False Discovery Rate Method Research Articles

Polymorphisms in the dopamine (DA) signaling to predict outcome in patients (pts) with metastatic colorectal cancer (mCRC): Data from TRIBE, MAVERICC, and FIRE-3 phase III trials.

3048 Background: Strong evidence supports the critical role of the gut-brain axis in modulating gastrointestinal function and homeostasis. Available data suggest an involvement of the dopaminergic pathway in CRC dynamics. DA could inhibit proliferation and migration of tumor endothelial cells and enhanced 5-fluorouracil efficacy in CRC preclinical models. Hence, we hypothesized that genetic variants in DA signaling may predict treatment outcomes in mCRC pts. Methods: The impact on outcome of 22 selected single nucleotide polymorphisms (SNPs) in 9 genes of the DA signaling pathway ( DRD1, DRD2, DRD3, DRD4, DRD5, TAAR1, SLC6A3, SLC18A2, PPP1R1B) was analyzed on a total of 884 pts enrolled in three independent randomized first-line trials: TRIBE (n = 324), MAVERICC (n = 324), and FIRE-3 (n = 236). Genomic DNA from blood samples of pts was genotyped through the OncoArray, a custom array manufactured by Illumina. A meta-analysis approach using the METASOFT software was used to quantify SNPs prognostic effects and heterogeneities across treatment arms. P values were adjusted for multiple testing using the false discovery rate (FDR) method. Results: Overall, DRD3 rs3732790, rs9817063 and rs2134655 showed a significant nominal p value ( P) in association with tumor response (TR) across trials ( P= 0.032, P= 0.021, P= 0.027, respectively). TAAR1 rs8192620 showed an association with both progression free survival (PFS) ( P= 0.01) and overall survival (OS) ( P= 0.033), similar to DA transporter SLC6A3 rs6347 ( P= 0.016 and P= 0.002, respectively). SLC6A3 rs6347 association with OS remained significant after FDR ( PFDR= 0.045). Subgroup analyses showed a significant association with PFS for DRD1 rs267410 and SLC6A3 rs2652510 in females ( PFDR= 0.056), and between SLC6A3 rs6347 and OS ( PFDR= 0.041) and SLC6A3 rs6876890 and TR ( PFDR= 0.05) in KRAS wild type. Conclusions: Our results suggest that SNPs in DA signaling may have a prognostic value in mCRC pts receiving first-line treatment. Upon validation, these findings may provide novel insight on the role of DA signaling in CRC and possibly contribute to open novel therapeutic perspectives.

Genetic variants in p53 signaling pathway genes predict chemotherapy efficacy in colorectal cancer.

BackgroundThe murine double minute‐2 gene (MDM2) was originally identified as predicting chemotherapy efficacy. However, little is known regarding the association between single nucleotide polymorphisms (SNPs) in the p53 signaling pathway and prognosis/chemotherapy sensitivity in colorectal cancer.MethodsWe analyzed the association between 111 SNPs in 22 p53 signaling pathway genes and both progression‐free survival (PFS) and disease control rate (DCR) using Cox regression and logistics regression analysis. The false discovery rate method was used for correction of multiple testing. Secondary structure was predicted by RNAfold. Expression qualitative trait locus analysis and mRNA expression differences were assessed using the GTEx and TCGA databases.ResultsWe found that the rs747828 C allele of TP73 was significantly associated with reduced PFS (HR = 1.64, 95% CI = 1.27‐2.12, P = 2.00 × 10−4) in the additive model. In the stratified analysis, the rs747828 C allele was significantly associated with both reduced PFS (P = 1.40 × 10−3) and DCR (P = 1.82 × 10−2) in oxaliplatin‐based chemotherapy. The secondary structure of TP73 was altered in response to different rs747828 genotypes. Although the rs747828 C allele was not associated with messenger RNA (mRNA) TP73 expression, it was significantly associated with increased mRNA TP73‐AS1 expression levels in sigmoid tissues. TP73 mRNA was significantly overexpressed in tumor tissues compared to adjacent normal tissues (P = 2.36 × 10−19).ConclusionOur findings indicate that functional genetic variants of TP73 mediate the response to chemotherapy in colorectal cancer.

