dataesr/scientific-paragraphs-categorization huggingface dataset:
Imputation is a commonly used technique that exploits linkage disequilibrium to infer missing genotypes in genetic datasets, using a well-characterized reference population. While there is agreement that the reference population has to match the ethnicity of the query dataset, it is common practice to use the same reference to impute genotypes for a wide variety of phenotypes. We hypothesized that using a reference composed of samples with a different phenotype than the query dataset would introduce imputation bias. To test this hypothesis we used GWAS datasets from Amyotrophic Lateral Sclerosis (ALS), Parkinson Disease (PD), and Crohn's Disease (CD). First, we masked and then performed imputation of 100 disease-associated markers and 100 non-associated markers from each study. Two references for imputation were used in parallel: one consisting of healthy controls and another consisting of patients with the same disease.
INTRODUCTION In genome-wide association studies (GWAS), SNP data are used to find genetic loci associated with various traits, particularly common diseases. Due to the large number of tests performed (more than 2. 5 million for the latest chips), correction for multiple hypothesis testing is necessary to avoid type I errors. However, after correction, there is typically not enough power to detect small effects (OR < 1. 5), even with sample sizes exceeding 1000 cases and 1000 controls. A usual approach used in order to increase n, consists in merging datasets from two or more sources in a meta-analysis. However, if datasets were generated using different genotyping platforms (a likely scenario), a small minority of the total number of markers will be represented in both. In this case, non-overlapping genotypes are imputed using one or more reference populations  (Guan and Stephens, 2008; Huang et al.
RESULTS To test the hypothesis that imputation introduces a systematic bias that ultimately results in a deflation of the effect size, we used three publically available datasets in Amyotrophic Lateral Sclerosis (ALS), Crohn's Disease (CD), and Parkinson's Disease (PD). For each dataset, the following steps were performed. We randomly split the dataset into two groups of equal age, gender, and case/control distribution. The two halves were termed split A and split B. We conducted a GWAS on split A. We ranked all SNPs by GWAS effect size and selected the top 100 disease associated markers (DAM) and the bottom 100 non-associated markers (NAM). We then proceeded to perform three imputations on the SNPs of interest. Each time, the SNP of interest was masked and an independent reference was used to impute the SNP.
First, the cases from split A were imputed using controls from split B as reference for imputation, in order to measure the bias and error of imputation. Next, the cases from split A were imputed using cases from split B as reference for imputation, in order to see if the use of cases for imputation can improve accuracy and decrease bias. Finally, both cases and controls from the entire dataset were imputed using a commonly used publically available reference panel, in order to see if the measured effect size of a case control study is affected by using imputed genotypes vs. true genotypes.
When imputing fractional genotypes, discordance (D frac ) at each SNP was defined as the average of the absolute difference between the imputed fractional genotype and the "true" genotype (coded as 0, 1, or 2; where 2 = homozygous for the major allele) across all samples, where the "true" genotype is given by SNP-chip. In general, the average D int = 15-20% across all diseases and D frac = 0. 19 and 0. 24 (Table  1 ). Interestingly, the lowest overall discordance rate was found in CD, the dataset with the largest sample size. We observed that the imputation discordance rate (either D int or D frac ) was significantly higher for DAM than for NAM. For example, in the ALS dataset, D int = 19. 65% ± 1. 16 (mean ± 2 × standard error) for DAM and D int = 17. 4% ± 1. 11 for NAM, a statistically significant difference (p = 0.
005) (Table  1 ). Similarly, imputation of PD samples was significantly less accurate (p = 0. 0002) for the 100 DAM (D int = 19. 12% ± 1. 3) than for 100 NAM (D int = 15. 74% ± 1. 23). We found no difference in D int between DAM and NAM in the CD dataset (p = 0. 95). A similar pattern was found when using fractional genotypes (D frac ) (Table  1 ). When independent cases were used as a reference for imputation (instead of controls), a significant reduction in D int or D frac was observed. For example, in the ALS dataset, the average discordance at DAM (D int = 18. 04% ± 1. 15) using cases as reference was significantly lower (matched pairs t-test, p = 10 -4 ) than the average discordance at the same SNPs using controls as reference (D int = 19. 65% ± 1. 16), suggesting that more accurate imputation is obtained when matched cases are used as reference for imputation.
