dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional causal inferences via quantitative trait loci We used Mendelian randomisation to test whether changes in DNA methylation or RNA expression of genes phys ically proximal to significant Parkinson's disease risk loci were causally related to Parkinson's disease risk. To nominate genes of interest for Mendelian randomisa tion analyses, we took our putative 90 loci in the large LD reference used for the conditional and joint phase of analysis and identified SNPs in LD with our SNPs at an r² 0•5 within 1 Mb (appendix pp 9-10). Mendelian randomisation was used by integrating discov ery phase summary statistics with quan ti ta tive trait loci (QTL) association sum mary statistics across well curated methyl ation and express ion datasets. We used the curated versions of Qi and colleagues brain methylation and express ion summary statistics (multi study and multitissue metaanalysis), with a specific focus on substantia nigra data (GTEx).
6] [17] [18] [19]  For all QTL analyses, we used the multi SNP summarybased Mendelian random isation method as a framework to do Mendelian random isation. All Mendelian randomisation effect estimates are reported on the scale of an SD increase in the exposure variable relating Fixed effects meta-analysis plus conditional analyses to identify Parkinson's disease risk loci using GWAS data Additional analyses of GWAS loci investigating genetic risk at various levels, from single variants to genes, then pathways, and finally across phenotypes to a sim ilar change in Parkinson's disease risk. Simply, these Mendelian randomisation analyses com pare the local polygenic risk of an exposure (methyla tion or express ion) to similar polygenic risk in an outcome (Parkinson's disease). This method infers causal associ ations under the assumption that there is no intermediate confounder associated with both parameters and that the association is not simply due to LD.
Rare coding variant burden tests A uniformly quality controlled and imputed dataset from the International Parkinson's Disease Genomics Consortium was used to do burden tests for all rarer coding variants successfully imputed in a mean of 85% of the sample series (17 188 cases and 22 875 controls). These analyses include all variants at a hard call threshold of imputation quality more than 0•8. After annotation with ANNOVAR, we had 37 503 exonic coding variants (non synonymous, stop, or splicing) at minor allele frequency less than 5% and a subset of 29 016 at minor allele frequency less than 1%. 21 For inclusion in this phase, a gene had to contain at least two coding variants. After assembling this subset of 113 test able genes, we used the optimised sequence kernel association test to generate summary statistics at maximum minor allele frequencies of 1% and 5%.
LD score regression and causal inference To investigate correlations of Parkinson's disease genetics with that of multiple traits and diseases, we used bivariate LD score regression. 14 4] [25]  The p values from the bivariate LD score regression were adjusted for false discovery rate to account for multiple testing. Traits showing signifi cant gen etic correlations with Parkinson's disease were analysed with Mendelian randomisation methods. We excluded the UK Biobank data when a nominated trait was from summary statistics derived from the UK Biobank or if the UK Biobank was included as part of a metaanalysis. When complete GWAS summary statistics were available for traits of interest (relating to smoking and education), we used the more powerful bidirectional gen eralised summarydatabased Mendelian randomisa tion.
We analysed GWAS summary statistics for smoking initiation (453 693 records from a selfreport survey with 208 988 regular smokers and 244 705 never regular smokers) and current smoking within the UK Biobank, current smoking contrasted 47 419 current smokers versus 244 705 never regular smokers. The same analysis was done incorporating recent GWAS data regarding educa tional attainment (N=766 345) from selfreport in the UK and cognitive performance (N=257 828) as measured by the g composite score. 26 These data were analysed using methods to mirror that of the UK Biobank Parkinson's disease GWAS dataset. Combined left and right puta men volume from a T2 weighted MRI GWAS was available from Oxford Brain Imaging Genetics server (accessed Dec 28, 2018). 27 All Mendelian randomisation analyses included GWAS on the scale of ten thousand samples and overcame the considerable power demands of the methods.
The novel multisignal locus comprised independent risk variants rs2269906 (UBTF and GRN) and rs850738 (FAM171A2). Detailed summary statistics on all nominated loci can be found in the appendix (pp 4-6), including variants filtered out during additional quality control. To quantify how much of the genetic liability we have explained and what direction to take with future Parkinson's disease GWAS, we generated updated herit ability estimates and PRS. Using LD score regression on a metaanalysis of all 11 clinically ascertained datasets from our GWAS, we estimated the liabilityscale heritability of Parkinson's disease as 0•22 (95% CI 0•18-0•26), only slightly lower than a previous estimate derived using genomewide complex trait analysis (0•27, 0•17-0•38). 14, 28, 29 r the FUMA webserver version 1.
