Previous studies in the Norwegian pig breeds Landrace and Duroc have revealed a QTL for levels of skatole located in the region 74.7–80.5 Mb on SSC7. Skatole is one of the main components causing boar taint, which gives an undesirable smell and taste to the pig meat when heated. Surgical castration of boars is a common practice to reduce the risk of boar taint, however, a selection for boars genetically predisposed for low levels of taint would help eliminating the need for castration and be advantageous for both economic and welfare reasons. In order to identify the causal mutation(s) for the QTL and/or identify genetic markers for selection purposes we performed a fine mapping of the SSC7 skatole QTL region.NOVA1 and TGM1 (p < 0.001). The same QTL was obtained in Duroc and, although less significant, with associated SNPs spanning a 1.2 Mb region from 78.9–80.1 Mb (p < 0.01). The highest test scores in Duroc were found in genes of the granzyme family (GZMB and GZMH-like) and STXBP6. Haplotypes associated with levels of skatole were identified in Landrace but not in Duroc, and a haplotype block was found to explain 2.3% of the phenotypic variation for skatole. The SNPs in this region were not associated with levels of sex steroids.A dense set of markers on SSC7 was obtained by whole genome re-sequencing of 24 Norwegian Landrace and 23 Duroc boars. Subsets of 126 and 157 SNPs were used for association analyses in Landrace and Duroc, respectively. Significant single markers associated with skatole spanned a large 4.4 Mb region from 75.9–80.3 Mb in Landrace, with the highest test scores found in a region between the genes Fine mapping of a QTL for skatole on SSC7 confirmed associations of this region with skatole levels in pigs. The QTL region was narrowed down to 4.4 Mb in Landrace and haplotypes explaining 2.3% of the phenotypic variance for skatole levels were identified. Results confirmed that sex steroids are not affected by this QTL region, making these markers attractive for selection against boar taint.The online version of this article (10.1186/s12863-017-0549-8) contains supplementary material, which is available to authorized users. Boar taint is an unpleasant smell and/or taste of meat from some uncastrated male pigs. In most countries, the problem is solved by castrating piglets at a young age. Banning surgical castration would, however, be advantageous due to ethical and economic reasons, and the EU aims for alternative solutions to the boar taint issue within few years .The two main compounds responsible for boar taint are androstenone and skatole (3-methylindole). Androstenone is a steroid hormone produced in the testicles, via the same biological pathway as testosterone and estrogens, whereas skatole is a fermentation product produced by degradation of tryptophan in the intestine , 3. BothCYP1A1 and CYP1A2 have been suggested as candidate genes for skatole and are located on SSC7 [Several QTL studies have been conducted to reveal genomic regions underlying boar taint , 23–32.  on SSC7 , 33. No  on SSC7  seems toSus scrofa build10.2 positions) [The aim of this study was to fine map the most significant region affecting the level of skatole, found on SSC7 at 74.7–80.5 Mb (sitions) . This QTSscrofa 10.2) with an overall mapping percentage of 77%. SNP detection was performed in a 5.8 Mb QTL region for skatole on SSC7 (74.7–80.5 Mb). After filtering, 3836 SNPs were found in common for Landrace and Duroc for the QTL region, and 166 of these were selected for genotyping. Additionally, 22 and 23 SNPs from the Illumina 60 K BeadChip were available in the QTL region for the Landrace and Duroc boars, respectively. SNP filtering on minor allele frequency (MAF) and call rate made 126 and 157 SNPs available for association analyses in Landrace and Duroc, respectively.Whole genome re-sequencing was performed on 24 Landrace and 23 Duroc pigs, and provided a total of 10.1 billion paired-end reads  with a per-animal genome coverage ranging from 9-17X. Initial quality control removed approximately 15% of the reads and the remaining reads were mapped against the pig reference genome . These SNPs are located in an intergenic region between a 5S rRNA gene (ENSSSCG00000018509) and syntaxin binding protein 6 (amisyn) (STXBP6) with extensive LD  in the region 75.9–80.3 Mb.The highest log likelihood-ratio test scores (LRT) for skatole in Landrace were found for SNPs rs431825253 at 80 Mb . When considering a p-value of 0.01 (LRT > 6.6), 18 SNPs in the 78.9–80.1 Mb region were found significant in the current study. The highest test scores in Duroc were found in GZMB ), GZMH-like and STXBP6  and one in the 3’UTR region of Transglutaminase 1 (TGM1). The other 19 significant SNPs were located in a 2.5 Mb segment in the intergenic regions NOVA1 - 5S rRNA (ENSSSCG00000018509), ENSSSC00000024192 -ENSSSC00000022792 and 5S rRNA (ENSSSCG00000018509) - STXBP6.Only two of the 21 significant SNPs are located inside genes, one in the first intron of rs322137732, rs344485447, rs326608782, rs431825241, rs321605443, rs344465955, rs330435414, rs334377914 with LRT scores of 19.8–34.6) showed 65–82% sequence similarity to human NOVA1 - STXBP6 intergenic regions when sequences of 200–400 bp surrounding the SNPs were blasted. However, no sequence hits were found for other mammals besides human, making it difficult to predict the degree of evolutionary conservation in this region.NOVA1 is a protein that controls alternative splicing of mRNAs in different cell types –36, wherrs321711075 (LRT = 20.9) showed a 68% sequence similarity to the human long non-coding (lncRNA) RNA LINC00645, and the regulatory properties of lncRNA may give new meaning to GWAS associations in non-coding genomic regions [Sequence surrounding  regions . It has  regions , and the regions , 43). Be regions  and the None of the SNPs investigated were associated with levels of indole, androstenone, testosterone, estradiol and estrone sulphate, confirming the results of Grindflek et al. 2011 . This maThe results in Duroc were less significant than those found in Landrace, which is in agreement with our earlier QTL study . The QTLGZMB and GZMH-like, while four are in the gene STXBP6  was associated with lower levels of skatole whereas the second most frequent haplotype (frequency ~0.22) was associated with higher levels. The difference in mean skatole levels between animals homozygous for these two haplotypes was 0.06 μg/g skatole. Considering consumer acceptance levels of 0.2 μg/g, 14.5% of Norwegian Landrace boars have been shown to have skatole levels that are too high . The SNPNOVA1 and TGM1 in Landrace. In Duroc, SNPs within GZMB, GZMH-like, STXBP6 and intergenic regions at 78.8–80.1 Mb were found to be most significant. The region in Duroc was also associated with levels of indole and androstenone. Although no causal variant was detected, genetic markers for boar taint that are not associated with sex steroids have been identified and would therefore be highly relevant for selection purposes.Fine mapping of a QTL for skatole on SSC7 was conducted to narrow down the QTL region and search for genes and mechanisms underlying the QTL. The most significant results were found in the intergenic regions between 75.9–78.5 Mb and in For re-sequencing purposes, 23 Norwegian Duroc and 24 Norwegian Landrace boars used in the Norsvin breeding program from 2010 to 2013 were selected. The boars were key individuals in our previous QTL studies, with either high or low levels of boar taint , 31, or 2, and the carcasses were ex-sanguinated, scalded and split within 30 min post mortem.A total of 911 Duroc and 767 Landrace boars from Norsvin’s boar testing station were genotyped in this study. The boars include fathers and sons from 70 Duroc and 92 Landrace half sib families, and 60 K BeadChip genotypes (Illumina) were available for all the boars. For Landrace, another 440 sons were available with 60 K genotypes from the previous study , making Levels of skatole were measured in subcutaneous fat, at the Hormone laboratory, NMBU, using high performance liquid chromatography  whereas DNA for genotyping purposes was extracted from blood using the MagAttract DNA Blood Midi M48 protocol on the Bio-Robot M48 . DNA concentration and quality were evaluated using a NanoDrop ND-1000 spectrophotometer .Sus scrofa 10.2 reference genome [AddOrReplaceReadGroups program (http://broadinstitute.github.io/picard/) was used to assign unique IDs to the files before SNP calling.Genomic DNA from 23 Duroc and 24 Landrace boars sequenced on an Illumina GAII . The sequencing was performed by a commercial sequencing center  according to manufacturer’s protocols. FastQC version 0.10.1  was used for quality checking, revealing an overall per-base quality ≥30. Pre-processing of reads was done using a custom Perl script written to remove duplicated reads, trim sequencing primer sequence and remove reads shorter than 0.8 of their original length. On average, 15% of reads were filtered using this pipeline and the remaining reads were aligned to the e genome  using the genome . Mapped e genome . The PicSNPs were detected within each breed using Freebayes  generatiThe boars were genotyped using matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy (MALDI-TOF MS) assays. Assays were designed using MassARRAY Assay Design software  at multiplexing levels between 8 and 27 [See Additional file 2 + ey = sire + herd-year-season + wait-station + pen + animal + sample-date + SNP/haplotype + age_25kg + days-test + days-wait + liveborn + (liveborn)Association analysis was done for skatole, indole, androstenone, testosterone, 17β-estradiol and estrone sulphate levels for all SNPs using the mixed model:Here, y is the phenotype expressed as ln(μg/g levels of the phenotype) and the fixed effects are sire, herd-year-season, waiting in boar test station before slaughter or not, and pen. Covariates used were age at 25 kg (start of boar test), age from 25 kg to 100 kg (days in boar test), days from 100 kg to slaughter (days in waiting station) and number of live born in same litter. Animal ID, sample date for adipose tissue and SNP/haplotype were fitted as random effects. To test if the QTL region contained more than one QTL, the dataset was re-analyzed fitting the most significant SNP as a fixed effect in the model above.Association mapping was conducted using the ASReml software v.2.00 . Log likAdditional file 1:Sscrofa10.2, functional classes and their effect, amino acid change, SIFT prediction and LRT scores for skatole, indole, androstenone in fat and plasma, testosterone, estradiol and estrone sulphate. (XLSX 18 kb)Summary of LogLikelihood test scores  for all SNPs and compounds examined in Landrace. SNPs are presented with their IDs (rs# or 60 K ID), position on Additional file 2:Sscrofa10.2, functional classes and their effect, amino acid change, SIFT prediction and LRT scores for skatole, indole, androstenone in fat and plasma, testosterone, estradiol and estrone sulphate. (XLSX 22 kb)Summary of LogLikelihood test scores  for all SNPs and compounds examined in Duroc. SNPs are presented with their IDs (rs# or 60 K ID), position on Additional file 3:All genes in the 5.8 Mb SSC7 QTL region  according to Ensembl. (XLSX 23 kb)Additional file 4:Primers used for genotyping. (XLSX 26 kb)


