dataesr/scientific-paragraphs-categorization huggingface dataset:
Mineral layer depth measurements To quantify the control that underlying mineral layer microtopography has on surface microtopography, we conducted synoptic measurements of mineral layer depth and thus organic soil thickness at each site. Within each of the 10 m diameter plots used for TLS at each site, we took 13 measurements (co-located with the randomly established validation spheres) of depth-to-mineral-layer using a steel 1. 2 m rod. At each point the steel rod was gently pushed into the soil with consistent pressure until resistance was met and the depth to resistance was recorded (resolution of 1 cm) as the "depth-tomineral-layer". We then associated each of these depth-tomineral-layer measurements with a soil elevation based on TLS data and the site-level datum (i. e. , elevation at the base of each site's well).
Hydrology We calculated simple hydrologic metrics based on the 3 years (2016-2018) of water level data for each site. For each site, we calculated the mean and variance of water level elevation relative to ground surface at the well, where negative values represent belowground water levels and positive values indicate inundation. We also calculated the average hydroperiod of each site by counting the number of days that the mean daily water level was above the soil surface at the well each year, and averaging across years.
Elevation distributions Our first line of inquiry was to evaluate the general spatial distribution of elevation at each site. We first calculated sitelevel omnidirectional and directional (0, 45, 90, and 135 • ) semivariograms using the "gstat" package in R  (Pebesma, 2004; Gräler et al. , 2016). We calculated directional variograms to test for effects of anisotropy (directional dependence) of elevation. Semivariogram analysis is regularly used in spatial ecology to determine spatial correlation between measurements  (Ettema and Wardle, 2002). The sill, which is the horizontal asymptote of the semivariogram, is approximately the total variance in parameter measurements. The nugget is the semivariogram y intercept, and it represents the parameter variance due to sampling error or the inability of sampling resolution to capture parameter variance at small scales.
From these semivariograms we calculated the best-fit semivariogram model among exponential, Matérn, or Matérn with Stein parameterization model forms  (Minasny and McBratney, 2005). We also extracted semivariogram nuggets, ranges, sills, and partial sills. Our second line of inquiry was to evaluate the degree of elevation bimodality in these systems, which is indicative of a positive feedback between hummock growth and hummock height  (Eppinga et al. , 2008). Based on the classification into hummock or hollow from our delineation algorithm, we plotted site-level detrended elevation distributions for hummocks and hollows and determined a best-fit Gaussian mixture model with Bayesian information criteria (BIC) using the "mclust" package  (Scrucca et al. , 2016)  in R (R Core Team, 2018), which uses an expectation-maximization algorithm.
Hummock spatial distributions To test whether there was regular spatial patterning of hummocks at each site, we compared the observed distribution of hummocks against a theoretical distribution of hummocks subject to complete spatial randomness (CSR) with the R package "spatstat"  (Baddeley et al. , 2015). We first extracted the centroids and areas of the hummocks using TopoSeg  (Stovall et al. , 2019)  and created a marked point pattern of the data. Using this point pattern, we conducted a nearest-neighbor analysis  (Diggle, 2002) , which evaluates the degree of dispersion in a spatial point process (i. e. , how far apart on average hummocks are from each other). If hummocks are on average further apart (using the mean nearest-neighbor distance, µ NN ) compared with what would be expected under CSR (µ exp ), the hummocks are said to be overdispersed and subject to regular spacing; if hummocks are closer together than what CSR predicts, they are said to be underdispersed and subject to clustering.
Acknowledgements. We gratefully acknowledge the field work and data collection assistance provided by  Mitch Slater ,  Alan Toczydlowksi , and  Hannah Friesen. The authors also acknowledge two anonymous reviewers and  Victor Lieffers , whose comments and suggestions improved this paper. Financial support. This project was funded by the  Minnesota Environmental and Natural Resources Trust Fund , the  USDA Forest Service Northern Research Station , and the  Minnesota Forest Resources Council. Additional funding was provided by the  Virginia Tech Forest Resources and Environmental Conservation department , the  Virginia Tech Institute for Critical Technology and Applied Science , and the  Virginia Tech William J. Dann Fellowship. Jacob S. Diamond  is supported by  POI FEDER Loire  no. 2017-EX001784 , the  Water Agency of Loire Catchment AELB , and the  University of Tours.