LD score regression is known to be more conservative than genomewide complex trait analysis; however, our LD score regression heritability estimate does fall within the 95% CI of this estimate. To establish the proportion of SNPbased heritability explained by our Parkinson's disease GWAS results with PRS, we used a twostage design, with variant select ion and training in the NeuroXdbGaP dataset  13  (5851 cases and 5866 controls) and then validation in the Harvard Biomarker Study (527 cases and 472 controls). Using equations from Wray and colleagues  30  and our current heritability estimates, the 88 variant PRS explained a minimum 16% of the genetic liability of Parkinson's disease assuming a global prevalence of 0•5%. 28, 30 The 1805 variant PRS explained 26% of Parkinson's disease heritability.
There were 305 genes within the 78 GWAS loci. We sought to identify the probable causal gene or genes in each locus using large QTL datasets and summary databased Mendelian randomisation (table 3, appendix pp 8-9). 17 This method allows for functional infer ences between two datasets to be made in an analogous frame work to a randomised controlled trial, treating the genotype as the randomising factor. Of the 305 genes under LD peaks around our risk variants of interest, 237 were possibly associated with at least one QTL in public reference datasets and were therefore testable via summarybased Mendelian random isation (appendix pp 8-9). The expression or methyla tion of 151 (64%) of these 237 genes was significantly associated with a possible causal change in Parkinson's disease risk.
The OR column is the exponent of the regression coefficient (β) from logistic regression of the PRS on case status, with the SE representing the precision of these estimates. These same metrics are derived across array types and datasets using random-effects meta-analyses. The area under the curve is included as the most common metric for predictive model performance. PRS=polygenic risk score. OR=odds ratio. IPDGC=International Parkinson's Disease Genomics Consortium. HBS=Harvard Biomarker Study. *r 2 approximation adjusted for an estimated prevalence of 0•5%, equivalent to roughly half of the unadjusted r 2 estimates for the PRS. All calculations and reported statistics include only the PRS and no other parameters after adjusting for principal components 1-5, age, and sex at variant selection in the NeuroX-dbGaP dataset  13.
Intracranial volume could not be tested because the GWAS data (available from Oxford Brain Imaging Genetics server) did not contain any genome wide significant risk variants. No significant causal rel ationship was observed between Parkinson's disease and    We were able to explain 16-36% of Parkinson's disease heritability, the range being directly related to varying prevalence estimates (0•5-2•0%). Power estimates sug gest that expansions of case numbers to 99 000 cases will continue to reveal additional insights into Parkinson's disease genetics. Although these risk variants will have small effects or be quite rare, they will help to further expand our knowledge of the genes and pathways that drive Parkinson's disease risk. Populationwide screening for individuals who are likely to develop Parkinson's disease is currently not feasible using our 1805 variant PRS alone.
44 e found four phenotypes that were genetically cor related with Parkinson's disease. Putamen and intra cranial volumes might prove to be valuable in future Parkinson's disease biomarker studies. Our bidirectional generalised summarydatabased Mendelian randomisa tion results suggest a complex causative connection between smoking initiation and Parkinson's disease that will require further followup. One of the implications of this work is that Parkinson's disease trials of nicotine or other smoking related compounds might be less likely to succeed due to the marginal strength of associations shown in this report affecting study power. The strong causal effect of cogni tive performance on Parkinson's disease is supported by observational studies. 45 lthough this study marks major progress in assessing genetic risk factors for Parkinson's disease, much remains to be established.
No defined external validation dataset was used, which could be seen as a limitation. However, due to the size of the dataset, it is considered infeasible to build a sufficiantly large replication series. Also, external replication of the novel associations we present will be difficult simply because of the sample sizes needed. Simulations have suggested that, without replication, variants with p values between 5 × 10 -⁸ and 5 × 10 -⁹ should be interpreted with greater caution. 46, 47 We found 16 risk variants in this range, including two known variants near WNT3 (proximal to the MAPT locus) and BIN3. To a degree, the fact that we filtered our vari ants with a sec ondary randomeffects metaanalysis could make our 90 Parkinson's disease GWAS hits somewhat more robust because of the conservative nature of randomeffects.
This study focused on Parkinson's disease risk in individuals of European ancestry. Adding datasets from nonEuropean populations would be helpful to further improve our granularity in association testing and ability to finemap loci through integration of more variable LD signatures while also evaluating populationspecific associations. Also, risk predictions might not generalise across populations in some cases and ancestry specific PRS should be investigated. Additionally, large ancestry specific Parkinson's disease LD reference panels, such as those for patients who are Ashkenazi Jews, will help us further unravel the genetic risk of loci such as GBA and LRRK2. This clarification might be particularly crucial at these loci where LD patterns could be variable within European populations, accentuating the possible influ ence of LD reference series on con ditional analyses in some cases.