DPP4 gene affects incretin levels, insulin secretion, and glucose tolerance in participants of the TÜbingen Family study for type-2 diabetes (TÜF).Dipeptidyl-peptidase 4 (DPP-4) cleaves and inactivates the insulinotropic hormones glucagon-like peptide 1 (GLP-1) and gastric inhibitory polypeptide, collectively termed incretins. DPP-4 inhibitors entered clinical practice as approved therapeutics for type-2 diabetes in 2006. However, inter-individual variance in the responsiveness to DPP-4 inhibitors was reported. Thus, we asked whether genetic variation in the DPP4 tagging single nucleotide polymorphisms (SNPs) were genotyped in 1,976 non-diabetic TÜF participants characterized by oral glucose tolerance tests and bioimpedance measurements. In a subgroup of 168 subjects, plasma incretin levels were determined.Fourteen common  DPP4 gene that, after correction for multiple comparisons and appropriate adjustment, revealed a significant genotype-body fat interaction effect on glucose-stimulated plasma GLP-1 levels (p = 0.0021). Notably, no genotype-BMI interaction effects were detected (p = 0.8). After stratification for body fat content, the SNP negatively affected glucose-stimulated GLP-1 levels (p = 0.0229), insulin secretion (p = 0.0061), and glucose tolerance (p = 0.0208) in subjects with high body fat content only.We identified a variant, i.e., SNP rs6741949, in intron 2 of the DPP4 gene interacts with body adiposity and negatively affects glucose-stimulated GLP-1 levels, insulin secretion, and glucose tolerance. Whether this SNP underlies the reported inter-individual variance in responsiveness to DPP-4 inhibitors, at least in subjects with high body fat content, remains to be shown.A common variant, i.e., SNP rs6741949, in the  Dipeptidyl-peptidase 4  is a ubiquitously expressed single-pass type II transmembrane protein that aggregates in cholesterol-rich lipid rafts and interacts with several other proteins, e.g., caveolin 1, adenosine deaminase, fibroblast activation protein, insulin-like growth factor 2 receptor, receptor-type protein tyrosine phosphatase C, and extracellular matrix proteins . CleavagDPP-4 exerts pleiotropic functions, e.g., in metabolism, immune reactions, and cancer growth . With reDPP4 gene exists that affects incretin levels, insulin secretion, and glucose tolerance in non-diabetic individuals recruited from the TÜbingen Family study for type 2 diabetes (TÜF). The identification of single nucleotide polymorphisms (SNPs) which determine differences in the aforementioned parameters could, after further corroboration in pharmacogenetic settings, support clinical decisions in terms of individualized therapy: for instance, subjects who do not adequately respond to DPP-4 inhibitors due to genetically increased DPP-4 levels/activities would possibly better benefit from DPP-4-resistant incretin mimetics.In this study, we therefore asked whether common genetic variation [minor allele frequency (MAF) ≥0.05] in the All participants gave informed written consent to the study which adhered to the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of the Eberhard Karls University Tübingen.The overall study population consisted of 1,976 White Europeans from the Southwest of Germany and was recruited from the ongoing TÜF study. TÜF currently comprises more than 3,000 non-related individuals at increased risk for type-2 diabetes, i.e., non-diabetic subjects with a family history of type 2 diabetes, a BMI ≥27 kg/m2, impaired fasting glycaemia, and/or previous gestational diabetes . All TÜFA standardized 75-g OGTT was performed following a 10-h overnight fast. For the determination of plasma glucose, insulin, and C-peptide levels, venous blood samples were drawn at baseline and at time-points 30, 60, 90, and 120 min of the OGTT . Increti2). The percentage of body fat was measured by bioelectrical impedance .BMI as a crude proxy for body fat content was calculated as weight divided by squared height (in kg/mPlasma glucose levels (in mmol/L) were measured with a bedside glucose analyzer . Plasma insulin and C-peptide levels  were determined by commercial chemiluminescence assays for ADVIA Centaur . Total GLP-1 and GIP levels  were quantified using radioimmunoassays specific for the C-terminal parts of the peptides ,20. To a0)*c(Ins0)*c(Glcmean)*c(Insmean)]½ . OGTT-derived insulin secretion was estimated as area under the curve (AUC) Cpep0-120/AUC Glc0-120 according to the trapezoid method: ½[½c(Cpep0)+c(Cpep30)+c(Cpep60)+c(Cpep90)+½c(Cpep120)]/½[½c(Glc0)+c(Glc30)+c(Glc60)+c(Glc90)+½c(Glc120)] (with Cpep = C-peptide). This insulin secretion index was recently shown to be superior to several fasting state- and OGTT-derived indices for the detection of genetically determined defects in the incretin axis [The OGTT-derived insulin sensitivity index (ISI OGTT) was estimated as proposed earlier : 10,000/tin axis .http://hapmap.ncbi.nlm.nih.gov/index.html.en), we analyzed in silico a genomic area on human chromosome 2q24.2 spanning the DPP4 gene , 5 kb of the gene’s 5’-flanking region, and 5 kb of its 3’-flanking region  and ~7 kb downstream by the SLC4A10 gene (encoding a sodium-dependent chloride/bicarbonate exchanger with no known function in glucose metabolism). Notably, no obvious high-linkage disequilibrium (LD) block overlapping the screened DPP4 locus and one of its adjacent genes was detected. Within the analyzed DPP4 locus, 44 informative HapMap SNPs with Hardy-Weinberg p-values and MAFs ≥0.05 were found, and all of them are intronic or located in the 3’-flanking region. Their LD data  are presented in http://www.broadinstitute.org/scientific-community/science/programs/medical-and-population-genetics/haploview/haploview), 14 tagging SNPs were identified covering all the other common HapMap SNPs with an r2 ≥0.8 (100% coverage of the common genetic variation in the locus). These tagging SNPs were rs2909443 (A/G) in the 3’-flanking region, rs2909448 (T/C), rs2389643 (C/T), and rs2909450 (G/A) in intron 20, rs1014444 (A/G) in intron 19, rs6432708 (T/C) in intron 8, rs12995983 (T/C) and rs3788979 (G/A) in intron 5, and rs6741949 (G/C), rs4664446 (A/G), rs741529 (G/A), rs3788976 (C/T), rs12469968 (A/G), and rs1861978 (T/G) in intron 2  derived from the Central European (CEU) population . The call rates were ≥98.9%. The mass spectrometric results were validated in 50 randomly selected subjects by bidirectional sequencing, and both methods gave 100% identical results.DNA was isolated from whole blood using a commercial kit . The 14 2 test (based on one degree of freedom). LD  between the tagging SNPs was analyzed using the MIDAS 1.0 freeware (http://www.genes.org.uk/software/midas). Continuous variables with non-normal distribution were loge-transformed prior to statistical analysis. Multiple linear regression analysis was performed using the least-squares method. In the regression models, the trait of interest  was chosen as outcome variable, the SNP genotype (in the additive inheritance model) as independent variable, and gender, age, and percentage of body fat (and ISI OGTT when testing SNP associations with insulin secretion) as confounding variables. In some analyses, glucose tolerance status was included in the multiple regression models as additional confounding variable in form of a dummy variable . For analysis of SNP-body fat interaction effects on incretin levels, the respective cross effects were tested with gender and age as confounding variables. When testing all 14 tagging SNPs in parallel, a Bonferroni-corrected p-value <0.0037 was considered statistically significant. In all subsequent follow-up analyses testing only SNP rs6741949, the significance threshold was set at p<0.05. We did not correct for the tested metabolic traits since these were not independent. For all analyses, the statistical software JMP 8.0  was used. Based on the observed MAFs, the study was sufficiently powered (1-β≥0.8) to detect SNP effects on insulin secretion ≥5% in the overall population and to detect SNP effects on fasting GLP-1 levels ≥26% in the GLP-1/GIP subgroup (two-sided p<0.05). Power calculations were performed using the Quanto 1.2.4 freeware (http://hydra.usc.edu/gxe).Hardy-Weinberg equilibrium was tested using χhttp://www.med.uni-tuebingen.de/Mitarbeiter/Kliniken/Medizinische+Klinik/Innere+Medizin+IV/Forschung+/Klinisches+Studienzentrum.html. The volume and complexity of the data collected precludes public data deposition because the participants could be identifiable from such extensive data which would compromise participants’ privacy.Data are available upon request from the principal investigators of the TÜF study. Please contact us at 2), two thirds were women and one third men , the gender distribution and the proportion of prediabetic individuals were however similar to the overall study population  and overweight , the linkage between the tagging SNPs ranged from very low (r2<0.001) to moderate (r2 = 0.613) with the following exception: the three SNPs rs2909443, rs2909448, and rs64322708 were in high linkage in our population  genetic variants in the pulation .30/GLP-10 and GIP30/GIP0. After adjustment for gender, age, and percentage of body fat, no associations that resisted Bonferroni correction for multiple comparisons (p<0.0037) could be detected, and SNP rs6741949 was the only SNP to show a nominal association with oral glucose-stimulated GLP-1 increase  only, we divided the overall study population and the GLP-1/GIP subgroup in body fat strata. Since genotype-trait interactions are often driven by the more extreme parts of a trait’s data distribution, we excluded the area around the median as “grey zone”. On the other hand, we intended to keep the excluded sample small in order not to lose statistical power. Therefore, we chose quintiles and stratified the subjects in a low body fat group (lowest two quintiles combined) and a high body fat group (highest two quintiles combined) and excluded the middle quintile from the analysis. In these follow-up analyses, the significance threshold was set at p<0.05. After adjustment for gender and age, the minor C-allele of SNP rs6741949 