Biomarkers of blood cadmium and incidence of cardiovascular events in non-smokers: results from a population-based proteomics study

BackgroundCadmium is a toxic metal with multiple adverse health effects, including risk of cardiovascular disease (CVD). The mechanistic link between cadmium and CVD is unclear. Our aim was to examine the associations between blood cadmium (B-Cd) and 88 potential protein biomarkers of CVD.MethodsB-Cd and 88 plasma proteins were measured in a community-based prospective cohort, the Malmö Diet and Cancer study. The primary analysis was performed in never smokers (n = 1725). Multiple linear regression was used with adjustments for age and sex, and correction for multiple comparisons using the false discovery rate method. Proteins significantly associated with B-Cd were replicated in long-term former smokers (n = 782). Significant proteins were then studied in relation to incidence of CVD (i.e., coronary events or ischemic stroke) in never smokers.ResultsFifteen proteins were associated with B-Cd in never smokers. Eight of them were replicated in long-term former smokers. Kidney injury molecule-1, fibroblast growth factor-23 (FGF23), tumor necrosis factor receptor-2, matrix metalloproteinase-12, cathepsin L1, urokinase plasminogen activator receptor, C-C motif chemokine-3 (CCL3), and chemokine (C-X3-C motif) ligand-1 were associated with B-Cd both in never smokers and long-term former smokers. Except for CCL3 and FGF23, these proteins were also significantly associated with incidence of CVD.ConclusionsB-Cd in non-smokers was associated with eight potential plasma biomarkers of CVD and kidney injury. The results suggest pathways for the associations between B-Cd and CVD and kidney injury.

Cell-level somatic mutation detection from single-cell RNA sequencing.

MotivationBoth single-cell RNA sequencing (scRNA-seq) and DNA sequencing (scDNA-seq) have been applied for cell-level genomic profiling. For mutation profiling, the latter seems more natural. However, the task is highly challenging due to the limited input materials from only two copies of DNA molecules, while whole-genome amplification generates biases and other technical noises. ScRNA-seq starts with a higher input amount, so generally has better data quality. There exists various methods for mutation detection from DNA sequencing, it is not clear whether these methods work for scRNA-seq data.ResultsMutation detection methods developed for either bulk-cell sequencing data or scDNA-seq data do not work well for the scRNA-seq data, as they produce substantial numbers of false positives. We develop a novel and robust statistical method—called SCmut—to identify specific cells that harbor mutations discovered in bulk-cell data. Statistically SCmut controls the false positives using the 2D local false discovery rate method. We apply SCmut to several scRNA-seq datasets. In scRNA-seq breast cancer datasets SCmut identifies a number of highly confident cell-level mutations that are recurrent in many cells and consistent in different samples. In a scRNA-seq glioblastoma dataset, we discover a recurrent cell-level mutation in the PDGFRA gene that is highly correlated with a well-known in-frame deletion in the gene. To conclude, this study contributes a novel method to discover cell-level mutation information from scRNA-seq that can facilitate investigation of cell-to-cell heterogeneity.Availability and implementationThe source codes and bioinformatics pipeline of are available at https://github.com/nghiavtr/SCmut.Supplementary information Supplementary data are available at Bioinformatics online.

500 Skin microbiota perturbations are clinical severity-dependent in hidradenitis suppurativa

Hidradenitis Suppurativa (HS) is a prevalent and debilitating inflammatory skin disease. Previous studies have demonstrated skin microbiota perturbations in HS, but how these perturbations relate to disease severity is not understood. We aimed to understand the relationship between clinical disease severity and cutaneous dysbiosis in HS. We collected skin surface swabs from the inguinal crease of 12 HS patients and 5 age- and sex-matched healthy volunteers (HV). Demographic and clinical data, including clinical severity as measured by Hurley staging, were collected. The V1-V3 hypervariable region of the 16S ribosomal RNA gene was sequenced from purified genomic DNA and analyzed using a mothur-based pipeline. We used chi square tests to compare relative abundances of bacteria between HS subjects and HV, and Spearman correlation to examine relationships between clinical severity and bacterial relative abundance; p<0.05 was considered significant after adjusting for multiple comparisons using the false discovery rate method. Subjects had a mean age of 33.0 years (SD 11.6, women 58.3%, men 41.7%), and a range of clinical disease severity (Hurley I: 16.7%, II: 66.6%, III: 16.7%). Bacterial communities from HS subjects were distinct and clustered apart from those of HV on principal components analysis. The separation between the bacterial communities of HS subjects and HV increased with worsening clinical severity. The mean relative abundance of Cutibacterium acnes was reduced in HS subjects as compared with HV (HS 0.14 ± 0.04% vs HV 18.0 ± 15.3%, p<0.05). Lower relative abundances of Staphylococcus (Spearman ρ=-0.64, p<0.05) and Corynebacterium (ρ=-0.52, p<0.05) species, and higher relative abundances of mixed anaerobes (including Porphyromonas, Prevotella, and genera belonging to Clostridiales XI Incertae Sedis, ρ>0.53, p<0.05 for each comparison) were observed in HS subjects with worsening disease severity as compared with HV. Our findings indicate that worsening clinical severity is associated with increased skin microbiota perturbations in HS.