48 Finally, our work used stateoftheart QTL datasets to nominate candidate genes, but many QTL associations are hampered by both small sample size and low cisSNP density. Larger QTL studies and Parkinson's diseasespecific network data from large scale cellular screens would allow us to build a more robust functional inference framework. As the field moves forward there are some crucial next steps that should be prioritised. First, allowing researchers to share participantlevel data in a secure environment would facilitate inclusiveness and uniformity in analyses while maintaining the confidentiality of study participants. Our work suggests that GWAS of increasing size will con tinue to provide useful biological insights into Parkinson's disease. In addition to studies of the genetics of Parkinson's disease risk, studies of disease onset, progression, and subtype will be important and will require large series of well characterised patients.
loci discovery New and unpublished GWAS Previously published GWAS Gene-level analyses Heritability and polygenic risk estimates Shared heritability Gene set analyses Nalls and UK Biobank 37•7 thousand LDSC used to Mendelian WebGestaltR* LD Hub survey colleagues, 18 618 proxies cases quantify randomisation to investigate of 757 curated 2014 6 436 419 controls 18•6 thousand heritability of QTLs biological GWAS datasets 13 708 cases proxies pathway 95 282 controls SGPD 1•4 million Predictive Rare coding enrichment in Significant 1169 cases controls models variant GWAS loci results from IPDGC-NeuroX 13 968 controls 7•8 million SNPs developed burden analysis LDSC were 5851 cases using polygenic FUMA to followed up 5866 controls PDWBS (Chang and colleagues, 2017 4 ) 302 042 controls 6476 cases 571 411 controls 2448 cases IPDGC 8036 cases 5803 controls Post-Chang, 23andMe 90 independent risk signals (38 novel) 305 candidate genes under LD peaks future, larger estimates for risk scoring here and Quantification of detected heritable risk investigate tissue and cell type enrichment in GWAS loci using Mendelian randomisation to infer causal associations GWAS.
Data were transcribed, translated to English, and coded and analyzed using Dedoose software and a codebook developed a priori. Results: This paper reports low levels of knowledge of obstetric and newborn complications among men and limited male involvement during pregnancy, childbirth and the post-partum period in Jigawa state. Men are key decision-makers around the location of the delivery and other decisions linked to maternal and newborn health, and they provide crucial resources including nutritious foods and transportation. However, they generally do not accompany their wives to antenatal visits, are rarely present for deliveries, and do not make decisions about complications arising during delivery and the immediate post-partum period. These gendered roles are deeply ingrained, and men are often ridiculed for stepping outside of them.
Methodology The qualitative study was conducted between June and November 2015 among families experiencing maternal and neonatal illness and deaths in 24 rural Local Governmental Areas (LGAs) in Jigawa State, Northern Nigeria. The households sampled for the qualitative data collection were drawn from the study population of a large cluster RCT of community-based interventions to reduce maternal mortality in Jigawa State, Northern Nigeria. The RCT was implemented by the Abdul Latif Jameel Poverty Action Lab (J-PAL) and the Planned Parenthood Federation of Nigeria (PPFN) to assess the impact of three interventions: 1) training local women as Community Resource Persons (CoRPs) who provide education and referrals to pregnant women and their families including husbands, 2) the CoRPs program plus distribution of safe birth kits to pregnant women, 3) the CoRPs program plus community dramas to change social norms on maternal health.
The qualitative study included 40 event narratives with families who had experienced a maternal or newborn complication or death, in-depth interviews with 10 husbands and four community leaders, as well as four focus group discussions (FGDs) with CoRPs. The main results of the qualitative study have been presented elsewhere  [24]. For this analysis we focus primarily on data from the male and community leader IDIs as well as the FGDs with CoRPs. Data specific to male involvement from the 40 illness narratives are also included in the analysis.
Study site and background Jigawa state reported a population of 4. 3 million during the 2006 census  [25]. The state is divided into  27  LGAs, with 80% of the population living in rural areas  [25]. The RCT was conducted in 96 clusters of villages across  24  LGAs, covering an estimated population of 280,000. LGAs were included if they had a PHC that was part of the MSS. The RCT baseline sample consists of women of reproductive age in a 15% subsample of households randomly selected at baseline between December 2011 and May 2012 (N = 7069). A RapidSMS surveillance system, where local women were trained in each village to report vital events using text messaging, was implemented to track births and deaths of women and infants. For all births in baseline households, questionnaires were administered within three days after birth, and at 28 days after birth to capture data on the pregnancy, delivery, and post-partum period.
Study design and sampling method The qualitative study included data on 40 cases, equally divided between four categories (maternal deaths, reported PPH, neonatal deaths, and neonatal illnesses); maternal and newborn cases were included from both the control and CoRPs arms of the RCT. Potential cases were identified sequentially, using the RapidSMS surveillance system, until the target numbers were reached. Reported PPH, neonatal illness and neonatal death cases were identified via maternal and newborn complication data from the three and 28-day after birth questionnaires, while maternal death cases included those where the death had been verified and a verbal autopsy conducted. Those cases that met the study eligibility criteria via the surveillance data were verified by the field team and then a follow-up visit was conducted for the purpose of qualitative data collection.