The average D int in the ALS dataset using independent ALS cases as a reference was 18. 04% (± 1. 15) for DAM and 17. 6% (± 1. 2) for NAM, a non-significant difference (p = 0. 6) (Table  1 ). Similar results were obtained in the PD dataset, in which the average discordance at DAM using controls as reference (D int = 19. 12% ± 1. 3) was significantly higher than that observed when matched cases were used instead (D int = 18. 22% ± 1. 4) (matched pairs t-test, p = 0. 002). Although using independent cases as a reference reduces the discordance in imputation, a significant drop in imputation accuracy was still observed in the PD dataset for DAM (D int = 18. 22% ± 1. 4) compared with NAM (D int = 15. 89% ± 1. 21) (p = 0. 01) (Table  1 ). We observed similar results using fractional imputation metrics.
For integer genotypes, when using controls as a reference to impute NAM (markers not associated with disease), there is a small significant bias toward the major allele of 1-2% in all three datasets (ALS p = 0. 0067; PD p < 10 -6 ; and CD p < 10 -7 ), Table  2. This effect was reduced by using fractional imputation (Table  2 ). This inherent bias is the reason that we defined bias in terms of major or minor allele, and the reason that we split the DAM (disease markers) into two groups for further analysis: markers where the major allele is the susceptibility ("risk") allele, and markers where the minor allele is the risk allele. When controls were used as a reference for imputation DAM, a consistent bias against the risk allele was observed for all three diseases. For example, when the major allele was the risk allele, integer imputation was biased toward the minor allele (p < 10 -7 for each disease) and when the minor allele was the risk allele, imputation was biased toward the major allele (p < 10 -15 for each disease).
Similar results were observed when fractional imputation was used. Interestingly, when independent cases were used as a reference to impute DAM, the bias against risk alleles remained. Similarly to what we observed using controls as a reference, when the major allele was the risk allele, imputation was significantly biased toward the minor allele (p < 10 -8 in ALS; p = 3 × 10 -4 in PD; and p = 0. 012 in CD). Conversely, when the minor allele was the risk allele, imputation was significantly biased toward the major allele (p < 10 -6 for each disease). Figure  1  shows the bias (Y-axis) when controls (left) or cases (right) are used as a reference to impute SNPs in the ALS dataset. With either reference population the bias is consistently against the risk allele and can be observed for all DAM (circles) including the most significantly associated SNPs (dark gray) as well as for more modestly associated (light gray).
However, the magnitude of the bias is lower when cases are used as reference. We observed similar results in the PD and CD data sets (Supplementary Figures  S1, S2 ). Of note, results were largely unchanged when the call tolerance parameter T was changed      -S5 ). To evaluate the potential effect of imputation on a genomewide association study (GWAS), the same three datasets (PD, ALS, and CD) were used to perform three parallel association studies. For this analysis, imputation was performed using the CEU subset of the 1000 Genomes as a reference for imputation  (Abecasis et al. , 2010). For any SNP, the odds ratio (OR) was defined by logistic regression (OR = exp(b), where b is the estimate of the logit coefficient in the logistic regression). Odds ratios computed from imputed genotypes (imputed OR) were compared to those obtained using data from experimentally determined genotypes (from SNP chip; termed "true" OR).
Imputed OR were compared to true OR for each DAM. Figure  2  shows the distribution of imputed/true OR for the top 100 DAM in each dataset. Using integer imputation, the magnitude of the mean imputed OR was only 64. 2% ± 2. 8 (mean ± 2 × standard error) as high as the magnitude of the true OR in ALS, 71. 1% ± 3. 2 in PD, and 73. 8% ± 2. 4 in CD. In all cases, it is evident that the distribution of OR is shifted toward the left of 1. 0, a vertical dotted line which represents equality between imputed OR and true OR. Using fractional genotypes yielded similar results (Figure  2 ). Using another commonly used imputation software algorithm (Beagle) yielded similar results (Figure  2 ). However, in contrast to Mach imputation, the magnitude of the mean imputed OR was closer to the magnitude of the true OR in each dataset.
In summary, the observed effect size after imputation is considerably lower for both types of imputation although in the second analysis using Beagle and a larger reference dataset improves the accuracy of imputation. It should be noted that for a few SNPs, the imputed OR is nearly equal or greater than the true OR (imputed OR/true OR ≥ 1). That is, for these SNPs, there is no decrease in observed magnitude of association with disease when imputed genotypes are used.