Recently, statistical methods that aggregate all loci from disease-specific GWAS summary statistic datasets in a linkage disequilibrium (LD)-informed manner have been developed to better model these polygenic architectures  [11]. These aggregated signals can be leveraged to estimate the genetic correlation between different diseases  [11, 54] , even at the level of gene expression in specific tissues  [35, 57]  or across tissues  [38]. Here, we apply these novel methods to GWAS summary statistics derived from the largest currently available studies of melanoma  [45] , PD  [13, 63, 64] , and other neurodegenerative diseases  [25, 44]  to investigate whether there exists specific genetic architecture overlap between melanoma and PD.
GWAS Summary Statistics: We obtained the largest available, European genetic ancestry, case-control, GWAS summary statistic data for melanoma (Law2015  [45] ) and three independent studies of PD (Nalls2014  [64] ; Chang2017  [13] ; Nalls2019  [63] ) as well as two negative control comparator neurodegenerative diseases: Alzheimer disease (Kunkle2019  [44] ) and frontotemporal dementia (Ferrari2014  [25] ). The summary statistics for these datasets included p-value, effect allele, number of individuals or studies, and standard error for every genetic variant reported in each study. All individual studies contributing to the GWAS summary statistic datasets used in the current analysis received approval from the pertinent institutional review boards or ethics committees, and all participants gave informed consent.
Melanoma -Law2015 We obtained meta-analysis Melanoma risk summary statistic data from the Melanoma meta-analysis consortium (https://genomel. org/). This data was published in Law et al. , Nature Genetics, 2015  [45]. This dataset includes melanoma-association results for 9,469,417 genotyped and imputed variants derived from 12,814 pathologically-confirmed melanoma cases and 23,203 controls of European ancestry.
Parkinson disease -Nalls2014 We obtained PD risk summary statistic data from PDGENE (http://www. pdgene. org/). This dataset was published in Nalls et al. , Nature Genetics, 2014  [64]  and Lill et al, PLoS Genetics 2012  [50]. The summary statistic data we obtained did not include any 23andMe participants and thus the dataset includes PD-association results for 7,799,580 genotyped and imputed variants derived from 9,581 PD casesmostly diagnosed, but some self-reportedand 33,245 controls of European ancestry. This dataset only included the number of studies, and not the number of individuals, supporting the association results for each variant. Consequently, we only included variants supported by at least 12 of 13 studies in downstream analyses.
Parkinson disease -Chang2017 We obtained Parkinson disease (PD) risk summary statistic data from 23andMe, Inc. , a personal genetics company (https://research. 23andme. com/dataset-access/). This data was published in  Chang et al. , Nature Genetics, 2017 [13]. This dataset includes PD-association results for 12,896,220 genotyped and imputed variants derived from 6,476 self-reported PD cases and 302,042 controls of European ancestry. This dataset excludes any 23andMe participants included in the Nalls2014 study.
Parkinson disease -Nalls2019 We obtained PD risk summary statistic data from the IPDGC (https://pdgenetics. org/). This dataset was published in Nalls et al. , The Lancet Neurology, 2019  [63]. The summary statistic data we obtained did not include any 23andMe data nor Nalls2014 data and thus includes PD-association results for 17,510,617 genotyped and imputed variants derived from 33,674 PD casesdiagnosed and UKB proxy-cases, that is individuals with a first-degree relative with PDand 449,056 controls of European ancestry.
Alzheimer disease -Kunkle2019 We downloaded stage 1 meta-analysis Alzheimer Disease (AD) risk GWAS summary statistic data from NIAGADS (National Institute on Aging Genetics of Alzheimer Disease Data Storage Site) website: https://www. niagads. org/datasets/ng00075 (#NG00075). This data was generated by the International Genomics of Alzheimer Project and published in Kunkle et al. , Nature Genetics, 2019  [44]. The stage 1 meta-analysis dataset includes AD-association results for 11,480,632 genotyped and imputed variants derived from 21,982 AD cases and 41,944 cognitively normal controls of European ancestry.
Frontotemporal Dementia -Ferrari2014 We obtained discovery phase Frontotemporal Dementia (FTD) risk GWAS summary statistic data from the International Frontotemporal Dementia Genomics Consortium (IFGC, https://ifgcsite. wordpress. com/data-access/). This data was generated by the IFGC and published in  Ferrari et al. , Lancet Neurology, 2014 [25]. The discovery phase dataset includes FTD-association results for 6,026,385 variants derived from 2,154 individuals with FTD and 4,308 control of European ancestry.