was associated with a lower glucose-induced GLP-1 increment in subjects with high, but not in subjects with low, body fat content  or in the low and high body fat strata .Finally, we interrogated publicly available data of the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC). In these unstratified datasets, SNP rs6741949 revealed weak trends for congruent association with three parameters of insulin secretion, i.e., insulin concentrations at 30 min of the OGTT , 30-min insulin concentrations adjusted for 30-min glucose concentrations and BMI , and incremental insulin concentrations 0–30 min . Data stratified for body adiposity were not available.DPP4 gene that negatively affects glucose-stimulated GLP-1 levels, insulin secretion, and glucose tolerance in humans. Most interestingly, the SNP effect was detectable in subjects with high body adiposity only, whereas no phenotypic differences were seen in subjects with low body adiposity. This is in agreement with the recent in vitro finding that DPP-4 represents an adipokine released by hypertrophic adipocytes [in vivo observation of higher DPP-4 levels/activity in obese rodent models [DPP4 tagging SNP study [In this study, we identified by a tagging SNP approach a common intronic variant, i.e., SNP rs6741949, in the ipocytes , with tht models  and humat models –27, and t models ,29. ThusNP study .It is noteworthy that the SNP effects were only observed when we tested for genotype-body fat interaction using bioimpedance-derived percentage of body fat, but not when using BMI. The latter is probably an insufficient proxy for body fat content in this context. This limits the possibility to replicate our results since many pre-existing human studies of comparable sample size use BMI to assess body adiposity. Thus, novel studies with more precise body fat measurements are urgently needed. The use of BMI could also be the reason why a recent meta-analysis failed to detect a significant impact of body adiposity on the efficacy of vildagliptin .http://grch37.ensembl.org/Homo_sapiens/Info/Index), from 5% in Han Chinese to 50% in subjects from Los Angeles with Mexican ancestry. Moreover, the SNP exerts its effects predominantly in subjects with high body fat, and Chinese and Mexicans also considerably vary in the prevalence of obesity . Based on these data, we would expect that the SNP plays a more important role in Mexicans than in Han Chinese.As to the SNP’s role in different ethnicities, we can just speculate. The allele frequency of the minor C-allele considerably varies between the ethnicities analyzed in the 1000 Genomes Project (GCG) is adjacent and located ~70 kb upstream of the DPP4 gene, and therefore, the SNP could theoretically influence the expression of proglucagon, the precursor of glucagon, GLP-1, and GLP-2. Thus, the SNP’s effect on GLP-1 formation/release could be a direct one. However, careful analysis of publicly available genetic linkage data (from the 1000 Genomes Project) revealed that no obvious linkage block spans both loci. Therefore, this possibility is rather unlikely, and the SNP might directly affect the DPP4 gene.With respect to the functionality of the SNP, it has to be noted that the proglucagon gene Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Identification of functional non-coding variants and their mechanistic interpretation is a major challenge of modern genomics, especially for precision medicine. Transcription factor (TF) binding profiles and epigenomic landscapes in reference samples allow functional annotation of the genome, but do not provide ready answers regarding the effects of non-coding variants on phenotypes. A promising computational approach is to build models that predict TF-DNA binding from sequence, and use such models to score a variant’s impact on TF binding strength. Here, we asked if this mechanistic approach to variant interpretation can be combined with information on genotype-phenotype associations to discover transcription factors regulating phenotypic variation among individuals.We developed a statistical approach that integrates phenotype, genotype, gene expression, TF ChIP-seq, and Hi-C chromatin interaction data to answer this question. Using drug sensitivity of lymphoblastoid cell lines as the phenotype of interest, we tested if non-coding variants statistically linked to the phenotype are enriched for strong predicted impact on DNA binding strength of a TF and thus identified TFs regulating individual differences in the phenotype. Our approach relies on a new method for predicting variant impact on TF-DNA binding that uses a combination of biophysical modeling and machine learning. We report statistical and literature-based support for many of the TFs discovered here as regulators of drug response variation. We show that the use of mechanistically driven variant impact predictors can identify TF-drug associations that would otherwise be missed. We examined in depth one reported association—that of the transcription factor ELF1 with the drug doxorubicin—and identified several genes that may mediate this regulatory relationship.Our work represents initial steps in utilizing predictions of variant impact on TF binding sites for discovery of regulatory mechanisms underlying phenotypic variation. Future advances on this topic will be greatly beneficial to the reconstruction of phenotype-associated gene regulatory networks.The online version of this article (10.1186/s12915-019-0679-8) contains supplementary material, which is available to authorized users. A major open problem today is how differences in DNA sequence, e.g., single-nucleotide polymorphisms (SNPs) and variants (SNVs), lead to health-related and other phenotypic differences among individuals. A common approach is to find polymorphisms/variants that are statistically correlated with phenotypic differences, as in genome-wide association studies (GWAS) , family-It must be noted, however, that the above approach to identify phenotype-related non-coding variants along with their regulatory mechanism is still in its infancy and its sensitivity-specificity tradeoff is not well understood. Reliable mechanistic claims of a SNP mediating a TF’s influence on phenotypic variation often require multiple lines of “-omic” evidence as well as prior knowledge. A related but less-explored opportunity is to examine a collection of variants associated with a phenotype  and test the collection for enrichment of variants predicted to impact TF-DNA binding; such an enrichment can associate the TF, rather than individual variants, with the phenotype. This may give us mechanistic insights of a more global nature, with greater confidence than what the available data allows at the level of individual SNPs. In recent work, we adopted this general strategy to identify transcription factors regulating phenotypic variation across individuals, by combining genotype, gene expression, and phenotype information with genome-wide profiles of TF-DNA binding. The underlying principles were twofold: (1) If a gene’s expression is correlated with phenotype, and a SNP correlated with that gene’s expression (eQTL of the gene) is located within a TFBS, we treated this as (weak) evidence that the TF influences the phenotype via that gene; (2) if such evidence is observed significantly many times, i.e., across many genes, we hypothesized that the TF plays an important regulatory role in phenotypic variation. The assumption is that TF binding is affected by the SNP and this effect underlies the SNP’s correlation with gene expression, which in turn contributes to phenotypic variation. We pursued this line of reasoning in , 17 to sTwo of the three pieces of information considered in the above scheme—(a) strength of SNP association with gene expression (eQTL) and (b) gene expression correlation with phenotype (a transcriptome-wide association study or “TWAS” )—are quaWe develop and use a new computational pipeline to identify TFs associated with drug response variation across individuals, building on the ideas outlined above, and performing integrative analysis of genotype, gene expression, and cytotoxicity data on a panel of ~ 300 cell lines, along with TF-ChIP data from ENCODE and TF binding motifs from various databases. We utilize a state-of-the-art, “k-mer”-based machine learning technique to predict the impact of a SNP on TF binding strength. We also develop an alternative method for this task, which uses one or more motifs known to represent a TF’s binding preferences, and combines biophysically inspired modeling and machine learning ideas. Through systematic benchmarking, we find that this motif-based method has a similar predictive ability as the “k-mer”-based technique for predicting allele-specific TF-DNA binding, in contrast to recent reports that leading k-mer-based approaches clearly outperform motif-based approaches . UltimatWe first sought a method to predict the impact of a SNP on TF binding (henceforth referred to as the “TFBS-SNP impact prediction task”), with the ultimate goal of utilizing such predictions to discover TF-phenotype relationships. This requires a sensitive method to quantify the strength of binding, since the effect of a typical SNP on a binding site is expected to be relatively modest. Several such methods have been reported in the literature , 13, 23,D. melanogaster [We first used the thermodynamics-based method called Sequence To Affinity Prediction (STAP)  and trainogaster  and in mnogaster , as wellFigure We first evaluated methods for prediction of TF binding strength from sequence, since this underlies the prediction of TFBS-SNP impacts. As noted above, the newly developed MOP score, which underlies Delta-MOP, is a generalization of the motif-based STAP method –32 for pT-test p value 0.0038, and Additional file We trained the three methods—STAP, MOP, and gkm-SVM—using the same training data set composed of 800 positive sequences (ChIP peaks of a TF) and 800 negative sequences (non-peaks), and cross-validated them on a set of 400 unseen sequences, balanced between the positive and negative classes. The negative sequences were randomly selected from the ChIP peaks of any other TF aside from the one under consideration (test TF); this is an important distinction from past benchmarks for the task  and Delta-PWM (average difference of 0.025), but not as significantly different from Delta-gkm-SVM (average difference of 0.0043). The median AUROC using Delta-MOP is 0.60 and that using Delta-gkm-SVM is 0.58. Two of the 16 TFs—BHLHE40 and EGR1—had their ASB events predicted with AUROC of ~ 0.7 or greater when using Delta-MOP. These two methods exhibited a fair degree of complementarity in their performance on