SNP Set Research Articles

Inclusion of imputed genotypes from non-genotyped dairy cattle in a Thai multibreed genomic-polygenic evaluation.

This study assessed the impact of incorporating imputed SNP information from non-genotyped animals on genomic-polygenic evaluations in a Thai multibreed dairy population under various levels of imputation accuracy. Data encompassed pedigree and phenotypic records for 305-day milk yield (MY), 305-day fat (Fat), and age at first calving (AFC) from 12,859 first-lactation cows, and genotypic records of various densities from 4,364 animals. A set of 64 animals genotyped with GeneSeek Genomic Profiler 80K and with four or more genotyped progenies was defined as target animals to simulate imputation scenarios for non-genotyped individuals. Actual and imputed genotypes were utilized to construct three SNP sets. All SNP Sets contained actual and imputed SNP markers from genotyped animals. SNP Set 1 contained no SNPs from target animals, whereas SNP Set 2 incorporated imputed SNPs from target animals, and SNP Set 3 added actual SNPs from target animals. Genomic-polygenic evaluations were conducted using a 3-trait single-step model that included contemporary group, calving age, and heterozygosity as fixed effects and animal additive genetic and residual as random effects. The imputation accuracy was similar across non-genotyped animals irrespective of the number of genotyped progenies (average: 40.55%; range: 34.68% to 53.82%). Estimates of additive genetic and environmental variances and covariances for MY and AFC varied across SNP sets. SNP Sets 1 and 2 had slightly higher additive genetic and lower environmental variances and covariances than SNP Set 3. Heritabilities and additive genetic, environmental, and phenotypic correlations between MY, Fat, and AFC were similar across all SNP Sets. Spearman rank correlations between genomic-polygenic EBVs from SNP Sets 2 and 3 were high for all traits (0.9990±0.0003). Utilization of phenotypic and pedigree data from imputed non-genotyped animals enhanced the efficiency and cost-effectiveness of the genetic improvement program in the Thai multibreed dairy cattle population.

A Tool for the Design of the Minimal Fingerprinting SNP Set: Use Case for Barley

A Tool for the Design of the Minimal Fingerprinting SNP Set: Use Case for Barley

Forensic height estimation using polygenic score in Korean population.

Height is known to be a classically heritable trait controlled by complex polygenic factors. Numerous height-associated genetic variants across the genome have been identified so far. It is also a representative of externally visible characteristics (EVC) for predicting appearance in forensic science. When biological evidence at a crime scene is deficient in identifying an individual, the examination of forensic DNA phenotyping using some genetic variants could be considered. In this study, we aimed to predict 'height', a representative forensic phenotype, by using a small number of genetic variants when short tandem repeat (STR) analysis is hard with insufficient biological samples. Our results not only replicated previous genetic signals but also indicated an upward trend in polygenic score (PGS) with increasing height in the validation and replication stages for both genders. These results demonstrate that the established SNP sets in this study could be used for height estimation in the Korean population. Specifically, since the PGS model constructed in this study targets only a small number of SNPs, it contributes to enabling forensic DNA phenotyping even at crime scenes with a minimal amount of biological evidence. To the best of our knowledge, this was the first study to evaluate a PGS model for height estimation in the Korean population using GWAS signals. Our study offers insight into the polygenic effect of height in East Asians, incorporating genetic variants from non-Asian populations.

QTL discovery for agronomic and quality traits in diploid potato clones using PotatoMASH amplicon sequencing.

We genotyped a population of 618 diploid potato clones derived from six independent potato-breeding programmes from NW-Europe. The diploids were phenotyped for 23 traits, using standardized protocols and common check varieties, enabling us to derive whole population estimators for most traits. We subsequently performed a genome-wide association study (GWAS) to identify quantitative trait loci (QTL) for all traits with SNPs and short-read haplotypes derived from read-backed phasing. In this study, we used a marker platform called PotatoMASH (Potato Multi-Allele Scanning Haplotags); a pooled multiplex amplicon sequencing based approach. Through this method, neighboring SNPs within an amplicon can be combined to generate multiallelic short-read haplotypes (haplotags) that capture recombination history between the constituent SNPs and reflect the allelic diversity of a given locus in a different way than single bi-allelic SNPs. We found a total of 37 unique QTL across both marker types. A core of 10 QTL was detected with SNPs as well as with haplotags. Haplotags allowed to detect an additional 14 QTL not found based on the SNP set. Conversely, the bi-allelic SNP set also found 13 QTL not detectable using the haplotag set. We conclude that both marker types should routinely be used in parallel to maximize the QTL detection power. We report 19 novel QTL for nine traits: Skin Smoothness, Sprout Dormancy, Total Tuber Number, Tuber Length, Yield, Chipping Color, After-cooking Blackening, Cooking Type, and Eye depth.

Genomic prediction of resistance to Hymenoscyphus fraxineus in common ash (Fraxinus excelsior L.) populations.