05 in the gnomAD NFE population, variants with strandambiguous alleles, variants supported by a low sample size or effective sample (Neff = 4/(1/Ncases+1/Ncontrols)) for the meta-analysis  [90] , and variants that were not reported in the HapMap3 study  [31]. The number of variants overlapping across all processed GWAS summary statistic datasets analyzed in the present study are presented in Table  1.
Estimating Genetic Overlap by GNOVA We calculated genetic overlap using GNOVA software  [54]. GNOVA estimates genetic covariance based on all the genetic variants shared between two GWAS summary statistic datasets. In brief, the summary statistic z-scores observed for each variant are multiplied and their product is regressed against the LD score for that variant, with the LD score being calculated based on the external 1000 genomes project CEU population  [84]. Genetic covariance is then estimated based on all shared variants using the method of moments and a block-wise jackknife approach as described in the GNOVA manuscript  [54]. GNOVA further provides an estimate of genetic correlation based on this calculated genetic covariance and the estimated GWAS variant-based heritabilities. As with LD score regression  [11] , GNOVA is able to statistically correct for any sample overlap between two different sets of GWAS summary statistics.
Disease-Inferred Gene Expression Overlap Analyses We investigated whether the genetic overlap between PD and melanoma was mediated by shared regulation of gene expression. To do this we generated tissue-specific, diseaseinferred gene expression profiles from the processed GWAS summary statistics using FUSION/TWAS software with the default parameters  [35]. FUSION/TWAS imputes gene expression using cis expression quantitative trait loci (eQTL) data derived from reference panels of paired genotype and tissue-specific gene expression data. As gene expression is imputed based on disease-specific GWAS summary statistics, FUSION/TWAS identifies disease-inferred gene expression profiles with tissue-level resolution. For this study, we used eQTL weights based on the 48 tissue Genotype-Tissue Expression (GTEx)  [34]  version 7 (v7) reference panel provided with FUSION/TWAS to generate all disease-inferred gene expression profiles.
We tested for overlap or correlation between the disease-inferred gene expression using RHOGE software  [57] , providing the effective sample size  [90]  for each dataset and only including those FUSION/TWAS results that were at least nominally (p < 0. 05) associated with each disease as per the default RHOGE parameters. RHOGE provides an estimate of the genetic correlation between two traits that can be attributed to eQTLs as represented by the different trait-inferred gene expression profiles. We exclude the major histocompatibility complex (MHC) region from disease-inferred gene expression overlap analyses due to its complex LD structure  [35, 57]. To consider an overlap as significant we used a Bonferroni corrected threshold: P < 1. 04 × 10 -03 (0. 05 / 48 tissues) and present uncorrected p-values and 95% confidence intervals in the text.
Investigating for Differential Expression of Highlighted eGenes in PD Brain Tissues To investigate whether the eGenes we identified as being independently associated with both melanoma and PD demonstrated differential expression in PD, we downloaded publicly available, normalized microarray gene expression data derived from substantia nigra brain tissues donated by individuals with and without PD. These datasets were deposited in the Gene Expression Omnibus (GEO) under the accession codes: GDS2821  [47]  and GDS3129  [22, 62]. The GDS2821 dataset includes Affymetrix Human Genome U133 Plus 2. 0 array data collected from 16 individuals with neuropathologically-confirmed PD and nine aged individuals with no history or pathological diagnosis of neurologic or psychiatric disease  [47]. The GDS3129 dataset includes Affymetrix Human Genome U133B array data derived from 15 samples of medial substantia nigra and nine samples of lateral substantia nigra from individuals with neuropathologically-confirmed PD as well as eight samples of medial substantia nigra and seven samples of lateral substantia nigra from control individuals without neurodegenerative disease pathology  [22, 62].