Composition of forage and grain from genetically modified DP202216 maize is equivalent to non-modified conventional maize (Zea mays L.)

ABSTRACTDP202216 maize was genetically modified to increase and extend the expression of the zmm28 gene relative to native zmm28 gene expression, resulting in plants with enhanced grain yield potential. Standard nutritional and compositional parameters for maize grain and forage (e.g., proximates, fiber, minerals, amino acids, fatty acids, vitamins, anti-nutrients, secondary metabolites) from DP202216 maize were compared to grain and forage from non-modified near-isoline maize (control). Three amino acids (glycine, methionine, and serine) and two vitamins (vitamin B1 and vitamin B3) were statistically different between DP202216 and control maize grain but were not statistically different when adjusted using the false discovery rate method. These analyte values also fell within the ranges of natural variation of non-modified commercial maize varieties supporting that statistical differences were not biologically relevant. The composition of grain and forage from DP202216 maize is comparable to grain and forage from non-modified maize with a history of safe use.

Identification and Validation of a Biomarker Signature in Patients With Resectable Pancreatic Cancer via Genome-Wide Screening for Functional Genetic Variants

Surgery currently offers the only chance for a cure in pancreatic ductal adenocarcinoma (PDAC), but it carries a significant morbidity and mortality risk and results in varying oncologic outcomes. At present, to our knowledge, there are no tests available before surgical resection to identify tumors with an aggressive biological phenotype that could guide personalized treatment strategies. Identification of noninvasive genetic biomarkers that could direct therapy in patients whose cases are amenable to pancreatic cancer resection. This multicenter study combined a prospective European cohort of patients with PDAC who underwent pancreatic resection (from University Hospital of Zurich, Zurich, Switzerland; Cantonal Hospital of Winterthur, Winterthur, Switzerland; and University Clinic of Ulm, Ulm, Germany) with data from the Cancer Genome Atlas database in the United States, which includes prospectively registered patients with PDAC. A genome-wide screening for functional single-nucleotide polymorphisms (SNPs) that affect PDAC survival was conducted using the European cohort for identification and the Cancer Genome Atlas cohort for validation. We used Cox proportional hazards models to screen for high-frequency polymorphic variants that are associated with allelic differences in tumor-associated survival and either result in an altered protein structure and function or reside in known regulatory noncoding genomic regions. The false-discovery rate method was applied for multiple hypothesis-testing corrections. Data analysis occurred from November 2017 to May 2018. Pancreatic resection. Tumor-associated survival. A total of 195 patients in the European cohort were included, as well as 136 patients in the Cancer Genome Atlas cohort (overall median [range] age, 66 [19-87] years; 156 [47.1%] were women, and 175 [52.9%] were men). Two SNPs in noncoding, functional regions of genes that regulate cancer progression, invasion, and metastasis were identified (CHI3L2 SNP rs684559 and CD44 SNP rs353630). These were associated with survival after PDAC resection; patients who carry the risk alleles at 1 of both SNP loci had a 2.63-fold increased risk for tumor-associated death compared with those with protective genotypes (hazard ratio for survival, 0.38 [95% CI, 0.27-0.53]; P = 1.0 × 10-8). The identified polymorphisms may serve as a noninvasive biomarker signature of prospective survival after pancreatic resection that is readily available at the time of PDAC diagnosis. This signature can be used to identify a subset of high-risk patients with PDAC with very low survival probability who might be eligible for inclusion in clinical trials of new therapeutic strategies, including neoadjuvant chemotherapy protocols. In addition, the biological knowledge about these SNPs could help guide the development of individualized genomic strategies for PDAC therapies.

PC-10 Classical HLA alleles are associated with prevalent and persistent cervical high-risk HPV infection in African women

