dataesr/scientific-paragraphs-categorization huggingface dataset:
It already includes structural MRI data from 1,850 patients and 3,483 controls. Data analysis is currently in progress. An additional working group on attention deficit/ hyperactivity disorder is currently being formed, ENIGMA-ADHD, which will analyze data from more than 1,500 cases (children and adults) with the disorder and over 2,000 controls. An even more recent extension is a developing ENIGMA-Addiction working group that will analyze data relevant to addiction phenotypes including case-control comparisons across a variety of substances, and going beyond this to examine the influence of comorbidities, gender and stages of disorder. The ENIGMA meta-analytic approach will be used to aggregate data from case-control and developmental cohorts to examine the relative contribution of various genetic and brain correlates to risk for early onset substance misuse, transition to regular use, susceptibility to dependence, and individual differences in relapse vulnerability.
We will also examine variants common to all addictive behaviors, and attempt to identify those that might be specific to homogeneous addiction profiles. To date, a pooled sample of 8,000 participants with neuroimaging, genetics and addiction phenotypes has been identified; their data will be re-analyzed to address these questions. Clearly, these studies aggregate data from cohorts that are heterogeneous in terms of duration of illness, disease etiology, medication history, demographics, exposure to potentially neuroprotective substances such as lithium, and many other Fig. 5  Locations of the ENIGMA Working Groups. After ENIGMA's first project was completed (ENIGMA1;  Stein et al. 2012) , large amounts of brain imaging data had been analyzed from patients with a variety of psychiatric disorders.
Working groups (WGs) were formed to understand the effects on the brain of bipolar disorder, major depressive disorder (MDD), schizophrenia, by pooling and comparing data from many neuroimaging centers. These groups are open to any researchers who have collected MRI scans from patients with these illnesses. No genetic data is needed to join. In fact, most projects study factors that might influence how these disorders affect the brain-medications, geographic factors, and the age and gender of the patient. A further working group focuses on diffusion tensor imaging, which assesses white matter integrity; current projects relate DTI measures to individual differences in cognition and genetic make-up. The institutions in the working groups, as of June 2013, are shown on the map (see color key, inset) factors.
ENIGMA's working groups on psychiatric disorders are meta-analyzing representative datasets from schizophrenia, bipolar, depressed, ADHD, autism, OCD, 22q deletion syndrome, HIV, addiction, and other cohorts worldwide. Eventually, cross-disorder comparisons should be able to identify common and distinctive patterns of brain morphometry, and how they depend on genetic variation, and diagnostic criteria. Psychiatric cohorts vary in their inclusion and exclusion criteria, and in duration of illness, medication history, ethnicity, and in other demographics. Some studies include patients with co-morbid conditions deliberately excluded by other studies. The diversity of psychiatric cohorts in ENIGMA suggests a second wave of analyses to understand cohort-specific factors that might account for, or contribute to, the heterogeneity in results across sites.
Much of this overview of the history and future efforts of ENIGMA highlights its relevance to studies of disease, focusing on psychiatric and neurodegenerative disorders such as Alzheimer's disease. Even so, only around a third of the ENIGMA data comes from patients with psychiatric illness, and much can be learned about the genetic factors that drive normal variation in the general population. A great deal of fundamental information on the biology of the human brain can be discovered from efforts such as ENIGMA, irrespective of whether it has a direct relevance to any specific disease. There are limitations to a study like ENIGMA despite its strengths. The first is that many other types of genetic or epigenetic variation other than GWAS are important-rare variants, CNVs, expression and methylation analyses are all crucial; they simply have not yet been evaluated through ENIGMA, but that is likely to change in the future.