Polygenic Analysis Reveals Specific Genetic Overlap between Melanoma and PD Prior to cross-disease analyses, we first confirmed that the three independent PD datasets demonstrated positive and significant genetic correlation with each other (genetic correlation range: 0. 94 to 1. 07, Table  2 ) using GNOVA software. Following this confirmation and method validation, we proceeded to analyze for potential genetic correlations between melanoma, PD, and the comparator neurodegenerative disease datasets. We identified a significant and positive genetic correlation between melanoma and the meta-analyzed PD dataset (genetic correlation: 0. 17, 95% CI 0. 10 to 0. 24; P = 4. 09 × 10 -06 , Table  3 ). This result was not driven by any specific PD dataset, but all three independent datasets contributed to the association (P < 0.
05; genetic correlation range: 0. 14 to 0. 25, Figure  1  and Table  4 ). We further investigated the genetic correlation between melanoma and the meta-analyzed PD dataset by stratifying it to the level of minor allele frequency and chromosome annotations. Consistent with the polygenic nature of these diseases, we found their genetic correlation to be most highly enriched in those genetic variants annotated as being in the top quartile of minor allele frequency (Supplementary Table  1 , online resource). We also found the genetic correlation between melanoma and the meta-analyzed PD dataset to be enriched in chromosomes 1, 2, 8, 11, 16, and 17 (Supplementary Table  2 , online resource). We found no shared genetic architecture between melanoma and Alzheimer disease (genetic correlation: -0.
02, 95% CI -0. 11 to 0. 07; P = 0. 73, Table  3 ) nor between melanoma and Frontotemporal dementia (genetic correlation: -0. 13, 95% CI -0. 37 to 0. 12; P = 0. 32, Table  3 ). We similarly did not observe any significant correlation between the meta-analyzed PD dataset and AD (Table  3 ), although one of the individual PD studies showed nominal correlation with AD (Nalls2014: genetic correlation: -0. 22, 95% CI -0. 22 to 0. 00, P = 4. 94 × 10 -02 ; Table  4 ). We did identify a positive and significant genetic correlation between the meta-analyzed PD dataset and FTD (genetic correlation: 0. 27, 95% CI 0. 07 to 0. 47; P = 8. 43 × 10 -03 , Table  3 ), but this appeared to be primarily driven by one of the individual PD studies (Table  4 ). Together these results demonstrate a consistent, positive and significant genetic correlation between melanoma and PD but not between melanoma and FTD or AD.
PD and Melanoma Disease-Inferred Gene Expression Overlaps Across Tissues To investigate whether melanoma and PD-associated risk variants regulated the expression of the same genes, we generated disease-inferred, tissue-specific gene expression profiles from the processed melanoma and METAPD GWAS summary statistic datasets via FUSION/TWAS software  [35]. We further investigated for overlap between the different diseaseinferred gene expression profiles using RHOGE software  [57]. We identified a positive and significant overlap between the PD-and melanoma-inferred gene expression profiles in a joint analysis of the 48 tissues included in the GTEx v7 reference panel provided with the FUSION/TWAS software (disease-inferred gene expression correlation: 0. 14, 95% CI 0. 06 to 0. 22; P: 7. 87 × 10 -04 ).
28 × 10 -04 ). In the melanoma dataset, we identified 168 significantly associated eGenes (Supplementary Table  4 , online resource) including those reported in a previous TWAS study  [92] , such as MAFF (P: 1. 28 × 10 -12 ). Comparing the two sets of cross-tissue summary metric results, we identify seven eGene-disease associations that passed the FDR threshold for both PD and melanoma: GPATCH8, MYO9A, PIEZO1, SOX6, TRAPPC2L, ZNF341, and ZNF778 (Figure  3  and Table  6 ). In addition, we found evidence for differential expression between individuals with and without neuropathologically-confirmed PD for five of these seven eGenes in publicly available substantia nigra microarray datasets (Supplementary Figure  1A-O , online resource). Together, these results suggest that some component of the genetic correlation between melanoma and PD may be mediated by the shared regulation of gene expression across tissues.
Discussion In this study, we have identified a positive and significant genetic correlation between melanoma and PD by leveraging the largest available GWAS summary statistic datasets and recent advances in polygenic complex trait modeling  [11, 54]  (Tables  3 4 ). Our results support the findings of several epidemiologic studies of sharedindividual and familialrisk  [5, 17, 21, 27, 29, 36, 42, 53, 67, 68, 81, 87, 91]  between the two diseases. We also demonstrate no evidence for shared genetic overlap between melanoma and two negative comparison neurodegenerative diseases: AD and FTD (Table  3 ), suggesting specificity. Our results of positive genetic correlations between melanoma and PD stand in contrast to negative results from several other genetic studies including single-variant analyses  [24, 26, 28, 55, 65, 66]  and multi-variant analyses  [65, 66].