DISCUSSION We have shown that imputation of DAM is consistently and significantly biased against the risk allele. This was seen in three diseases, and using two different software algorithms of imputation. The number of top SNPs selected as DAM or NAM was arbitrary and we acknowledge that many of these SNPs are likely to be false positives. However, since this arbitrary threshold was used for all datasets, we deemed this as a valid strategy. Furthermore, the fact that using independent cases as a reference (rather than controls) reduces the imputation bias indicates that at least some of these SNPs may be truly associated with disease. In each of three datasets, 100 DAM were selected and the odds ratio of association with disease was estimated using both genotyped (true) data and imputed data.
The ratio of imputed odds ratio to true odds ratio (x-axis) takes a similar distribution across the 100 SNPs in each disease. The odds ratio (OR) of association was generally lower for imputed data than for true data (imputed OR/true OR < 1). This hold true whether we use whole number imputation by Mach (top), fractional imputation by Mach (middle), or whole number imputation by Beagle (bottom). In contrast to Mach imputation, the magnitude of the mean imputed OR was closer to the magnitude of the true OR for all three datasets when using Beagle imputation. ALS, Amyotrophic Lateral Sclerosis; PD, Parkinson's Disease; CD, Crohn's Disease. While imputation bias is reduced when matching cases were used as reference for imputation, the effect is still present. We offer three possible explanations for the persistence of bias in this scenario.
The reduced bias for Beagle imputation can be explained by at least two reasons. First, Beagle may be using a more accurate algorithm for imputation though this is unlikely given previously published head-to-head analysis of Beagle vs. Mach, and, second, the reference panel may be more up to date and consists of more individuals. In conclusion, while combining datasets by imputation can lead to a more powerful GWAS  (Becker et al. , 2009; Hao et al. , 2009)  by allowing successful identification of SNPs associated with various phenotypes  (Sanna et al. , 2008; Willer et al. , 2008; Zeggini et al. , 2008; De Jager et al. , 2009) , the described decrease in signal inherent to imputation can partially offset any gain in power resulting from the combination of studies. Important implications of this finding include the fact that some truly associated variants may not be detected, and that some genomewide significant findings may have larger true effect sizes than estimated.
DATASETS: THREE CASE-CONTROL GWAS Quality controlled, genotype-level data from three previously published independent case-control GWAS in individuals of European ancestry in Amyotrophic Lateral Sclerosis (ALS), Crohn's Disease (CD), and Parkinson's Disease (PD) were obtained from dbGAP  (Mailman et al. , 2007)  (Supplementary Table  S1 ). In CD  (Rioux et al. , 2007)  and PD  (Fung et al. , 2006) , cases and controls were matched by sex, age (or year of birth), and ancestry  (Rioux et al. , 2007; Simon-Sanchez et al. , 2009). For ALS cases  (Schymick et al. , 2007) , a sample from neurologically normal controls  (Simon-Sanchez et al. , 2007)  were matched for age and gender and ancestry  (Schymick et al. , 2007). After quality control done by the original authors, we performed a second layer of quality control on markers (MAF > 0.
05, HWE p-value > 10 -6 in controls, genotype success rate > 95%). For each disease dataset, half of the patients and a matched number of controls were extracted (split "A") while maintaining original ratios of gender and age. This half of the data (split A) was used to perform a association study (using software Plink  Purcell et al. , 2007) , using a genotypic model or a linear dose model. Here we report results using the genotypic model; results for the linear trend model were largely similar and not shown. The 100 markers with the largest absolute effect size (DAM) were considered for imputation (OR > 1. 88 in ALS, OR > 1. 53 in PD, OR > 1. 44 in CD). As a control, 100 SNP markers with the smallest absolute effect size (NAM) were imputed with the same procedure (1 ≤ OR < 1. 001). For each marker of interest (DAM or NAM), genotypes of cases of split A were masked with the goal of predicting them using imputation.