So far, people in Africa and Asia are under-represented on the ENIGMA map; we are therefore keen to include samples from these continents. Third, the need for multivariate analysis has long been known in the neuroimaging field-no serious neuroscientist would predict diagnosis from a single pixel or voxel in an image-the multivariate pattern of signals is paramount. Methods to access and recover the maximum amount of pertinent information in an image dataset are only just in their infancy in the fields of genetics and neuroimaging-partially due to the lack of sufficient data to test competing methods, until recently  (Liu et al. 2009; Stein et al. 2010; Hibar et al. 2011b; Vounou et al. 2010; Le Floch et al. 2012; Rosenblatt et al. 2013; Meda et al. 2010 Meda et al. , 2012)). Fourth, the discovery of a GWAS hit-in ENIGMA or any other GWAS study-is the beginning of a long road of discovery, especially if the finding is intergenic or in a gene of unknown function.
Some genomic screens of anatomical or structural connectivity data have implicated genes such as SPON1  (Jahanshad et al. 2013b ) and FRMD6  (Ryles et al. 2012 ) that were discovered in later case-control studies to be risk genes for AD  (Hong et al. 2012; Sherva et al. 2013). Functional validation of genetic variants reliably implicated in large scale studies will be the way we learn new biological processes and further our understanding of risk for psychiatric diseases. Fifth, ENIGMA has started by analyzing those phenotypes that are easily measured in a standardized way. Brain images can be analyzed in more sophisticated ways than traditional morphometric methods; shape analysis, for example, has long been used to characterize features of brain structure, including cortical complexity, curvature, fractal dimension, spectral content, and other indices.
Also, the reporting of regional summaries from DTI data clearly does not exploit all of the available information in the data-DTI can be analyzed using automatic whole-brain tractography to reveal the brain's fiber patterns and measure connectivity, and even the topology of these brain networks. Despite the ever-expanding landscape of brain features that can be studied, the large samples required for genetic analysis have motivated the study of the simplest brain measures first. Undoubtedly the future will hold large scale genetic studies of brain connectivity, anatomical shapes, functional networks, and features that are as yet unknown and undiscovered. As a final limitation, ENIGMA analyses have been cross-sectional to date, rather than longitudinal. Genetic predictors of brain changes over time have substantial importance for clinical trial enrichment (e.
If such effect sizes are typical, sample size requirements will generally be larger in genetic association studies than in neuroimaging studies of disease effects on the same phenotypes, but not vastly larger. To some researchers, these preliminary observations suggest an expected pattern of effect sizes, whereby GWAS for cognitive traits may have top hits with smaller effect sizes than those for imaging traits, which in turn may tend to have smaller effect sizes than expression traits. Also, now that consistent hits have been identified in ENIGMA1 and ENIGMA2, it should be possible to estimate effect sizes for the same hits in new replication samples, to understand what sample sizes are sufficient to detect them. Conversely, some have argued that the available data on genetic effect sizes at different levels of neuroscience are currently too limited to draw conclusions from only the three data points cited here.
Clearly, we do not yet have sufficient information to say whether imaging genetics effects sizes will always be larger than effect sizes for cognitive/behavioral phenotypes. One way to begin to address this question might be to quantify effects of some specific functional SNP at three different related levels, such as eQTL data from the hippocampus, hippocampal volume on MRI, and a behavior that is tightly dependent on hippocampal function; this may show the appropriate differential effect sizes in one framework. A further interesting angle is the follow-up of the top ENIGMA1 hits in ethnically isolated cohorts of Dagestan and Chechnya by  Bulayeva et al. (2013). Genetic isolates can be valuable for studying any human phenotypes; required sample sizes for studying genetic effects can be smaller than in heterogeneous outbred populations due to the genetic homogeneity of these isolates.
This has long been noted by classical statistical and population geneticists (e. g. ,  Falconer 1960; Neel 1992; Bulayeva et al. 2005). As genetic isolates have a high rate of traditional endogamy and inbreeding, it is not possible to perform GWAS, but genome-wide linkage analysis is possible  (Sheffield et al. 1998). In particular, the analysis of isolated populations makes it less challenging to study polygenic disorders by reducing the number of loci possibly involved in the disorder. ENIGMA now focuses on genetic analysis of neuroimaging measures, but psychiatric diagnosis is important as well. ENIGMA does not simply relate imaging data to genetics; many of its working groups study the relationship between imaging measures and diagnosis. So, in a sense, psychiatric diagnosis is also a key target of study.