different TFs , but similar to the other two methods, with the medians of Delta-MOP, Delta-gkm-SVM, and Delta-STAP being 0.39, 0.38, and 0.36 respectively.We next evaluated the above methods for the TFBS-SNP impact prediction task, by asking if the SNPs with strongest effects on predicted TF binding, henceforth called “binding-change SNPs,” are enriched for allele-specific binding sites (ASB), defined as sites where ChIP-seq read counts are significantly different between alleles . The DelTo summarize the evaluations reported above, we found that the motif-based method MOP and the k-mer-based method gkm-SVM are equally good predictors of binding strength as well as of allele-specific binding events, with noticeable degree of complementarity to each other, while MOP shows clear improvements over the two other motif-based methods evaluated. We therefore selected Delta-MOP and Delta-gkm-SVM to predict TFBS-SNP impact for the next steps of analysis. It was instructive to find that a motif-based approach (Delta-MOP) is competitive with, and for some TFs better than, the k-mer-based Delta-gkm-SVM method see “”. The saTo discover TFs associated with phenotypic variation, we adopted a statistical approach illustrated in Fig. We consider the collection of all SNPs that are located within accessible DNA in the cell type of interest; this is the “universe” set for the test. We define a subset of SNPs that are likely to impact phenotypic variation through a cis-regulatory effect on a gene’s expression; we refer to this as the “phenotype-associated” SNPs. Specifically, we identify phenotype-associated genes based on significant association between gene expression and the phenotype, and then determine significant eQTL SNPs in the regulatory regions  that have previously been assayed for their cytotoxic response (EC50) to each of 24 different treatments, mostly cancer drugs . Gene exp value < 0.0021). . We note that 21 of the 38 reported pairs have only ≤ 10% chance of being discovered when not using TFBS-SNP impact prediction scores, thereby underscoring the value of such predictions in the procedure. This comparison establishes that impact prediction scores can indeed help identify novel statistical associations, though a rigorous assessment of the sensitivity-precision tradeoff due to their use is not attempted here. In another control experiment, we assigned to each TF a random set of SNPs (size-matched with the binding-change SNP sets above) from the universe of all SNPs within accessible regions and tested all 888 TF-drug pairs. We discovered that, on average, across 100 such randomized control tests, only 1.27 pairs (about 0.14% of the 888 tested) were significant at a nominal p value of 0.0021, the criterion used above for reporting  were evaluated; we report in Table p value) have such direct confirming evidence, and these three pairs would have not have been discovered if not using TFBS-SNP impact scores .While we showed above that the use of TFBS-SNP impact scores can help predict TF-drug associations that might otherwise not rise above statistical significance, we also needed to convince ourselves that the discovered statistical associations are likely to be biologically true. In the absence of any systematic benchmarks of causal relationships between TFs and drug response, we had to rely on extensive but ad hoc survey of the literature for supporting evidence, following guidelines established in . Out of We found six additional pairs to have strongly suggestive evidence of a biological relationship. This includes cases where the TF is a demonstrated regulatory mechanism of the drug’s action . For instance, while the reported pair MAX-CDDP does not have direct evidence, the “Max Dimerization Protein 1” (MXD1), a member of the Myc-Max-Mxd family, is a well-known dimerization partner of MAX , and hasp score ≤ 0.1), making the case for the added value of these scores in cis-regulatory analysis leading to mechanisms of phenotypic variation. Some TFs and drug families were clearly overrepresented in the predictions of Table Thus, we were able to retrieve direct or indirect evidence in support of 18 of the 38 reported TF-drug pairs of Table p value), especially if the association is also supported by prior literature evidence. Thus, we considered the SNPs in the abovementioned “intersection set” as especially informative, and examined the genes linked to them for further evidence of phenotype relevance.Our statistical procedure not only identifies TFs likely to regulate a drug’s cytotoxic response, it also provides us the opportunity to probe more deeply into the regulatory interactions mediating such a TF’s influence. Each TF-drug pair is reported based on a statistically significant overlap between the drug-response SNPs and the binding change SNPs. Thus, the SNPs in this “intersection set” represent the confluence of four pieces of evidence: they are located within ChIP peaks of a TF, have evidence suggesting impact on TF binding site strength, and are statistically correlated with the expression of a cis-linked gene (via chromatin interaction), and the gene’s expression, in turn, is correlated with drug response levels. A fifth important piece of evidence is that the TF is likely to be a regulator of the phenotype  The SNPs in the intersection set for this pair were linked to 39 unique genes  relevant to phenotypic variation that would be missed if relying only on the location of variants inside TF binding sites. In doing so, we have begun to bridge the actively researched field of TFBS-SNP impact prediction  with genThe hypothesis testing approach to TF-phenotype associations was adapted from our previous work , with thWe also developed a new computational method, called MOP, for predicting TF binding strength, which underlies the Delta-MOP score for TFBS-SNP impact prediction. There are several tools available today for predicting TF binding from sequence. We chose to build MOP, based on a thermodynamics-based method called STAP , partly A note is in order regarding our overall scheme for training and testing binding predictors such as MOP and gkm-SVM: contrary to the more common practice of discriminating between ChIP peaks of a TF and random segments of the genome , we traiThe evaluation of Delta-MOP and other TFBS-SNP impact prediction methods using allele-specific binding (ASB) data is the most direct strategy for such evaluation available today, and more direct than, for example, enrichment with disease SNPs or eQTLs. The results leave us with a sense of measured optimism: while AUROC values ≥ 0.65 for 4 (≥ 0.60 for 8) of the 16 tested cases are promising, there is clearly much work cut out for the future. We refer the reader to an excellent review by Bart et al. , who poiIn earlier stages of the work, we performed preliminary evaluations of TFBS-SNP impact predictors with eQTL data rather than ASB data which was our final choice Fig. . We usedIn conclusion, this work has taken the first steps towards demonstrating the value of TFBS-SNP impact prediction in the discovery of regulatory mechanisms underlying phenotypic variation. At the same time, the performance of the impact predictors leaves much room for improvement, and future advances in this active area of research will be greatly beneficial to the reconstruction of phenotype-associated gene regulatory networks.Genotype, gene expression, and drug response data on 95 Han-Chinese, 96 Caucasian, and 93 African-American lymphoblastoid cell lines (LCL) were from the Coriell Cell Repository , of which 176 were female and 104 male. 1,344,658 germline SNPs were genotyped, and SNPs with minor allele frequency < 5% or which deviated from Hardy-Weinberg equilibrium < 95% were removed. Strand information was collected from dbSNP. 1,283,254 SNPs with same identifier and location in both LCL data and dbSNPs are used. Gene expression data consisted of 54,613 Affymetrix U133 Plus 2.0 Gene-ChIP  probes assayed for the 284 individuals, with raw expression data being transformed using GC Robust Multi-Array Averaging (GC-RMA). Genotype and gene expression data are available at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus  under SuDrug response data were derived from dosage–response curves of 24 cytotoxic treatments published in Hanson et al. : 6MP, 6Thttp://hgdownload.soe.ucsc.edu/goldenPath/hg19/databasefactorbookMotifPwm.txt.gz [Twenty-nine PWMs for 27 TFs were collected from ENCODE factor book motifs from m.txt.gz .http://www.factorbook.org/human/chipseq/tf) [One hundred eighty-five PWMs based on ChIP data for 37 TFs from GM12878 cell line were downloaded from the Factorbook website (pseq/tf) .Twenty-five PWMs for 21 TFs were obtained from HOCOMOCO Human (v10) , via theTwo hundred twenty-five PWMs for 37 TFs were collected from three sources:All motifs are included in Additional file www.encodeproject.org). Signal values are used as numeric measurements of the TF binding strength for training and testing TF-DNA binding prediction. DNaseI hypersensitivity (DHS) uniform peaks for GM12878 cell line (ENCODE project) were downloaded from the UCSC website [We used ChIP-seq data from the ENCODE project, as summarized in the “Txn Factor” track at the UCSC genome browser (“wgEncodeRegTfbsClusteredWithCellsV3” bed files). Clustered peaks observed in GM12878 cell line were used in this study. We also used genome-wide profiles of ChIP-seq signal values from the ENCODE project  We defined a large collection of “negative windows” for a TF to be 500-bp-long segments in the positive sets of other TFs but not bound by the test TF. We then randomly selected 1000 windows from this collection as the negative set for the test TF. DNA sequence and signal value for each window was extracted from the reference genome (hg19) and ChIP-seq data from the ENCODE project. Thus a balanced data set with 2000 windows was generated for each of the 37 TFs. These data sets were further separated into a balanced training set with 1600 windows and a balanced test set of 400 windows. .gkm-SVM: For each TF, a separate model was trained as recommended by authors , 11, witMOP: The scores of a window reported by STAP models using different motifs of the TF were used as a feature vector representing the window and provided to a support vector machine (SVM). We trained an SVM model (package “e1071” in R ) to predTo make the binding scores predicted by different methods fall on a comparable scale, we rescaled every score by a linear function so that the predicted binding scores for the 2000 windows in training and testing data range exactly from 0 to 1.We first generated a reference genome specific to our LCL genotype data set by starting with the “hg19” reference genome