Background: Persistent cervical high-risk human papillomavirus (hrHPV) infection is a necessary cause of cervical cancer. However, the genetic factors underlying its risk are not well understood. We hypothesized that immunogenetic variation plays a role in hrHPV infection and persistence. Therefore, we conducted a study of classical HLA alleles and their association with hrHPV infection and persistence. Methods: We characterized HPV infection using SPF25/LiPA10 in Nigerian women at baseline and at 6 months follow-up visits in 2014. hrHPV infection was prevalent if at least one carcinogenic HPV genotype was detected in a sample provided at the baseline visit and persistent if at least one carcinogenic HPV genotype was detected in each of the samples provided at the baseline and follow-up visits. Genotyping was performed with the Illumina Multi-Ethnic Genotyping Array. Classical HLA alleles were imputed from genotypes in the MHC region using the HLA genotype imputation with attribute bagging (HIBAG) algorithm. HLA association tests were conducted under additive genetic models. P values were adjusted for multiple comparisons with the false discovery rate (FDR) method. Results: The mean (±SD) age of the study participants was 38 (±8) years, 48% (247/517) were HIV negative, 24% (125/517) were hrHPV positive at baseline and 10% (51/517) had persistent hrHPV infections. In multivariate regression models adjusted for age, HIV status and the first principal component, DQA1*01:02 (OR 1.46; 95% CI: 1.07 to 1.97; P = 0.03). and DQA1*02:01 (OR 1.89; 95% CI: 1.12 to 3.15; P = 0.03) were positively associated with prevalent but not persistent hrHPV infections, while DQA1*05:01 was negatively associated with prevalent hrHPV (OR 0.45; 95% CI: 0.24 to 0.85; P = 0.03) but positively associated with persistent cervical hrHPV infections (OR 2.46; 95% CI: 1.27 to 4.77; P = 0.04). Four haplotypes (A*30:01 - DQA1*05:01, B*07:02 - C*07:02, B*07:02 - DQA1*05:01 and C*07:02 - DQA1*05:01) were significantly associated with prevalent cervical hrHPV infections and several haplotypes that included the DQA1*05:01 allelic variant (including B*15:10 - DQA1*05:01, DQA1*05:01 - DQB1*02:01 and DQA1*05:01 - DRB1*03:01) were significantly associated with persistent cervical hrHPV infections. Six amino acids located on the DQA1 gene were associated with prevalent but not persistent cervical hrHPV infections. Conclusions: This study, the first to investigate the association between HLA alleles and persistent hrHPV risk in African women, has identified important risk alleles that merit further study. Our findings provide new insights into risk factors for hrHPV infection in women of African ancestry.

Abstract P205: Genome-Wide Epigenetic Study of Prenatal Famine Exposure and Blood Lipids in Late Adulthood: The Genomic Research of the Chinese Great Famine (GRECF) Study

Objectives: To identify differentially methylated DNA positions associated with prenatal exposure to the Chinese Great Famine and their influence on blood lipids in late adulthood among participants of the Genomic Research of the Chinese Great Famine (GRECF) study. Methods: The GRECF study was designed to study the influence of the Chinese Great Famine on human genome and metabolic disorders. We randomly selected 8 participants born in a famine stricken area during the Great Chinese Famine (1959-1961) and 8 sex- and residency of location-matched participants born within 3 years after the famine. Genome-wide DNA methylation was profiled using the IlluminaEPIC BeadChip. After quality control, a total of 391,633 probes remained for analyses. Total cholesterol, low-density lipoprotein cholesterol (LDLC), and high-density lipoprotein cholesterol (HDLC) were assayed using enzymatic colormetric tests. Beta values for each probe were compared between individuals with and without famine exposure using the empirical Bayes statistics implemented in Limma software. Probes with a raw p-value &lt;0.05 were mapped to genes using Kobas followed by KEGG pathway enrichment analyses. Those probes were also evaluated for associations with blood lipids using linear regression. False discovery rate (FDR) method was applied to correct for multiple testing. Results: A total of 48,055 CpG positions showed nominal associations ( p &lt;0.05) with prenatal famine exposure. None of them was significant after FDR correction. The top seven CpG positions reached p &lt;1х10 -5 and are relevant to metabolism and/or development. Specifically, prenatal famine exposure was associated increased methylation levels in CpG positions in C8orf31 , ELAVL1 , and U6 and decreased methylation levels in CpG positions in GBA2 , SHOX2 , SLC1A4 , and NPHP4 . Of note, the CpG probe in the GBA2 gene lies in the first exonic region. Pathway enrichment analyses to genes harboring nominally significant probes revealed 44 KEGG pathways that were significant after FDR correction ( q &lt;0.05), of which, 17 pathways are related to metabolism, development, and/or energy expenditure, including 11 pathways of the EGFR, FGFR, and SCF/KIT signaling and pathways of gene expression ( p =1.2х10 -11 ), metabolism ( p =6.8х10 -11 ), developmental biology ( p =7.9х10 -8 ), metabolism of protein ( p =3.1х10 -7 ), and metabolism of lipids and lipoproteins ( p =1.3х10 -4 ). None of the 48,055 probes were associated with blood lipid traits after FDR correction, however, four probes had p-values&lt;1.0х10 -4 for associations with LDLC or HDLC. The four probes lie in ZC3H18 , LINC00243 , PIGQ , and HS3ST4 genes, of which, PIGQ plays an important role in lipid metabolism. Conclusion: The small-scale genome-wide epigenetic study revealed important epigenetic changes associated with both prenatal famine exposure and blood lipids in late adulthood.