2012b; Jahanshad et al. 2013b). In most analyses, multivariate refers to condensing information on the imaging side, not the genetic side, although both methods and joint methods are emerging. Multivariate methods can be quite sophisticated mathematically. Some draw upon a century of powerful work in classical quantitative genetics and twin designs. Chiang and colleagues  (Chiang et al. 2011 (Chiang et al. , 2012)) , for example, computed the "cross-trait crosstwin correlation" between all pairs of voxels in an image, to pull out "image clusters" with common genetic determination (see also  Chen et al. 2011 Chen et al. , 2012a)). Others have incorporated knowledge of the genetic or imaging relationships to guide the solution  (Chen et al. 2013). Such tactics, among others, offer a principled way to mine high-dimensional datasets, boosting power for any subsequent GWAS.
Study area and participants A FGS case definition study was carried out in June-July 2010 in Miandrivazo district, Madagascar headed by the Institut Pasteur de Madagascar (IPM). The study participants were recruited from five villages in Miandrivazo district, which is located in the western part of Madagascar. Four of the chosen villages were identified as potential hyper-endemic villages (. 50% S. haematobium prevalence) in previous surveys conducted in 2009 by IPM in collaboration with the schistosomiasis unit at the Madagascar Ministry of Health. These surveys showed a S. haematobium eggs in urine prevalence of 82% (61/74) in one village and 84% (89/106) based on pooled data from two high endemic villages. In the last high endemic village the prevalence of S. haematobium was based on observation of a high prevalence of macro-haematuria as reported by the local medical officer.
Data analysis ELISA absorbance readings and protein concentration calculations were handled using SkanIt3. 1. 0. 4 software. Only standard curves with a correlation co-efficient (r) of minimum 0. 95 were accepted, and protein concentrations were determined using linear regression. Data were analysed using SPSS 19. 0. 0 for Windows (IBM SPSS Inc. ), and the graphical representation in figure  1  was made using GraphPad Prism 4. 03 software. Spearman's correlation co-efficient was used to describe the relation between granule proteins and mean egg counts. Two-tailed non-parametric Mann-Whitney U (2-sample) and Kruskall-Wallis (k-sample) tests were used to test for significant differences in protein levels in independent samples. In order to test independent variables for significance, multinominal logistic regression models using pathology categories as dependent variable were run (protein levels were normally distributed after log transformation).
Study Design and Participants This study utilized data from participants from three independent study groups composed of six GWA studies. All individuals included in this study have provided written informed consent. The first group was the Medical Research Council (MRC)-Wellcome Trust Case Control Consortium (WTCCC2) group including 3,292 individuals with LOAD (cases), 1,223 older individuals without LOAD (controls), and 5,074 individuals from the general population (population controls), consisting of four cohorts: 3,216 cases and 1,165 controls from the GERAD1 cohort (Genetic and Environmental Risk for Alzheimer's disease) consortium  [4]  genotyped on the Illumina 610-quad chip; 18 LOAD cases and ten controls from the MRC Brain cohort, genotyped on the Illumina 666W-Quad chip; and 5,074 population controls from the WTCCC2 publically available control cohorts (www.
wtccc. org. uk/ ccc2/: the 1958 British Birth Cohort  [WTCCC2 1958 BC]  and UK Blood Service Collection [WTCCC2 National Blood Donors (NBS)]), genotyped on the Illumina 1. 2M chip. The second group was the Institute of Psychiatry Plus (IOP+) group including 450 individuals who were cases, and 365 individuals who were controls, from the AddNeuroMed (ANM) cohort (362 cases, 237 controls)  [21] , and individuals from the Dementia Case Register (88 cases and 128 controls). These groups were genotyped on the Illumina 610-Quad chip in two different batches and merged together (batch 1: 222 cases, 111 controls; batch 2: 228 cases and 254 controls). Additionally, batch 1 contained 99 and batch 2 contained 78 individuals with mild cognitive impairment (MCI). The third group consisted of 330 individuals who were cases and 187 who were controls obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.