Data were collected by trained interviewers and note-takers (two male and two female) who conducted interviews in pairs in Hausa. These data collectors were recruited from the study areas specifically for this research and their age and acceptability to the target population were important considerations in the selection process. Interviews were audio recorded and transcribed, and expanded notes were created using notes, memory and the audio. In-depth interviews were on average between 20 and 60 min, while FGDs were between 40 and 90 min. Supervisors provided ongoing assistance for quality assurance and periodic re-training. Interviewers completed a debriefing template following each interview including additional notes about the data collection process.
Analysis Qualitative data were transcribed verbatim by the field team, translated to English and cross-checked for accuracy by Hausa speakers who compared English transcripts to the audio recordings. A codebook was developed a priori and used as a basis for coding the expanded notes. Dedoose software (http://www. dedoose. com/) was used for the coding and analysis of the anonymized data by two researchers (VS, NG). Coding focused on emerging themes related to knowledge of danger signs in pregnancy, during delivery and post-partum as well as in newborns, involvement of men in maternal and newborn health as well as around recognition of illness, decisionmaking, and patterns of care-seeking. Thematic content analysis was conducted to understand the spectrum of male involvement across the continuum of maternal and newborn health care and whether perspectives varied by type of interviewee (women, men, CoRPs).
Background characteristics Ten IDIs with husbands, four IDIs with community leaders, four FGDs with 28 female CoRPs and 40 illness narratives with a total of 82 men and women were completed (See Table  2 ). The husbands were between 23 and 40 years of age, had zero to 15 living children and had completed varying levels of education. Community leaders were older and included men between 30 and 70 years of age with six to eight years of education. The CoRPs were women between 30 and 50 years of age, while the illness narratives included women and men between 18 and 60 years of age. Interviewees from  16  LGAs across Jigawa state participated in the data collection.
Additionally, community health programs such as the CORPs program have been unable to systematically engage with men because of these gendered norms and expectations, and there are limited opportunities for men to learn about maternal and newborn health. Finally, similar to several other studies  [35] [36] [37]  health facilities in this area were reported to be particularly unwelcoming to men and family members which also impeded male involvement. This study has several strengths. First, it includes a sample drawn from  24 distinct  LGAs. The study was prospective and involved case-finding using real-time surveillance. These elements increase diversity of the sample and ensure the inclusion of perspectives from a wide geographic area. Data was also collected from female members of the households where men were interviewed which permitted assessment of consensus between males and females in these areas.
Acknowledgements This study was made possible by the support of the American people through the  United States Agency for International Development (USAID). The findings of this study are the sole responsibility of J-PAL, and do not necessarily reflect the views of USAID or the  United States Government. We also acknowledge the support received from the  John D. and Catherine T. MacArthur Foundation , the  Planned Parenthood Federation of Nigeria, National Population Commission of Nigeria , the  Jigawa Ministry of Health , the  Jigawa State Operations Research Committee (ORAC) , the leadership of  Jigawa State  at all administrative levels, the village leaders in the study communities, the field team and the community members for their ongoing support and participation. We also acknowledge  Abdullahi Bulama ,  Kelvin Chukwuemeka ,  Dr.
Morphometric brain changes occur throughout the lifetime and are often investigated to understand healthy ageing and disease, to identify novel biomarkers, and to classify patient groups. Yet, to accurately characterise such changes, an accurate parcellation of the brain must be achieved. Here, we present a manually-parcellated dataset of the superior frontal, the supramarginal, and the cingulate gyri of 10 healthy middle-aged subjects along with a fully detailed protocol based on two anatomical atlases. Gyral parcels were hand-drawn then reviewed by specialists blinded from the protocol to ensure consistency. Importantly, we follow a procedure that allows accounting for anatomical variability beyond what is usually achieved by standard analysis packages and avoids mutually referring to neighbouring gyri when defining gyral edges.
Background & Summary To study large morphological brain changes associated with ageing and disease, magnetic resonance imaging (MRI) data of the brain are acquired then analysed, often by segmenting tissue types (i. e. grey matter, white mater, cerebro-spinal fluid) and parcellating the brain into regions which are defined by gyri and sulci. Most gyral volumes that are publicly available have been derived either automatically or semi-automatically. The latter group typically stems from automated parcellation (or segmentation), followed by manual changes, correcting for errors often due to inaccuracies in the segmentation and/or the parcellation scheme  1, 2. Because of the manual aspect, they are usually classified as 'manuallygenerated'. These datasets are considered as the 'gold standard' and exist for the various brain regionscortical  2, 3  , subcortical  4  , abnormal (such as tumours or lesions  5  ), and the whole brain (LPBA40  6  ; NeuAtlas (Neuromorphometrics, Inc.