and setting the nucleotide at each SNP location (in the LCL data set) to the major allele of that SNP in the data set. For each SNP, a 501-bp window centered on that SNP was extracted from this LCL-specific reference genome, and two versions of its sequence—one with the minor allele and another with the major allele of that SNP—were used as inputs for TF binding predictors. The absolute value of the difference between predicted binding scores of these two sequences was used as the TFBS-SNP impact score . In this step, binding predictors trained on all 2000 windows defined above were used. The fourth method for SNP impact prediction, called Delta-PWM, directly uses the “Delta raw scores” for “MEME signif PWM” provided by Wagih et al. .ASB SNPs and non-ASB SNPs for lymphoblastoid cell lines were collected from Wagih et al. . Twenty-p value < 0.05 were considered as cis-eQTLs for the gene.We used Hi-C data  on 3-D cp value of 0.05 or lower as “drug response genes” and eQTLs assigned to these genes (see above) as the drug response-associated SNPs. A TF’s “binding-change SNPs” were defined as those with large TFBS-SNP impact score using either MOP or gkm-SVM. In particular, SNPs located within the TF’s ChIP peaks and ranked among the top 300 by Delta-MOP or among top 300 by Delta-gkm-SVM score were called “binding-change SNPs.”Hypergeometric tests were used to identify TFs whose “binding-change SNPs” are enriched in drug response-associated SNPs. We used SNPs in GM12878 DNase-seq narrow peaks , 81 as tAdditional file 1:Supplementary notes. This file contains seven supplementary notes, with each accompanied by short descriptions. (PDF 637 kb)Additional file 2:Table S1. The number of motifs for each TF. (2) Table S2. Accuracy values (correlation coefficient) of binding strength predictions for each TF. (3) Table S3. The AUROCs and AUPRCs of TFs in the ASB enrichment using four different TF binding predictors. (4) Table S4. TFs discovered regulating drug response using Delta-MOP alone to predict binding-change SNPs. (5) Table S5. TFs discovered regulating drug response using Delta-gkm-SVM alone to predict binding-change SNPs. (6) Table S6. The results of drug associated enrichment tests for all 888 TF-drug pairs. (7) Table S7. Mediator genes in ELF1-doxorubicin Analysis. SNPs in the intersection of TF-drug hypergeometric tests are: (1) eQTLs associated to drug response genes; (2) “binding-change SNPs” of the TF. DRGs associated with those SNPs are potentially mediators regulated by the TF. Here we list all mediator genes in ELF1-Docorubicin analysis. (XLS 149 kb)The file includes seven supplementary tables. All supplementary tables are included as sheets in this Excel file. Legends for these tables are provided here. (1) Additional file 3:Transcription factor binding motifs. We obtained 239 PWMs from three different data sources: (1) factor book motifs processed by ENCODE UCSC; (2) motifs downloaded from Factorbook; (3) HOCOMOCO Human (v10) motifs. STAP models are trained separately for each motif. We excluded a motif if the maximum prediction of the corresponding STAP model is too small (< 0.5). The remaining 225 PWMs, which were used in further analysis, are included in Additional file


Several recent studies focus on the inference of developmental and response trajectories from single cell RNA-Seq (scRNA-Seq) data. A number of computational methods, often referred to as pseudo-time ordering, have been developed for this task. Recently, CRISPR has also been used to reconstruct lineage trees by inserting random mutations. However, both approaches suffer from drawbacks that limit their use. Here, we develop a method to detect significant, cell type specific, sequence mutations from scRNA-Seq data. We show that only a few mutations are enough for reconstructing good branching models. Integrating these mutations with expression data further improves the accuracy of the reconstructed models. As we show, the majority of mutations we identify are likely RNA editing events indicating that such information can be used to distinguish cell types. Several recent methods have been developed to infer psuedo-time and branching trajectories from time series single-cell RNA-seq (scRNA-Seq) data . These mIn addition to methods that rely on expression data, several genetic based lineage tracing methods have been developed over the last two decades, though these have not been combined with scRNA-Seq analysis . RecentlWhile CRISPR based methods are useful, they are limited in several ways ,21. Firsde novo mutations are only one reason why RNAs can differ between cells. Another reason is RNA editing, which is a molecular process through which some cells make discrete changes to specific nucleotide sequences with an RNA molecular after it has been generated by RNA polymerase and was previously reported to involve in the cell differentiation process  that identifies such mutations . Once significant SNPs have been identified we use them to reconstruct a phylogenetic tree for the cells profiled. We show that the tree agrees quite well with known cell states for these cells even though we did not use the expression levels themselves to construct it. Next, we extend a previous method we developed to reconstruct dynamic models of cell differentiation so that it can utilize both expression and SNP data. As we show, the reconstructed models that utilize the SNP data further improve upon models generated by only using the expression level data indicating that SNPs provide information that is not captured by the expression levels themselves. We also discuss the biological meaning of the SNPs and argue that many of them are likely RNA editing events rather than de-novo mutations.An alternative to using CRISPR is to rely on de novo mutations. These have several advantages since they do not require any engineering, are not restricted in time and can be used for all species. The major challenge for using such an approach is the fact that such mutations are rare. For example, the mutation rate is ∼1.1e−8 per site per generation  in human process . Gommansspecific . Gagnizdll types . CombineTSBP starts by filtering potential SNPs in the scRNA-Seq data. Next cells in the input data are clustered using all significant SNPs. Starting from the initial clusters, we iterate  between cell assignments to clusters and SNP selection until convergence or the maximal iterations. The final selected SNPs are used by TBSP to calculate distances between cells (based on the Hamming distance). Neighbor-joining is then used to construct trajectories for the cells based on the calculated distance matrix. Finally, TBSP combines SNP and expression data to provide a more comprehensive view of the developmental or progression trajectories. Figure baseline SNPs) are uninformative and likely represent differences between the cell line or animal used for the experiment and the reference. We also tried other cutoffs such as 20% for Rare SNPs, and found the results to be very similar . Since we find that most significant SNPs are only identified in a small fraction of cells  such requirement means that identified SNPs represent real differences between the cells.M) for all cells (denoted by C) and all SNPs after the initial filtering (denoted by X), where M is a binary value, which tells whether SNP j is detected in cell i. As the initial set of SNPs X could possibly contain many false positive or non-informative SNPs, our objective here is to find the best subset of SNPs P from X, which can best distinguish cells of different sub-types.We first build a cell-SNP matrix (N) using K-means  where i ∈ C, j ∈ X. The number of clusters is determined using Silhouette score .We initialize the cell clusters (denoted by  K-means  on rows te score . To findk groups: for each sub-population (I) (cluster) in the data, we identify the set of SNPs that best separates the cells in I from all other cells (C − I), where C represents all the cells in the data.N denotes all the sub-population (clusters) in the data and P(I) denotes the signature SNPs for I ∈ N. Since this is a challenging combinatorial problem we use a greedy algorithm to find a local optimal solution. See To find such SNP subset P matrix might contain both false positive and non-informative SNPs. To refine the matrix and the clustering, we iterate using the identified SNP set. In each iteration, we re-cluster the cells and use the new clusters to re-identify SNPs. This is repeated until convergence or when the maximal iterations are reached. When the algorithm converges, we are left with a selected set of SNPs (P). Similar to all clustering methods this approach can be sensitive to the initial clustering result and so we include additional SNPs if they improve the Silhouette score. See The initial Vi = [M|∀j ∈ P] where M was defined in equation  is calculated using hamming distance i, Ij) is calculated as the average distance between every pair of cells in the two clusters Using the selected SNPs we utilize a distance-based neighbor-joining algorithm  to constequation . The disThe SNPs we selected provide information that is complementary to the profiled scRNA-Seq expression data. We have thus next integrated SNP information with our previously developed method for reconstructing dynamic regulatory networks from scRNA-Seq data, scdiff ,32. As tP) to help re-assign cells to states. This new assignment combines the expression profile and SNP for each cell ci as follows:P represents the probability of ci in cluster s and pe(ci|s) denotes the conditional probability of ci in cluster s based on expression, which is calculated in the same way as in scdiff (ps(ci|s), the conditional probability of cell ci in cluster s based on SNP information. The initial trajectories will be refined by the aforementioned cell assignments and the initial clusters will be also updated. Next, we re-identify the informative SNPs using the same method described above on the updated clusters. We iterate the above process until convergence or maximal iterations. The converged trajectories will be the final predictions. Here, we demonstrated the integration of the SNP information using the scdiff method. However, such SNP information can also be integrated into other existing single-cell expression based methods. TBSP provides a Cell-SNP matrix, which describes the signature SNP vector for each of the cells in the dataset and thus can be used to refine the cell assignments/trajectories in the existing expression-based methods.Here, We integrated the SNP information into scdiff. First, we build up the initial trajectories in the same way as scdiff. Next, we use not only the expression information (and the TF-gene regulatory