Similarly, previous multi-variant genetic analyses investigating melanoma and PD have focused specifically on GWAS-significant loci and thus can be expected to have missed a substantial proportion of the genetic architecture  [8]  underlying these complex diseases. Genetic correlation methods that consider linkage disequilibrium structure and incorporate all common variants are better powered to detect genetic overlap, especially given current GWAS sample sizes, as we demonstrate here for melanoma and PD. The classification and ascertainment of participants was different between the three independent PD datasets included in the present study; however, they all demonstrate positive and significant genetic overlap with each other (Table  2 ). While this overlap does not guarantee specificity of the represented genetic architecture  [12] , the fact we observe all three independent PD studies to demonstrate positive and significant genetic overlap with melanoma (Figure  1  and Table  4 ) bolsters confidence in our results.
Importantly, although the PD and melanoma genetic correlation point estimates for the three individual PD studies appear different, their 95% confidence intervals overlap which indicates that the effect size estimates are not significantly different (Figure  1  and Table  4 ). The genetic overlap between the independent PD datasets supported their meta-analysis, and the genetic correlation between the meta-analyzed PD dataset and melanoma provided the most precise estimate (genetic correlation: 0. 17, 95% CI 0. 10 to 0. 24; P = 4. 09 × 10 -06 ; Figure  1  and Tables  3 4 ). Further increases in precision may result from incorporating additional independent GWAS summary statistic datasets and thus our analyses should be repeated as these become available for both melanoma and PD. Similarly, our FTD genetic correlation results should be interpreted with caution as the current sample size is at least one order of magnitude smaller than the other disease datasets.
For example, among the individual PD datasets, we only observe a positive genetic correlation between FTD and Nalls2019. Parkinsonism has been observed in about 20% on individuals with FTD  [2, 7] , and this result may suggest that individuals with FTD with parkinsonism were included among the UKB-proxy cases in the Nalls2019 dataset. Alternatively, a positive genetic correlation between FTD and the other PD datasets may be observed from the use of a larger FTD GWAS summary statistic dataset. Thus, our analyses should be repeated as larger GWAS summary statistic datasets become available. We infer disease-associated gene expression profiles  [35]  using melanoma and metaanalyzed PD GWAS summary statistics and investigate for their overlap at the level of tissues  [57]  and genes  [38]  to provide bioinformatically-driven biological context to our melanoma and PD genetic correlation results.
In terms of PD-relevant brain tissues, we observe positive, nominal disease-inferred gene expression correlation in the substantia nigra and basal ganglia caudate nucleus (disease-inferred gene expression correlation: 0. 42 and 0. 29, respectively; P < 0. 05, Figure  2  and Table  5 ). Importantly, the available GTEx v7 inferred gene expression reference model for brain tissues are based on substantially fewer samples than most peripheral tissues, for example the brain substantia nigra reference is derived from 80 donors compared to 335 donors for the suprapubic skin reference (Table  5 ). Consequently, our disease-inferred gene expression risk profile overlap analyses should be repeated as larger reference panels become available. Similarly, another limitation of the GTEx dataset is the inclusion of tissues from individuals with extended post-mortem intervals.
The intergenic variant rs12921479 -which is an eQTL for TRAPPC2L in the brain  [34, 74]  was reported to be associated with PD (P: 9. 31 × 10 -07 ) in an autopsy-confirmed cohort of PD  [3] , but is only nominally associated with PD in our metaanalyzed PD dataset (P: 1. 01 × 10 -02 ). A role for TRAPPC2L in melanoma remains to be explored. SOX6 is a transcription factor which was recently identified as a determinant of substantia nigra neuron development and maintenance  [70]. Its expression was observed to localize to pigmented and tyrosine hydroxylase positive neurons but not to pigment-negative neurons within the substantia nigra  [70]. In addition, SOX6 expression was diminished in the substantia nigra of individuals with PD and deletion of SOX6 in mice was observed to decrease dopamine levels and innervation in the striatum  [70] , a brain region that is also impacted in PD  [9].