and correlated with poor performance in MoCA (r = -0.2679 [-0.4628 to -0.0482]), MDS-UPDRS subscales 1 and 2 (r = 0.2239 [0.0014-0.4252], 0.3404 [0.1276-0.5233], respectively), Epworth Sleepiness Scale (r = 0.3215 [0.1066-0.5077]), and Modified Schwab and England Activities of Daily Living Scales (r = -0.4455 [-0.6078 to -0.2475]). Ser(P)-1292 LRRK2 levels predicted those with worse cognitive impairment in PD patients with some success (c = 0.73). CONCLUSIONS: Urinary exosome Ser(P)-1292 LRRK2 levels are elevated in idiopathic PD and correlated with the severity of cognitive impairment and difficultly in accomplishing activities of daily living. These results implicate biochemical changes in LRRK2 in idiopathic PD. © 2016 International Parkinson and Movement Disorder Society.

On the design and analysis of next-generation sequencing genotyping for a cohort with haplotype-informative reads.

Next-generation sequencing (NGS) technologies, which can provide base-pair resolution genetic information for all types of genetic variations, are increasingly used in genetics research. However, due to the complex nature of NGS technologies and analytics and their relatively high cost, investigators face practical challenges for both design and analysis. These challenges are further complicated by recent methodological developments that make it possible to use haplotype information in sequencing reads. In light of these developments, we conducted comprehensive simulations to evaluate the effects of sequencing coverage, insert size of paired-end reads, and sample size on genotype calling and haplotype phasing in NGS studies. In contrast to previous studies that typically use idealized scenarios to tease out the effects of individual design and analytic decisions, we used a complete analytical pipeline from read mapping and variant detection to genotype calling and haplotype phasing so that we can assess the joint effects of multiple decisions and thus make more realistic recommendations to investigators. Consistent with previous studies, we found that the use of haplotype information in reads can improve the accuracy of genotype calling and haplotype phasing, and we also found that a mixture of short and long insert sizes of paired-end reads may offer even greater accuracy. However, this benefit is only clear in high coverage sequencing where variant detection is close to perfect. Finally, we observed that LD-based refinement methods do not always outperform single site based methods for genotype calling. Therefore, we should choose analytical methods that are appropriate to the sequencing coverage and sample size in order to use haplotype information in sequencing reads.

Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.

OBJECTIVE: The kernel machine (KM) test reportedly performs well in the set-based association test of rare variants. Many studies have been conducted to measure phenotypes at multiple time points, but the standard KM methodology has only been available for phenotypes at a single time point. In addition, family-based designs have been widely used in genetic association studies; therefore, the data analysis method used must appropriately handle familial relatedness. A rare-variant test does not currently exist for longitudinal data from family samples. Therefore, in this paper, we aim to introduce an association test for rare variants, which includes multiple longitudinal phenotype measurements for either population or family samples. METHODS: This approach uses KM regression based on the linear mixed model framework and is applicable to longitudinal data from either population (L-KM) or family samples (LF-KM). RESULTS: In our population-based simulation studies, L-KM has good control of Type I error rate and increased power in all the scenarios we considered compared with other competing methods. Conversely, in the family-based simulation studies, we found an inflated Type I error rate when L-KM was applied directly to the family samples, whereas LF-KM retained the desired Type I error rate and had the best power performance overall. Finally, we illustrate the utility of our proposed LF-KM approach by analyzing data from an association study between rare variants and blood pressure from the Genetic Analysis Workshop 18 (GAW18). CONCLUSION: We propose a method for rare-variant association testing in population and family samples using phenotypes measured at multiple time points for each subject. The proposed method has the best power performance compared to competing approaches in our simulation study.

A Generalized Sequential Bonferroni Procedure for GWAS in Admixed Populations Incorporating Admixture Mapping Information into Association Tests.

OBJECTIVE: To develop effective methods for GWAS in admixed populations such as African Americans. METHODS: We show that, when testing the null hypothesis that the test SNP is not in background linkage disequilibrium with the causal variants, several existing methods cannot control well the family-wise error rate (FWER) in the strong sense in GWAS. These existing methods include association tests adjusting for global ancestry and joint association tests that combine statistics from admixture mapping tests and association tests that correct for local ancestry. Furthermore, we describe a generalized sequential Bonferroni (smooth-GSB) procedure for GWAS that incorporates smoothed weights calculated from admixture mapping tests into association tests that correct for local ancestry. We have applied the smooth-GSB procedure to analyses of GWAS data on American Africans from the Atherosclerosis Risk in Communities (ARIC) Study. RESULTS: Our simulation studies indicate that the smooth-GSB procedure not only control the FWER, but also improves statistical power compared with association tests correcting for local ancestry. CONCLUSION: The smooth-GSB procedure can result in a better performance than several existing methods for GWAS in admixed populations.