Abstract P114: Genetic Polymorphisms of CD36 Associated With Metabolic Syndrome in Chinese Han Population: A Nested Case-Control Study

Aims: CD36 , an important gene involved in cholesterol and fatty acid metabolism,, has been associated with many metabolic-related traits, including diabetes, hypertension, and obesity in different populations. However, the association of this gene with metabolic syndrome (MS) has not been well-studied. We aimed to investigate the association of CD36 with the risk of MS and longitudinal changes of its components in among participants of the Rural Chinese Cohort Study (RCCS). Materials and Methods: The RCCS enrolled 20,194 participants aged 2 18 years old between July of 2007 and August of 2008. A total of 17,265 participants (response rate 85.5%) were examined in the follow-up survey between July of 2013 and October of 2014. During the six years, 729 new incident cases of MS were ascertained. We conducted a nested case-control study within the RCCS. Specifically, 408 MS randomly cases and 408 one-to-one matched controls were included in the current study. In addition to follow-up time, non-MS controls were matched to MS cases on age, gender, marital status, and residence village. Human genomic DNA was extracted using a standard method from blood samples collected at the baseline. After quality control, 4 single nucleotide polymorphisms (SNPs) within the CD36 were successfully genotyped. MS was defined according to the 2009 criteria proposed by IDF and AHA/NHLBI. Multivariate conditional logistic regression was used to determine additive associations of SNPs and SNP-haplotype with MS adjusting for body mass index (BMI), smoking, drinking, and physical activity at baseline. Multivariate linear regression models were conducted among the control group to examine association of SNPs and SNP-haplotypes with MS components, controlling for age, gender, BMI, smoking, drinking and physical activity. Multiple testing was corrected by false positive discovery rate (FDR) method. Results: Two SNPs, rs7755 and rs1049673, were associated with risk for MS. Per minor allele increase in rs7755 and rs1049673 was associated with 2.29 (95% confidence interval [CI]: 1.24-4.24, p =0.008) and 2.30 (95% CI: 1.24-4.26, p =0.008) times higher odds of MS, respectively. In addition, rs1194197 was associated with changes of triglycerides level (β=0.151, p =0.014), and rs7755 (β=-0.065, p =0.028) and rs1049673 (β=-0.067, p =0.025) were associated with changes of high density lipoprotein cholesterol in the control group. One haplotype of the four SNPs, AGAG, was associated with the change of waist circumference (β=-1.773, p =0.048). Conclusions: We identified important SNPs and SNP-haplotypes of the CD36 associated with MS and changes of MS components in a Han Chinese population.

Identification of novel genetic variants for type 2 diabetes, childhood obesity, and their pleiotropic loci.

Obesity has result in increased prevalence of type 2 diabetes (T2D) in children. The genetic mechanisms underlying their relationship, however, are not fully understood. Here, we aim to identify novel SNPs associated with T2D and childhood obesity (CO), especially their pleiotropic loci. We integrated the summary statistics for two independent GWASs of T2D (n = 149,821) and childhood body mass index (CBMI) (n = 35,668) using the pleiotropy-informed conditional false discovery rate (cFDR) method. By leveraging the information of different levels of association for CBMI, we observed a strong enrichment of genetic variants associated with T2D. We identified 139 T2D-associated SNPs with 125 novel ones (cFDR < 0.05). Conditioned on T2D, we identified 37 significant SNPs for CBMI (cFDR < 0.05), including 25 novel ones. The conjunctional cFDR (ccFDR) analysis showed ten novel pleiotropic loci for T2D and CBMI (ccFDR < 0.05). Interestingly, the novel SNP rs1996023 is located at protein coding gene GNPDA2 (ccFDR = 1.28E-02), which has been reported to influence the risk of T2D and CO through central nervous system. Our findings may help to explain a greater proportion of the heritability for human traits and advance the understanding of the common pathophysiology between T2D and CO.

Goodness-of-fit tests for the functional linear model based on randomly projected empirical processes

We consider marked empirical processes indexed by a randomly projected functional covariate to construct goodness-of-fit tests for the functional linear model with scalar response. The test statistics are built from continuous functionals over the projected process, resulting in computationally efficient tests that exhibit root-n convergence rates and circumvent the curse of dimensionality. The weak convergence of the empirical process is obtained conditionally on a random direction, whilst the almost surely equivalence between the testing for significance expressed on the original and on the projected functional covariate is proved. The computation of the test in practice involves calibration by wild bootstrap resampling and the combination of several p-values, arising from different projections, by means of the false discovery rate method. The finite sample properties of the tests are illustrated in a simulation study for a variety of linear models, underlying processes, and alternatives. The software provided implements the tests and allows the replication of simulations and data applications.