, 2006)  (see  Nothnagel et al. , 2009  for an evaluation of the relative performance of this algorithm compared to others), with default settings and default quality control criteria for SNPs (MAF > 0. 05, HWE p-value > 10 -6 , genotype success rate > 95%). For each imputed genotype, Mach outputs a fractional genotype (G imp ) between zero and two that corresponds to the inferred number of copies of the minor allele, where minor allele is determined using dataset frequencies (cases and controls). The fractional genotype is compared to the genotype by SNPchip (G 0 ), or "true" genotype.
ACKNOWLEDGMENTS Sergio E. Baranzini  is a  Harry Weaver Scholar  and is funded by a grant from the  National Multiple Sclerosis Society  ( RG4051A2/1 ). Funding support for  Genome-Wide Genotypic Association of Amyotrophic Lateral Sclerosis  was provided by  NIH  Intramural funding from  NIA  and  NINDS , with additional funding from the  ALS Association  and the  Packard Center for ALS Research at Johns Hopkins  and the genotyping of samples was provided by the  National Institute of Neurological Disorders and Stroke (NINDS). Funding support and the genotyping of samples for the  NINDS Ischemic Stroke Genetics Study  was provided by the  National Institute of Neurological Disorders and Stroke (NINDS)  (Singleton, PI). Funding support for NINDS Parkinsons Disease was provided by the  National Institute for Neurological Disease and Stroke  and the genotyping of samples was provided by the  Singleton Lab (NIA Laboratory of Neurogenetics ) with support from  NINDS.
The  NIDDK IBDGC Crohn's Disease Genome-Wide Association Study  was conducted by the  NIDDK IBDGC Crohn 's Disease study investigators and supported by the  National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This manuscript was not prepared in collaboration with investigators of the  NIDDK IBDGC Crohn's Disease study and does not  necessarily reflect the opinions or views of the IBDGC or the  NIDDK. The dataset(s) used for the analyses described in this manuscript were obtained from the dbGAP,  NINDS , and  NIDDK  databases found at http:// www. ncbi. nlm. nih. gov/through accession numbers phs000004, phs000101, phs000102, phs000089, and  phs000130. Sarah B. Hill  provided editorial assistance.
The contrasting impact of drought on GPP and TER between grazed and ungrazed desert steppe was also evidenced by data collected with tower-based eddy covariance approach on the Mongolian Plateau (Shao et al 2013). This two-year dataset indicates that grazing can play a positive role in biophysical regulation of carbon fluxes in a desert steppe. The positive effects of grazing on the uptake of CO 2 in semiarid steppe were also reported by  Kang et al (2013). Although both these examples were short, the analyses will be useful for grazing management. Nevertheless, additional long-term flux measurements are needed to understand the mechanisms underlying the interactions between extreme climate, ecological disturbance, and carbon cycling.
Long-term drought events Babst et al (2012) did clustering analysis of tree-ring growth data at ∼1000 sites across Europe that showed regionally consistent growth patterns driven by climate regimes and species-specific growth characteristics. Fifteen regional, synthetic chronologies that represent radial tree growth anomalies across Europe were reconstructed over the past ∼500 years. The analysis revealed 18 extreme tree-growth events during the pre-instrumental and 2 events  (1947/48 and 1976)  in the instrumental period, which may be been caused by extreme late-summer drought conditions. The complexity of interactions between drought and vegetation response is demonstrated in a 26-year study that analyzed vegetation growth based on Normalized Difference Vegetation Index (NDVI) in China (Xu et al 2012).
e. 1%,…,10%) on the absolute values of the anomalies. They then defined an extreme event by spatiotemporally contiguous points whose values are larger than q (positive extremes) and smaller than -q (negative extremes), respectively. Zscheischler et al (2014) reanalyzed four global MODIS-based GPP datasets over the last 30 years and found that positive (extreme excess in carbon uptake) and negative (extreme reduction in photosynthetic carbon uptake) GPP extremes occurring on 7% of the spatiotemporal domain explained 78% of the global interannual variation in GPP. Most of the negative GPP extremes (larger than positive extremes) were attributed to droughts. The tail approach depends on the shape of the probability distribution of the anomalies, and on their severity. The identification of extreme events requires that the shapes of probability distributions are similar so that comparison and analysis cross all sites can be made.