information) as in scdiff but also the SNP information to refine the initial trajectories. We use the clusters from the initial trajectories to identify informative SNPs  list. The Lung dataset has three time points and a total of 152 cells. For all these datasets the original papers provide some information (based on known markers) about the expected trajectories or organizations, and these can be used to test the accuracy of the SNP based analysis.We first tested the reconstruction of temporal and spatial trajectories using only the SNP information. For this we used several different scRNA-Seq datasets including the ‘Neuron’ scRNA-Seq expression data which studies neuron reprogramming , the ‘LiFor the mouse ‘Neuron’ data , the SNPFor the human Liver differentiation data , the SNPFor the mouse ‘Lung’ differentiation data , the SNPWe also compared TBSP with Monocle2 . These cThe reconstruction above used 36, 55 and 33 SNPs for the Neuron, Liver and Lung data respectively. As can be seen in Figure As noted in Methods, we removed baseline mutations that are only identified because some genes are only expressed in the subset of the cells. All of the mutations we identified overlap genes that are expressed in the majority of cells. Given the relatively low genomic mutation rate and the small number of cell divisions in the data we studied (less than 10), it is unlikely that most of the SNPs we identified represent de novo mutations. We have thus looked for alternative explanations for the significant SNPs we found. One such possibility is RNA editing, which is a molecular process through which some cells make discrete changes to specific nucleotide sequences with an RNA molecular after it has been generated by RNA polymerase and was previously reported to involve in the cell differentiation process .The predicted SNPs are dominated by A/G  and C/T (U)  substitutions Figure  A. Note In addition to the type of substitution, their locations also match. Predicted SNPs are found mostly in non-coding regions Figure  especialP-value: 2.4e−4). Note that current knowledge of RNA editing sites is still limited. Finally, we used an RNA-editing site prediction tool RED-MEL . See Supporting Results for details.We found that predicted SNPs are located near the Alu elements, which is also observed for RNA-editing sites . We also RED-MEL  to scoreTo further investigate the origin of the SNPs identified, we have analyzed data from a study that jointly performed RNA and DNA seq analysis in single cells . We downP-value = 1.32e−4, FDR = 1) and “Regulation of protein complex disassembly” , which are consistent with the potential protein degradation related functions of RNA-editing previously reported by study ' and ‘contranslational protein targeting to membrane ', which is also supported by ' and ‘protein-containing complex subunit organization '.We also looked at the function of genes for which we identified SNPs in each of the datasets (Methods). In the neuron data, we found 26 such genes associated with the 36 predicted SNPs. The most significant GO terms associated with these 26 genes are ‘Regulation of protein depolymerization'  . For eacWhile all three datasets discussed above studied dynamic processes, they have only profiled hundreds of cells each (though with relatively high overage). This number is quite low compared to more recent studies that usually profile thousands of cells. To test the scalability of TBSP, we have also used it to analyze a larger dataset which profiled close to 4000 Hematopoietic stem/progenitor cells (HSPCs) from mice bone marrow . While tExisting methods for the analysis of time series scRNA-Seq data mostly utilize the expression levels for each cell. Such methods reconstruct developmental or response trajectories based on similarities between their expression . While these methods have been successfully applied, it is also clear that in many cases that cannot accurately capture the dynamic process that they are modeling due to noise, dropouts and the impact of low expressed genes.Here, we presented a complementary approach, TBSP, which utilizes what, until now, was a discarded part of the data: The errors in the sequenced RNAs. While some of these may indeed be just that (errors), others, especially those that pass stringent filtering criteria and that are identified in multiple cells, are likely to be true differences. As we show, by using the identified SNPs we can construct reasonable trajectories assigning cells to different branches even without using the expression level information. We applied TBSP to four different datasets ranging in size from less than 200 to close to 4000 cells. As we show, in all cases the method was able to obtain good clusters and trajectories when only using SNP data. When combined with expression data to resulting models are even better and improve upon expression only models.de-novo mutations inserted during cell division. However, it is unlikely that the majority are indeed such mutations given the small number of expected de-novo mutations in coding regions for the data that we studied as we estimated in the introduction. Instead, we argue that many of these likely represent RNA-editing events. Several lines of evidence support this claim. First, the type of mutation we observed, A/G or C/T substitutions, is consistent with RNA-editing sites are also mostly substitutions  technologies sacrifice the full-length coverage to sequence part of the primer used for cDNA generation ,48, whichttps://github.com/phoenixding/tbsp). As the number and types of biological processes that are studied using scRNA-Seq data increases, methods that can accurately infer developmental and response trajectories become an important part of the analysis and modeling process. We hope that TBSP, which aims at better utilization of existing data, would aid researchers seeking to analyze such time series scRNA-Seq data.Software implementing TBSP is freely available at GitHub (gkz146_Supplemental_FileClick here for additional data file.


In the past decades, substantial effort has been made to explore the genetic influence on brain structural/functional abnormalities in schizophrenia, as well as cognitive impairments. In this work, we aimed to extend previous studies to explore the internal mediation pathway among genetic factor, brain features and cognitive scores in a large Chinese dataset.Gray matter (GM) volume, fractional amplitude of low-frequency fluctuations (fALFF), and 4522 schizophrenia-susceptible single nucleotide polymorphisms (SNP) from 905 Chinese subjects were jointly analyzed, to investigate the multimodal association. Based on the identified imaging-genetic pattern, correlations with cognition and mediation analysis were then conducted to reveal the potential mediation pathways.CSMD1, CNTNAP2, DCC, GABBR2 etc. This linked pattern was also replicated in an independent cohort (166 subjects), which although showed certain age and clinical differences with the discovery cohort. A further mediation analysis suggested that GM alterations significantly mediated the association from SNP to fALFF, while fALFF mediated the association from SNP and GM to working memory performance.One linked imaging-genetic pattern was identified to be group discriminative, which was also associated with working memory performance. Particularly, GM reduction in thalamus, putamen and bilateral temporal gyrus in schizophrenia was associated with fALFF decrease in medial prefrontal cortex, both were also associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including This study has not only verified the impaired imaging-genetic association in schizophrenia, but also initially revealed a potential genetic-brain-cognition mediation pathway, indicating that polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus could further affect cognition in schizophrenia. Schizophrenia is a severe psychiatric disorder demonstrating a strong genetic component with heritability and more and more GWAS analyses towards schizophrenia have been reported. To examine if these genetic schizophrenia-risk variants may affect brain structure, function or even cognitive performance, an effective strategy is to assess the genetic associations with brain-imaging phenotypes as well as behavioral measures; then, based on the identified linked factors, mediation analysis can be conducted to better delineate cognition behaviors among them.CSMD1, CNTNAP2, DCC, GABBR2 etc. The linked imaging-genetic pattern was replicated in an independent Chinese cohort and was also associated with working memory performance. Further mediation analyses suggested a “This study is the first attempt to jointly analyze brain fALFF, GM data in conjunction with PGC's reported SNPs in a large Chinese Han population (905 + 166 subjects) using a multivariate, data-driven manner. Gray matter volume reductions in thalamus, putamen and bilateral temporal gyrus in schizophrenia were associated with fALFF decrease in medial prefrontal cortex. Both these imaging impairments were associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including This study indicates that brain GM and fALFF abnormalities are significantly correlated with the schizophrenia-susceptible SNPs, and polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus may further lead to cognitive impairment in schizophrenia.Alt-text: Unlabelled Box1Schizophrenia (SZ) is a severe psychiatric disorder demonstrating a strong genetic component with heritability estimated up to 80% based on family and twin studies . A landmDISC1 and ANK3, have been revealed to be associated with reduced GM in SZ on hippocampus, prefrontal cortex and temporal regions [In this study, we focus on two imaging features, i.e., gray matter (GM) and fractional amplitude of low frequency fluctuations (fALFF), which are proven to be heritable in previous imaging-genetic analyses. Specifically, GM volume was validated to be one heritable measure as reported in ,4. Schiz regions . Moreove regions . And the regions . FALFF i regions ,16. A mu regions ; and the regions . Additio regions . Current regions , multimo regions ,24,83 an regions .COMT gene on cognitive control [In parallel, cognitive impairments are well documented in schizophrenia and there is a notion that genetic effects on cognitive abilities are mediated by brain functions. For example, prefrontal brain region has been revealed to mediate the effects of  control  . Moreov control , while t control , suggest control . All theCollectively, we aimed to use a multimodal fusion method ,30 to ex22.1t-test to measure the difference of age, duration of illness, PANSS scores and medication in controls and patients between cohort 1 and