loni. usc. edu), genotyped on the Illumina 610-Quad chip. Further details on all groups and cohorts are found in Text S1. All individuals contributing data for this study were white. All individuals with LOAD (cases) met criteria for either probable (NINCDS-ADRDA, DSM-IV) or definite (CERAD) AD  [22]. All non-population individuals who were controls were screened for dementia using the MMSE or ADAS-cog, or were determined to be free from dementia at neuropathological examination or had a Braak score #2. 5. All individuals with LOAD (cases) had an age of onset $60 years and controls were $60 years at examination. The 58BC individuals (population controls) were around 54 years at the age of collection and no age was available for the NBS cohort. Because genotype data were used from multiple sources, stringent quality control (QC) filters were applied since differential genotyping error rates between groups could result in spurious associations when the data are combined  [23, 24].
GWA Quality Control Individual QC filters in the MRC, WTCCC2 58C, WTCCC2 NBS, IOP+, and ADNI datasets were applied using tools implemented in PLINK  [25]. QC for the IOP+ group took place separately for the two different batches. Briefly, we excluded individuals with (a) gender mismatches (M. 0. 8, male; F,0. 2, female rule in PLINK; (b) an individual call rate #98%; (c) individuals with autosomal heterozygosity outside 64 standard deviation (SD) of the mean heterozygosity; and (d) duplicates and cryptically related by calculating identity by descent (IBD) estimates for all possible pairs of individuals in PLINK and removing one of each pair with an IBD estimate $0. 1875 (the level expected for second cousins). Each of the five datasets were then merged with genotypes from 210 unrelated European (CEU), Asian (CHB and JPT), and Yoruban (YRI) samples from the HapMap project (www.
hapmap. org). Following removal of SNPs in extensive regions of linkage disequilibrium and pruning of SNPs if any pair within a 50-SNP window had r 2. 0. 2, principal components analysis (PCA) as implemented in SMARTPCA  [26]  was used to infer continuous axes of genetic variation. Eigenvectors were calculated on the basis of the linkage disequilibrium (LD)-pruned subsets of each of the merged datasets to identify and then remove individuals of divergent ancestry displayed by plotting the first two principal components and using K-means clustering. EIGENSOFTplus  [27]  was then applied to each of the datasets to additionally correct for population substructure, and genetic outliers defined as individuals whose ancestry is at least 6 SDs from the mean on one of the top ten axes of variation were removed.
Final QC Steps The MRC dataset was then merged together with the WTCCC2 58C and WTCCC2 NBS datasets and the two IOP+ batches were also merged together. Symmetric SNPs were excluded and PLINK was used to identify incorrect strand assignment by utilizing LD patterns and exclude SNPs where the strand may have been incorrectly assigned between the three datasets. The merged datasets underwent an additional round of individual QC as described above and EIGENSOFTplus was applied to correct for population substructure as described. Four principal components explained most of the variation in the data and were extracted to be used as covariates in further analyses. Final QC resulted in 3,234 individuals with LOAD (cases), 1,175 individuals who were controls, and 4,989 individuals who were population controls from the MRC-WTCCC2 group (n = 9,398); 350 individuals with LOAD (cases) and 313 individuals who were controls from IOP+ group (n = 663); and 330 individuals with LOAD (cases) and 187 individuals who were controls from the ADNI group (n = 517), a total of 10,578 individuals included in all analyses.
Outcomes The main outcome of the study was LOAD status (dichotomous) in the 10,578 individuals included in analyses with available imputed data. A total of 227 individuals with LOAD (cases) and 196 individuals who were controls of the ANM cohort of the IOP+ group had HDL-c, LDL-c, TC, and TG serum levels (mmol/l) available. Additionally, lipid serum data were also available for 127 individuals with MCI from the ANM cohort who had undergone GWA/imputation together with LOAD cases and controls of the ANM cohort.