However, it is impossible to have similar distribution of probability of the anomalies on a global scale due to heterogeneous properties of vegetation from region to region. Liu et al (2013)  developed a new method of using the Box-Cox transformation  (Box and Cox 1964)  to convert non-normally distributed dataset into normally distributed dataset. They applied the new method to biweekly time series of global normalized difference vegetation index (NDVI) from 1982 to 2006 to investigate the extreme events on a global scale. They found that extreme NDVI events were aggregated in Amazonia and in the semi-arid and semi-humid regions in low and middle latitudes, while they seldom occurred in high latitudes. This NDVI data analysis indicates that precipitation was a major control on the NDVI extremes in low and middle latitudes, providing a consistent picture as by FLUXNET data synthetic analysis  (Yi et al 2010 (Yi et al , 2014)).
Perfect-deficit approach A perfect-deficit approach developed by Yi et al (  2012 ) is different from the widely used, traditional method of the tails approach (extreme fluctuations relative to mean values). The nature of the perfect-deficit approach is data-based. During observational period, a perfect growth (carbon uptake) curve can be constructed by the best growth of ecosystems across the years at the site. Thus, the growth curves of each year can be compared with the perfect growth curve. The difference between the perfect-growth curve and yearly growth curve is defined as deficit. Yi et al (2012)  derived this approach based on GPP data measured from five grassland tower sites. They found strong links between GPP deficits and drought index. This approach can be applied to any continuous dataset of ecosystem-climate interactions.
Therefore, the same extreme events can be cross-verified by different independent datasets. Wei et al (2014) applied the perfect deficit approach to assess the effects of climate extremes on forest carbon assimilation capacity based on FLUX-NET data and MODIS GPP datasets. They found a strong link between periods of drought and reduced capacity of trees to absorb carbon. In particular, broadleaf (evergreen and deciduous) trees were very sensitive, whereas coniferous trees were less affected. This is because most broadleaf trees are adapted to generally well watered and good nutrient conditions, whereas needles are a specific adaptation that evolved to maximize carbon uptake in water-and temperature-limited regions. Huang et al (2013) applied the perfect-deficit approach to explore how potential productivity of coniferous plantation forest was significantly reduced by extreme drought and ice storm.
Laser tracks were drawn with the PalmRobo 4. 5 software driving the machine around the cells of interest (see pre-cut and schematic Figure  3 ). Laser settings were adjusted to ensure the lowest possible radiation energy whilst maintaining effective dissection and light lifting to the collection vessel. Laser power ranged from 40 to 48 depending on the thickness of the cells: typically, laser energy was reduced to capture A-type cells because the cell wall was easy to cut, raised to capture B-type cells that have a much thicker cell wall, and adjusted in between for UA-type cells. Other settings remained constant (laser focus 35; LPC delta +27; LPC focus 35; cutting speed 14; 1 cycle of cutting). The laser spot was thin enough to cut between the cells without damaging the cellular content, and even the isolation of B-type cells was achieved with only minimum damage to the surrounding cells (see post-capture Figure  3B ).
METRICS OF A-TYPE AND B-TYPE CELL TRANSCRIPTOMES Amplified A-type and B-type cDNAs were paired-end sequenced on an Illumina HiSeq 2000 sequencer at the Beijing Genome Institute using a library with an insert size of 170 bp. This insert size ensured that the majority of molecules present in amplified cDNA (>70%) were represented. Reads were cleaned for low quality and adapter sequences, and approximately 13 million clean reads were obtained for each sample. Every read had an overall Q20 > 97% (base call quality above 20). We first examined the ribosomal RNA (rRNA) contamination in reads using SortMeRNA v1. 7  (Kopylova et al. , 2012)  with default parameters and a database containing all rRNA species provided with the software. Somewhat surprisingly, reads contained rRNA contamination of up to 94.
(2013) , although both studies reported inverted mapping values between intron and intergenic regions. The mapping rate to transcripts was also in the range of that found in  Sun et al. (2013). Reads from A-type and B-type cells mapped to 10613 (64%) and 10583 (63. 8%) transcripts respectively over the 16580 transcripts defined in the Ectocarpus transcriptome. The number of transcripts covered by the reads from each replicate and cell type is significantly variable, and the average coverage along these transcripts is relatively low (from 27 to 53%) (Table  3 ). The variation is likely a consequence of lowly expressed genes being captured by just one or a few reads, and in one replicate only. The two cell types had 9486 transcripts in common and in combination spanned a total of 11710 transcripts (70.