SA65 - IDENTIFYING VARIANTS IN NF1A GENE TO BE ASSOCIATED WITH SEASONAL PATTERN AND SUSCEPTIBILITY FOR DEPRESSION THROUGH A CANDIDATE GENE APPROACH

Background Seasonal Pattern (SP) was reported in depressive patients of both Bipolar Disorder (BD) and Major Depressive Disorder (MDD). In this study, we aimed to investigate genetic contributions of seasonal pattern of major depressive episodes in both BD and MDD patients, including genes in relevant biological pathways indicated in previous studies. To further clarify the roles of the variants in depression, we conducted analyses using a case-control study design to examine whether the genetic variants for SP also possess effects on susceptibility to depression. Methods We recruited 472 BD (83%) and MDD (17%) patients in Taiwan. SP of depressive episode were defined according to interview questions as “Which season do you feel the worst?” in their first, the most serious, and the most recent episode within a year. Subjects answered a specific season in two of the three episode categories were considered SP+, while others were categorized as non-SP (SP-). The other dataset for depression was obtained from the Taiwan biobank with about 10,000 people. Cases were defined by self-report MDD history or having the sum-score greater than 3 in the Patient Health Questionnaire-4. We examined 15 genes (824 single nucleotide variants) including the melatonin pathway (MTNR1a, MTNR1b, AANAT), serotonin pathway (TPH2, HTR2A, HTR2B, SLC6A4), circadian feedback loop (NPAS2, CRY2, ARNTL, ARNTL2, RORA, RORB, PER) and NF1A gene that identified from a previous genome-wide association study of seasonal mania. Association analyses were performed by multivariate logistic regression, adjusting for gender, age, and diagnosis. We corrected for multiple testings using false discovery rate method. Results After excluding subjects with missing covariates, there were a total of 464 subjects with 162 (35%) in the SP+ and 302 (65%) in the SP- groups. No differences were observed in gender, age or diagnosis distribution between the two SP groups. Nominal associations with p-value The 20 markers were tested again for depression in 9862 individuals from Taiwan biobank. Although none of them showed genome-wide significance, 6 SNPs had nominal p-values Discussion We found that NF1A gene maybe a susceptible gene for depression and modifiable gene for SP. Some genetic variants in this gene are related to both seasonal pattern of depressive episodes and depression with nominal significance, indicating partially shared genetic components between the development of mood symptoms and seasonality in depression. Further investigation is needed for replication with more stringent seasonal pattern evaluation, and for exploring the exact biological functions of the identified genetic variants.

Challenges and Future Trends for Microarray Analysis.

The current situation in microarray data analysis and prospects for the future are briefly discussed in this chapter, in which the competition between microarray technologies and high-throughput technologies is considered under a data analysis view. The up-to-date limitations of DNA microarrays are important to forecast challenges and future trends in microarray data analysis; these include data analysis techniques associated with an increasing sample sizes, new feature selection methods, deep learning techniques, covariate significance testing as well as false discovery rate methods, among other procedures for a better interpretability of the results.

Cerebral blood flow and cerebrovascular reactivity correlate with severity of motor symptoms in Parkinson's disease.

Background:Parkinson’s disease (PD) is a progressive neurodegenerative disorder that is mainly characterized by movement dysfunction. Neurovascular unit (NVU) disruption has been proposed to be involved in the disease, but its role in PD neurodegenerative mechanisms is still unclear. The aim of this study was to investigate cerebral blood flow (CBF) and cerebrovascular reactivity (CVR) within the regions belonging to the motor network, in patients with mild to moderate stages of PD.Methods:Twenty-eight PD patients (66.6 ± 8.6 years, 22 males, median [interquartile range, IQR] Hoehn & Yahr = 1.5 [1–1.9]) and 32 age- and sex-matched healthy controls (HCs) were scanned with arterial spin labeling (ASL) magnetic resonance imaging (MRI) for CBF assessment. ASL MRI was also acquired in hypercapnic conditions to induce vasodilation and subsequently allow for CVR measurement in a subgroup of 13 PD patients and 13 HCs. Median CBF and CVR were extracted from cortical and subcortical regions belonging to the motor network and compared between PD patients and HCs. In addition, the correlation between these parameters and the severity of PD motor symptoms [quantified with Unified Parkinson’s Disease Rating Scale part III (UPDRS III)] was assessed. The false discovery rate (FDR) method was used to correct for multiple comparisons.Results:No significant differences in terms of CBF and CVR were found between PD patients and HCs. Positive significant correlations were observed between CBF and UPDRS III within the precentral gyrus, postcentral gyrus, supplementary motor area, striatum, pallidum, thalamus, red nucleus, and substantia nigra (pFDR < 0.05). Conversely, significant negative correlation between CVR and UPDRS III was found in the corpus striatum (pFDR < 0.05).Conclusion:CBF and CVR assessment provides information about NVU integrity in an indirect and noninvasive way. Our findings support the hypothesis of NVU involvement at the mild to moderate stages of PD, suggesting that CBF and CVR within the motor network might be used as either diagnostic or prognostic markers for PD.