cohort 2. Chi-square test was adopted to compute the cross-cohort difference of gender as shown in The discovery dataset (cohort 1) consisted of 450 SZs and 455 HCs from Chinese Han population , which w2.2To control the discrepancy of imaging quality and performance across seven sites of corhot 1, we repeatedly scanned three healthy subjects by the scanners from all sites. Then, we evaluated the performance of imaging system by calculating signal-to-noise ratio (SNR) of the MRI system and coils in each site. Based on these SNRs, we optimized and identified the final scan parameters. Furthermore, as differences may exist in different hardware and software systems, we set the MRI sequences and parameters by following the two principles:  made the2.2.1Subjects were instructed to keep their eyes open during the scan and stare passively at a foveally presented fixation cross, as this is suggested to facilitate network delineation compared to eyes-closed conditions and helps ensure that subjects are awake. Resting state data were collected with single-shot full k-space echo-planar imaging (EPI) with ramp sampling correction using the inter commissural line (AC/PC) (anterior commissure/posterior commissure) as a reference, and details of the machine type and scanning parameters for each site in cohort 1 and cohort 2 were listed in Table S1.2.2.2The scanning parameters for each site were listed in Table S1.2.2.3We collected ethylene diamine tetraacetic acid (EDTA) anti-coagulated venous blood samples from all the participants and then extracted genomic Deoxyribonucleic acid (DNA) from their whole blood using the EZgene Blood gDNA Miniprep Kit. The whole-genome genotyping was performed on Illumina Human OmniZhongHua-8 BeadChips using the standard Illumina genotyping protocol (Illumina) spanning 900,015 SNP loci. For cohort 2, DNA was extracted from peripheral blood cell according to the standard protocol by protease K digestion, phenol-chloroform extraction and ethanol precipitation. The whole-genome genotyping was performed on Illumina human PsychArray-24 spanning 571,054 loci.Cognition assessment: Symptomatic assessment was performed using the Positive and Negative Syndrome Scale (PANSS) . Digital2.3The multimodal data used in the present study can be accessed upon request to the corresponding authors.2.4www.fil.ion.ucl.ac.uk/spm/software/spm8) using voxel based morphometry (VBM), with a unified model of image registration, bias correction, tissue classification and spatial normalization to the standard Montreal Neurological Institute (MNI) space [3, resulting in 53 × 63 × 46 voxels and smoothed with an 8 mm Gaussian model [sMRI data preprocessing. The T1-weighted sMRI data were preprocessed by Statistical Parametric Mapping 8  space . Modulatan model . A mask an model . We applhttp://brant.brainnetome.org/en/latest/) was employed to preprocess the collected fMRI data. The first 10 volumes of each functional time series were discarded for the magnetization equilibrium. Slice timing was performed with the middle slice as the reference frame. Then images were realigned using INRIalign, a motion correction algorithm that is unbiased by local signal changes, resulting in head motion parameters computed by estimating translational and rotational parameters. Each subject had a maximum displacement in a dataset that didn't exceed ±1.5 mm or  ±1.5°. Then data were spatially normalized into Montreal Neurological Institute (MNI) space [3. Denoising were further performed to regress out motion parameters, white matter, and cerebrospinal fluid. Voxel-wise fractional amplitude of low-frequency fluctuations (fALFF) were extracted to generate a map for each subject and spatially smoothed with an 8-mm isotropic Gaussian kernel. A mask was generated to include only voxels inside the brain across all subjects for each modality as our previous study [fMRI data preprocessing. Brant software package (I) space , resliceus study . We appl2.4.1p-values of SZ patients versus HCs tested against a uniform distribution showed no clear indication of population structure .Genotype data  were preprocessed using PLINK software following a series of standard quality control procedures ,36. Poor2.4.2https://www.med.unc.edu/pgc/results-and-downloads), and selected the SNPs from the GWAS results with a p-value lower than 0.01. Then we identified the overlap of the three SNP datasets , resulting in 4522 SNPs. Note that we have conducted imputation only on cohort 2, but not cohort 1. The SNPs were not selected by their odds ratios with schizophrenia but only the p-value from PGC according to our previous studies [As for cohort 2, the ungenotyped SNPs were imputed using SHAPEIT v2 (r790)  and IMPU studies ,41.subj × Nvoxel) for each of the three modalities. Then three feature matrices were organized as: fALFF , GM  and SNPs (905 × 4522), which corresponded to X1, X2, X3 in The analysis flowchart is shown in 2.5X1, X2, X3 are observation matrices composed of measurements of fALFF, GM and SNP, that are the three Nsubject × Nvoxel/SNP matrices prepared above; S1, S2, S3 represent the unknown sources for each phenotype with each row representing an independent component; A1, A2, A3 are linear mixing matrices/loadings with each column representing a loading parameter associated with a specific component in S1, S2, S3; W1, W2, W3 are unmixing matrices. Based on the decomposition, the Infomax algorithm attempts to find the W matrix resulting in independence through maximizing an entropy function as defined in Eq. fy(Y) is the probability density function of Y; E is the expected value; H is the entropy function; W0 is the bias vector.The formed fALFF, GM and SNP matrices were jointly analyzed using three-way parallel independent component analysis (para-ICA) method, which estimates maximally independent component (IC) using ICA within each modality separately and maximizes the pair-wise correlations between modalities using an entropy term based on information theory . The comA1, A2, A3.Thus, three entropy terms and an additional correlation term comprise the objective function of three-way para-ICA, as shown in Eq. fi, j, k is the additional aggregation function measuring the combined connection strength of the columns i, j, k from the corresponding matrices A1, A2, A3, respectively. This objective function will then be optimized through the gradient descent method. For a full description of the methodology and mathematical details of the algorithm, we refer readers to the original publications [To enhance the inter-modality association, three-way para-ICA attempts to maximize the correlation calculated between the columns of the loadings matrices ications ,43.(3)arhttp://mialab.mrn.org/software/fit). Since the number of true sources from in-vivo data is in principal unknown, it is necessary to estimate the number of components before proceeding with the ICA factorization [The code is available for public use through the Fusion ICA Toolbox  for cohort 2  that corresponded to the extent that a given subject's data from cohort 2 could be represented by the components determined from cohort 1. This was accomplished by multiplying  with the pseudoinverse of the source of cohort 1 . The generated weights  for cohort 2 were then evaluated for inter-modality correlations to assess whether the pair-wise correlations between the identified ICs could be replicated in another independent cohort.The spatial maps of the most significantly linked SNP-fALFF-GM pattern were projected to cohort 2, yielding the projected new loadings which were used to verify the pair-wise inter-modality association as  Fig. 1)Fig. 1). p = .05). We then selected a subset of subjects in cohort 2, which consists of 49 SZ patients together with 87 HCs. The 49 patients did not show significant difference of PANSS scores compare with discovery data . Then we ran same validating procedure by projecting spatial maps of discovery cohort 1 to the subset of cohort 2.In the current study, stable antipsychotic treatment on the patients was not required at the moment of the MRI, so not all patients were on stable antipsychotic treatment at the moment of the MRI. But the PANSS total score was similar across two cohorts (2.8Z-scores| > 2) from the identified SNP component were annotated to the genes which were further applied Gene Ontology analysis by an online database WebGestalt . A hypergenometric test was used to detect an overrepresentation of the high-ranking genes among all the genes in a category. The whole genome was selected as the reference gene list. The significance values were FDR-adjusted to correct for multiple comparison.Top contributing SNPs  /PANSS scores and para-ICA-derived components. Partial Pearson correlation was applied to compute the correlations between the identified ICs with working memory scores or PANSS scores separately, in which age, gender, site and diagnosis were controlled as covariates. Medication was regressed out on the 243 patients before computing the association.2.10Once a SNP-fALFF-GM pattern was identified to be group discriminative and associated with working memory performance (e.g. DB scores), as well as exhibiting significant pair-wise inter-modality correlation, we further conducted mediation analysis to better delineate the working pathways among them as shown in enotypes , we examIn order to test the direct and indirect effects among the identified SNP, fALFF, GM and working memory scores, bootstrapping is one of the most valid and powerful methods . It is a33.1r = 0.753, p < 10−12; SNP-fALFF: r = 0.174, p = 1.32 × 10−7; SNP-GM: r = 0.173, p = 1.78 × 10−7, passing Bonferroni-correction for multiple comparison. The correlations were consistent in 10-fold stability analysis with the average correlations of 0.713 ± 0.043, 0.179 ± 0.028, 0.180 ± 0.013 for fALFF-GM, SNP-fALFF and SNP-GM respectively. The identified linked pattern was robust to component numbers, as we observed similar components and inter-modality correlations when component number was set ±3 around the original number for each modality. After controlling for the diagnosis, partial correlations among the linked components were still significant . We then included the first three PCs as additional covariates and assessed the correlation between imaging modalities and SNP modality. The correlations between the two imaging components and the SNP component were still significant: SNP-fALFF: r = 0.148, p = 8.62 × 10−6; SNP-GM: r = 0.130, p = 9.80 × 10−5 (survived Bonferroni-correction). The less minor/reference allele counts on SNPs with positive component scores induce reduction of fALFF and GM. Furthermore, we conducted three-way para-ICA analysis on SNPs yielded by