Instrumental variable analysis using individual level data. For the main analysis of this study we investigated the association of each of the four lipid trait GRSs with LOAD. Logistic regression analyses were used to test for the association of each GRS with LOAD separately in each group adjusting for the first four PCS extracted during QC for each group. It was not possible to include a covariate for each chip for the MRC-WTCCC2 group as only population controls were genotyped on the 1. 2 M chip. Similarly, it was not possible to include a covariate for each of the four cohorts of the MRC-WTCCC2 group, as the WTCCC2 groups included only population controls. The IOP+ cohort was adjusted for an additional covariate denoting genotyping batch. The instrumental variable estimate for each lipid trait was obtained by dividing the LOAD-GRS log OR estimate of each group (second stage equation) with the respective beta estimate of the linear regression of each lipid trait against the respective GRS (lipid trait-GRS) from the 550 individuals of the ANM study with serum lipid data available (first stage equation).
To take into consideration the uncertainty in both the LOAD-GRS and the lipid trait-GRS associations we used the delta method to estimate the standard errors of the instrumental Table  1. Characteristics of the participants in the MRC-WTCCC2, the IOP+, and ADNI study groups who passed GWA and imputation QC, broken down by cohort and by disease status. variable ratio estimates  [30]. The resulting estimates were pooled together using inverse-variance fixed effects meta-analysis. We acknowledge that our instrumental variable (lipid trait-GRS associations) is calculated using only 550 participants from the ANM study. A recent study  [31]  has demonstrated that generating exposure data (such as lipid trait-GRS data) for a subset of participants rather than all participants in the study or from participants who are obtained from independent non-overlapping samples drawn from the same population does not substantially decrease power when the instrumental variable is relatively strong.
An instrumental variable is considered strong when the first stage equation R 2 <2. 5% and, as demonstrated by Pierce and Burgess  [31] , full power is achieved for an R 2 $1. 5% with exposure data for ,20% of the total sample. Additionally, loss in power is very small when the subsample/independent sample is 5%-10% of the total study sample. In our study groups, exposure data for the lipid trait-GRS association is available for 6% of the sample size of the MRC-WTCCC2 group (independent sample), for 83% of the IOP+ group (sub-sample), and for more participants than those in the ADNI group (independent sample). We have therefore used the instrumental variable estimate when the first stage equation for the four traits was R 2 $1. 5%. Instrumental variable analysis using summary data. A second instrumental variable approach was used in order to verify findings of the instrumental variable approach using individual level data and to be used alternatively when the lipid trait-GRS first stage association was R 2 ,1.
GRSs were calculated using all independent SNPs associated with each trait (full score) and SNPs associated exclusively with each trait (trait specific score). Full score estimates were derived by weighing the association between GRS and LOAD for each dataset with the association between GRS and blood lipid trait and pooling them together using inverse-variance fixed effects meta-analysis and by using the summary method. Restricted score estimates were derived by using the summary method since the trait specific score and blood lipid estimate was a weak instrument. See Methods for further details. doi:10. 1371/journal. pmed. 1001713. g002 Scores were calculated using all independent SNPs associated with each trait (full) and SNPs associated exclusively with each trait (trait specific) for all datasets and pooled together using inverse-variance fixed effects metaanalysis.
Since there was some evidence for between study heterogeneity, random effects models were also tested but did not affect the meta-analysis results. Scores were calculated using all independent SNPs associated with each trait (full) and SNPs associated exclusively with each trait (trait specific) for all datasets and pooled together using inverse-variance fixed effects metaanalysis. Full allele scores were calculated using both raw genotype data and summary data. Trait specific allele scores were calculated using summary data only. Since there was some evidence for between study heterogeneity, random effects models were also tested but did not affect the meta-analysis results. Age for the WTCCC2 population controls was set to 60 years (See Methods). Scores were calculated using all independent SNPs associated with each trait (full) and SNPs associated exclusively with each trait (trait specific) for all datasets and pooled together using inverse-variance fixed effects meta-analysis.