Leveraging Novel Statistical Methods And Big Data To Improve Imaging Genetics

Overall Abstract Background The brain is a highly heritable organ with obvious associations to psychiatric diseases. Despite initial hype at the beginning of this century, it has been difficult to find brain related genotype-phenotype associations that replicate in independent samples. However, with the recent emergence of large consortia such as ENIGMA and ABCD, harmonized processing and analysis protocols across multiple sites allows for unprecedented power to detect genetically determined brain variation. In addition, novel statistical methods for big data, customized to the polygenic nature of brain phenotypes, have increased the potential to quantify variance due to genetic factors, and to identify genetic loci that replicate across GWAS samples. Here, we present the potential of combining large scale brain imaging data collection with novel biostatistical tools, and provide an update of the field related to the following topics: 1) Novel statistical approaches for imaging genetics (Thompson et al). This talk will propose novel statistical approaches for imaging genetics data derived from the field of Bayesian psychometrics. This talk will also cover how these methods will be applied to the Adolescent Brain and Cognitive Development (ABCD) study, a nationwide 19 site NIH funded study that will recruit over 10,000 children aged 10-11 and followed for 10 years, with genetics, a twin component, and brain imaging every other year. The ABCD repository, which will be released to the public beginning December 1, 2017 and which will be of great value for imaging genetics studies, will be described in detail. The speaker, Dr. Thompson, is the Director of Biostatistics for the ABCD Consortium. 2) ENIGMA update - discoveries of novel brain associated gene variants (Hibar et al). Results from most recent GWAS of brain structure provides novel insight into common variants associated with cortical thickness and surface area using meta-analytic framework in more than 20,000 participants. Novel genome-wide significant variants were observed in the lateral occipital and pericalcerine gyri. These results provide insights into the common genetic architecture of the brain in the largest effort to date. 3) Bayesian approach to PGC - ENIGMA data reveals overlapping variants between schizophrenia and brain structure volumes (Smeland et al). Combined analysis of GWAS data using conditional false discovery rate methods provides increased power to detect overlapping genetic loci. Using this approach, we here identified genetic loci shared between schizophrenia and volumes of hippocampus, putamen and intracranial volume, suggesting novel molecular genetic mechanisms. 4) Imaging endophenotype revisit (Fan et al). Although neurodevelopmental processes seldom occur in discretized sets, imaging genetic analyses often rely on measures of morphologically defined region of interests (ROI). However, arbitrarily defined ROI can sometimes introduce bias. With larger training data available, it is now possible to extract reliable imaging features pertaining to genetic variations other than traditionally defined ROI. Here we demonstrate recent studies on learning new endophenotypes and the yield for discovering genetic influences on neuroanatomy. The learnt manifolds of anatomical structure can either be used for correcting the bias, e.g. population structure, or improving the power to detect genetic effects. This is important for extending imaging genetics research beyond caucasian samples.

Inter-ictal network of focal epilepsy and effects of clinical factors on network activity

ObjectiveAim of the study was to explore the inter-ictal, resting-state EEG network in patients with focal epilepsy (FE) and to specify clinical factors that influence network activity. MethodsFunctional EEG connectivity (EEGfC) differences were computed between 232 FE patients (FE group) and 77 healthy controls. EEGfC was computed among 23 cortical regions within each hemisphere, for 25 very narrow bands from 1 to 25 Hz. We computed independent effects for six clinical factors on EEGfC in the FE group, by ANOVA and post-hoc t-statistics, corrected for multiple comparisons by false discovery rate method. ResultsRobust, statistically significant EEGfC differences emerged between the FE and the healthy control groups. Etiology, seizure type, duration of the illness and antiepileptic treatment were independent factors that influenced EEGfC. Statistically significant results occurred selectively in one or a few very narrow bands and outlined networks. Most abnormal EEGfC findings occurred at frequencies that mediate integrative and motor activities. ConclusionsFE patients have abnormal resting-state EEGfC network activity. Clinical factors significantly modify EEGfC. SignificanceDelineation of the FE network and modifying factors can open the way for targeted investigations and introduction of EEGfC into epilepsy research and practice.

Brain structure and neurological and behavioural functioning in infants born preterm.