two other thresholds (p < 0.005 and p < .005) and those imaging features separately. In both p < 0.005 and p < 0.005 cases, the three-way pair-wise correlations between modalities were remained, and the fALFF and GM components show highly consistent spatial patterns. For SNP component, 52.5% overlap were obtained compared with p < 0.01. Note that although the associations could not survive correction for multiple comparisons, the SNP-fALFF-SNP pattern was similar to that identified with p < 0.01, as shown in Fig. S4.One strongest pair-wise correlated SNP-fALFF-GM triplet was identified in cohort 1. The pair-wise correlations between the linked components were fALFF-GM: In addition, age and gender in general are not expected to be significantly associated with autosomal SNPs. Therefore, we ran another analysis by regressing out age, gender and site for imaging modalities and regressing only site for the SNPs. The results are presented in Fig. S5. It can be seen that the three-way para-ICA can still yield a linked SNP-fALFF-GM pattern that was highly consistent between cohort 1 and cohort 2.Since no significant correlation was observed between the identified imaging components and the medication dose measured by the chlorpromazine equivalent  based on the 243 (out of 450) medicated patients, we further conducted a sub-set three-way para-ICA analysis on the 243 medicated SZ patients and 277 demographically-matched healthy controls. In this subset, medication was treated as nuisance covariate in the analysis both for fMRI and sMRI. The results are shown in Fig. S6, similar SNP-fALFF-GM pattern was revealed as well with significant correlation between modalities and significant group difference in each modality.3.2t-test between HCs and SZs revealed significant group difference in the identified GM (p = 0.0014), fALFF (p = 0.0004) and SNP (p = 0.044) components respectively as shown in Z-values (red regions) indicated higher contribution in HC than SZ and the negative Z-values (blue regions) indicated higher contribution in SZ than HC. The spatial map of the GM component thresholded at |Z-score| > 2 was shown in Post hoc two sample 3.3r = 0.680, p < 10−12; SNP-fALFF: r = 0.220, p = 0.0045; SNP-GM: r = 0.237, p = 0.0021, passing Bonferroni correction). When controlling for the diagnosis effect, the resulted loadings in cohort 2 again yielded significant pair-wise correlations . Meanwhile, we only observed significant group difference between SZ and HC in brain imaging data, p = 8.34 × 10−4, 0.024, 0.98 for fALFF, GM and SNP respectively . In addition, when controlling for the diagnosis and medication effects, the resulted loadings in the subset of cohort 2 again yielded significant pair-wise correlations , suggesting that the identified linked fALFF-GM-SNP pattern was reliable under multiple conditions.We then projected the spatial components of cohort 1 to cohort 2 as shown in 3.4CSMD1, CNTNAP2, DCC, GABBR2 etc. 172 SNPs were selected as top SNPs with |Z| > 2. Table S4 provides a summary of the identified 172 SNPs, including their corresponding genes and Z-scores. After annotating these SNPs, 75 genes were analyzed via a WebGestalt pathway analysis. Primarily significant Gene Ontology analysis results were provided in p = 8.49 × 10−6), synapse organization (p = 3.18 × 10−5) and axon (p = 1.26 × 10−4) in our study. The full results of Gene Ontology analysis were provided in Table S5. The pathway results for different thresholds were also provided in the supplementary file (Table S6).3.5r = 0.296, p = 1.19×10−17; GM-DB: r = 0.290, p = 4.79×10−17; SNP-DB: r = 0.114, p = 0.012; fALFF-DF: r = 0.186, p = 1.18×10−9; GM-DF: r = 0.104, p = 0.0031) after controlling for group difference. The association was still significant  when further controlling for medication effects on the GM and fALFF modalities. Results indicated higher GM volume in thalamus, putamen, temporal gyrus, cerebellum, higher fALFF in prefrontal gyrus, but lower fALFF in cerebellum and more minor/reference allele counts on SNPs were associated with better working memory performance. No significant correlation was found between PANSS and the identified SNP-fALFF-GM component.3.6Table S7-A indicated that both direct  and indirect effect  of SNP component on fALFF component via GM component was significant, which suggested a mediation effect presented as “4To the best of our knowledge, this is the first study to jointly analyze fALFF, GM volume in conjunction with PGC-reported schizophrenia-susceptible SNPs in a large Chinese Han population using a multivariate, data-driven manner. One linked SNP-fALFF-GM component was identified to be both group discriminative and show significant correlation between modalities. Furthermore, post hoc mediation analyses suggested a significant “SNPOn the lined components, GM volume reduction in schizophrenia was revealed in thalamus, putamen, bilateral temporal gyrus, cuneus and cerebellum, which has been supported by multiple studies . ParticuFor the corresponding fALFF component, we observed reduced fALFF in prefrontal gyrus in SZ, which is consistent with other reported studies ,62, as wCSMD1, CNTNAP2, DCC, GABBR2 etc. A recent study investigated the specific gene expression patterns arising from the 108 schizophrenia-associated loci also revealed that the genes in these loci were intensively enriched in the thalamus, cerebellum and cortex during several developmental stages [CSMD1 plays a significant role in the etiology of schizophrenia [CNTNAP2 was reported significantly associated with schizophrenia and major depression in the Han Chinese population [DCC during development and/or throughout life confers resilience to the development of schizophrenia-like dopamine and behavioral abnormalities [GABAB receptor subunits 1 and 2 (GABBR1 and GABBR2) were reported to alter significantly in the lateral cerebellum of subjects with schizophrenia [NFASC is significantly downregulated in the superior temporal gyrus of persons with schizophrenia [On the genetic association, both imaging impairments in GM and fALFF are associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including l stages . The prel stages . CSMD1 pophrenia  and was ophrenia . CNTNAP2pulation  and highpulation ,71. The malities . Expressophrenia . The mRNophrenia . Biologiophrenia . Synapseophrenia . In the ophrenia ,78.More importantly, the identified associations were replicated in an independent Chinese Han dataset. In the validation, not only were the identified SNP-fALFF-GM spatial patterns similar between discovery and replication data, but the pair-wise association between 3 components were also replicated. In addition, we tried several strategies to control for age, medication differences across sites by running same analysis in several matched subsets. Appreciably, a similar SNP-fALFF-GM pattern could be found in all cases, suggesting that the identified imaging-genetic association impaired in SZ could be replicated in multiple conditions.SCN1A allele-dependent activation differences in superior frontal gyrus typically involved in working memory processes [CSMD1 was also proved to be associated with working memory through affecting prefrontal gyrus [PPP2R2B was suggested to affect mRNA expression of this gene in prefrontal cortex to be associated with prefrontal processing during working memory [As to the association with cognition, when assessing the correlation between the identified SNP-fALFF-GM components and working memory performance (DF and DB scores), the imaging-genetic pattern were significantly associated with DB scores. Further mediation analyses revealed the GM mediated the association between SNP and fALFF, which was consistent with our hypothesis. Many studies have supported this finding by suggesting structure can determine or predict function ,48 and srocesses . Anotheral gyrus . Furtherg memory . FinallyIn addition, the current study conducted imaging-genetic analysis in a large Chinese Han population, which would raise a question that if it is reasonable to select risk SNPs based on the PGC GWAS. The explanations could be, first, the recent Chinese Han GWAS study  has claiSeveral limitations still need to be addressed in the present work. First, though no direct correlation was found between the incomplete medication information of antipsychotic dose and the identified components, medication may still have an underlying effect on multimodal imaging measures of schizophrenia patients as medication information for part of the patients were unavailable . The sec5To the best of our knowledge, this is the first attempt to jointly analyze brain fALFF, GM in conjunction with PGC's reported SNPs in a large Chinese Han population (905 + 166 subjects), using a multivariate, data-driven approach. Results demonstrated GM reduction in thalamus, putamen, temporal gyrus, cuneus, cerebellum were correlated with fALFF alteration in prefrontal and cerebellum. Furthermore,both imaging phenotypes were significantly correlated with genetic factors enriched in neuron development, synapse organization and axon pathways. Moreover, subsequent mediation analysis supported our initial hypothesis of revealing a “Natural Science Foundation of China ; the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDBS01040100); the Science Frontier Program of the Chinese Academy of Sciences (No. QYZDJ-SSW-SMC019); National Key R&D Program of China (No. 2017YFA0105203); Primary Research & Development Plan of Jiangsu Province (No. BE2016630); the National Institute of Health  and the National Science Foundation (No. 1539067).This work was supported by the The funder of the study played no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding authors had full access to all the data in current study and had final responsibility for the decision to submit for publication.The authors report no biomedical financial interests or potential conflicts of interest.NL, JS, JC, DL, TJ participated in the planning and design of the study. NL conducted the data analysis. NL, JS, JC wrote the paper. NL, KX, SL, JL preprocessed the data. JS, JC, DL, VDC, JL, VMV, YC, FZ, ZY, NZ, LF participate the results interpretation. MS, DZ and TJ design the protocol of data scanning and collection. FZ, LT, YX, SL contributed to the recruitment of the subjects in the Wuxi Mental Center. LL, YY, HZ contributed to the recruitment of the subjects in the Xinxiang Site. PL, LL, HY, JY, DZ contributed to the recruitment of subjects in the Beijing site. JC, HW contributed to the recruitment of the subjects in the Wuhan Site. YC, HW contributed to the recruitment of subjects in the Xijing Site. HG, PW contributed to the recruitment of subjects in the Zhumadian Site.