To examine: (1) relationships between brain structure, and concurrently assessed neurological and behavioural functioning, in infants born preterm at term-equivalent age (TEA; approximately 38-44wks); and (2) whether brain structure-function relationships differ between infants born very (24-29wks) and moderate-late (32-36wks) preterm. A total of 257 infants (91 very preterm, 166 moderate-late preterm; 120 males, 137 females) had structural magnetic resonance imaging (MRI) and neurological and behavioural assessments (Prechtl's general movements assessment, Neonatal Intensive Care Unit Network Neurobehavioral Scale [NNNS] and Hammersmith Neonatal Neurological Examination [HNNE]). Two hundred and sixty-three infants (90 very preterm, 173 moderate-late preterm; 131 males, 132 females) had diffusion MRI and assessments. Associations were investigated between assessment scores and global brain volumes using linear regressions, regional brain volumes using Voxel-Based Morphometry, and white matter microstructure using Tract-Based Spatial Statistics. Suboptimal scores on some assessments were associated with lower fractional anisotropy and/or higher axial, radial, and mean diffusivities in some tracts: NNNS attention and reflexes, and HNNE total score and tone, were associated with the corpus callosum and optic radiation; NNNS quality of movement with the corona radiata; HNNE abnormal signs with several major tracts. Brain structure-function associations generally did not differ between the very and moderate-late preterm groups. White matter microstructural alterations may be associated with suboptimal neurological and behavioural performance in some domains at TEA in infants born preterm. Brain structure-function relationships are similar for infants born very preterm and moderate-late preterm. Brain volume is not related to neurological/behavioural function in infants born preterm at term. White matter microstructure is related to some neurological/behavioural domains at term. Brain-behaviour relationships are generally similar for infants born very preterm and moderate-late preterm.

Cognitive Indicators of Preclinical Behavioral Variant Frontotemporal Dementia in MAPT Carriers.

The cognitive indicators of preclinical behavioral variant Frontotemporal Dementia (bvFTD) have not been identified. To investigate these indicators, we compared cross-sectional performance on a range of cognitive measures in 12 carriers of pathogenic MAPT mutations not meeting diagnostic criteria for bvFTD (i.e., preclinical) versus 32 demographically-matched familial non-carriers (n = 44). Studying preclinical carriers offers a rare glimpse into emergent disease, environmentally and genetically contextualized through comparison to familial controls. Evaluating personnel blinded to carrier status administered a standardized neuropsychological battery assessing attention, speed, executive function, language, memory, spatial ability, and social cognition. Results from mixed effect modeling were corrected for multiplicity of comparison by the false discovery rate method, and results were considered significant at p &lt; .05. To control for potential interfamilial variation arising from enrollment of six families, family was treated as a random effect, while carrier status, age, gender, and education were treated as fixed effects. Group differences were detected in 17 of 31 cognitive scores and spanned all domains except spatial ability. As hypothesized, carriers performed worse on specific measures of executive function, and social cognition, but also on measures of attention, speed, semantic processing, and memory storage and retrieval. Most notably, group differences arose on measures of memory storage, challenging long-standing ideas about the absence of amnestic features on neuropsychological testing in early bvFTD. Current findings provide important and clinically relevant information about specific measures that may be sensitive to early bvFTD, and advance understanding of neurocognitive changes that occur early in the disease. (JINS, 2019, 25, 184-194).

The Influence of Weather on the Incidence of Primary Spontaneous Intracerebral Hemorrhage

BackgroundIntracerebral hemorrhage has been associated with changes in various weather conditions. The primary aim of this study was to examine the collective influence of temperature, barometric pressure, and dew point temperature on the incidence of primary spontaneous intracerebral hemorrhage (sICH). MethodsBetween January 2013 and December 2016, patients with sICH due to hypertension or amyloid angiopathy with a known time of onset were identified prospectively. Meteorological variables 6 hours prior to time of onset were obtained from the National Oceanic Atmospheric Administration via two weather stations. Using a Monte-Carlo simulation, random populations of meteorological conditions in a 6-hour time window during the same years were generated. The actual meteorological conditions 6-hours prior to sICH were compared to those from the randomly generated populations. The false discovery rate method was used to identify significant meteorological variables. ResultsTime of onset was identified in 455 of 603 (75.5%) patients. Distribution curves for change in temperature, mean barometric pressure, and change in barometric pressure 6-hours prior to hemorrhage ictus were found to be significantly different from the random populations. (FDR approach P < .05). For a given change in temperature associated with intracerebral hemorrhage, mean barometric pressure was higher (1018 millibar (mb) versus 1016 mb, P = .03). Barometric pressure data was not influenced by variations in temperature. ConclusionsWe concluded that barometric pressure primarily influences the incidence of intracerebral hemorrhage. The association described in the literature between temperature and intracerebral hemorrhage is likely confounded by variations in barometric pressure.