The increase in introduced insect pests and pathogens due to anthropogenic environmental changes has become a major concern for tree species worldwide. Common ash (Fraxinus excelsior L.) is one of such species facing a significant threat from the invasive fungal pathogen Hymenoscyphus fraxineus. Some studies have indicated that the susceptibility of ash to the pathogen is genetically determined, providing some hope for accelerated breeding programs that are aimed at increasing the resistance of ash populations. To address this challenge, we used a genomic selection strategy to identify potential genetic markers that are associated with resistance to the pathogen causing ash dieback. Through genome-wide association studies (GWAS) of 300 common ash individuals from 30 populations across Poland (ddRAD, dataset A), we identified six significant SNP loci with a p-value ≤1 × 10-4 associated with health status. To further evaluate the effectiveness of GWAS markers in predicting health status, we considered two genomic prediction scenarios. Firstly, we conducted cross-validation on dataset A. Secondly, we trained markers on dataset A and tested them on dataset B, which involved whole-genome sequencing of 20 individuals from two populations. Genomic prediction analysis revealed that the top SNPs identified via GWAS exhibited notably higher prediction accuracies compared to randomly selected SNPs, particularly with a larger number of SNPs. Cross-validation analyses using dataset A showcased high genomic prediction accuracy, predicting tree health status with over 90% accuracy across the top SNP sets ranging from 500 to 10,000 SNPs from the GWAS datasets. However, no significant results emerged for health status when the model trained on dataset A was tested on dataset B. Our findings illuminate potential genetic markers associated with resistance to ash dieback, offering support for future breeding programs in Poland aimed at combating ash dieback and bolstering conservation efforts for this invaluable tree species.

Breast Cancer Polygenic Risk Score Validation and Effects of Variable Imputation.

Breast cancer (BC) is a complex disease affecting one in eight women in the USA. Advances in population genomics have led to the development of polygenic risk scores (PRSs) with the potential to augment current risk models, but replication is often limited. We evaluated 2 robust PRSs with 313 and 3820 SNPs and the effects of multiple genotype imputation replications in BC cases and control populations. Biological samples from BC cases and cancer-free controls were drawn from three European ancestry cohorts. Genotyping on the Illumina Global Screening Array was followed by stringent quality control measures and 20 genotype imputation replications. A total of 468 unrelated cases and 4337 controls were scored, revealing significant differences in mean PRS percentiles between cases and controls (p < 0.001) for both SNP sets (313-SNP PRS: 52.81 and 48.07; 3820-SNP PRS: 55.45 and 49.81), with receiver operating characteristic curve analysis showing area under the curve values of 0.596 and 0.603 for the 313-SNP and 3820-SNP PRS, respectively. PRS fluctuations (from ~2-3% up to 9%) emerged across imputation iterations. Our study robustly reaffirms the predictive capacity of PRSs for BC by replicating their performance in an independent BC population and showcases the need to average imputed scores for reliable outcomes.

Genomic prediction of male fertility in Brown Swiss cattle

Bull fertility has been recognized as an important factor affecting dairy herd fertility. The objective of this study was to assess the feasibility of predicting male fertility in Brown Swiss cattle using genomic data. The data set consisted of 1,102 Italian Brown Swiss bulls with sire conception rate (SCR) records and genotype data for roughly 480k SNP. The analyses included the use of linear kernel-based regression models fitting all SNPs or incorporating markers with large effect. Predictive performance was evaluated in 5-fold cross-validation using the correlation between observed and predicted SCR values and mean squared error of prediction. The entire SNP set exhibited predictive correlations around 0.19. Interestingly, the inclusion of 2 markers with large effect yielded predictive correlations around 0.32. Overall, using linear kernel-based models fitting markers with large effect is a promising approach. Our findings could help Brown Swiss breeders make enhanced genome-guided management and selection decisions on male fertility.

Comparison of ddRADseq and EUChip60K SNP genotyping systems for population genetics and genomic selection in Eucalyptus dunnii (Maiden).

Eucalyptus dunnii is one of the most important Eucalyptus species for short-fiber pulp production in regions where other species of the genus are affected by poor soil and climatic conditions. In this context, E. dunnii holds promise as a resource to address and adapt to the challenges of climate change. Despite its rapid growth and favorable wood properties for solid wood products, the advancement of its improvement remains in its early stages. In this work, we evaluated the performance of two single nucleotide polymorphism, (SNP), genotyping methods for population genetics analysis and Genomic Selection in E. dunnii. Double digest restriction-site associated DNA sequencing (ddRADseq) was compared with the EUChip60K array in 308 individuals from a provenance-progeny trial. The compared SNP set included 8,011 and 19,008 informative SNPs distributed along the 11 chromosomes, respectively. Although the two datasets differed in the percentage of missing data, genome coverage, minor allele frequency and estimated genetic diversity parameters, they revealed a similar genetic structure, showing two subpopulations with little differentiation between them, and low linkage disequilibrium. GS analyses were performed for eleven traits using Genomic Best Linear Unbiased Prediction (GBLUP) and a conventional pedigree-based model (ABLUP). Regardless of the SNP dataset, the predictive ability (PA) of GBLUP was better than that of ABLUP for six traits (Cellulose content, Total and Ethanolic extractives, Total and Klason lignin content and Syringyl and Guaiacyl lignin monomer ratio). When contrasting the SNP datasets used to estimate PAs, the GBLUP-EUChip60K model gave higher and significant PA values for six traits, meanwhile, the values estimated using ddRADseq gave higher values for three other traits. The PAs correlated positively with narrow sense heritabilities, with the highest correlations shown by the ABLUP and GBLUP-EUChip60K. The two genotyping methods, ddRADseq and EUChip60K, are generally comparable for population genetics and genomic prediction, demonstrating the utility of the former when subjected to rigorous SNP filtering. The results of this study provide a basis for future whole-genome studies using ddRADseq in non-model forest species for which SNP arrays have not yet been developed.

Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies

BackgroundNon-linear relationships at the genotype level are essential in understanding the genetic interactions of complex disease traits. Genome-wide association Studies (GWAS) have revealed statistical association of the SNPs in many complex diseases. As GWAS results could not thoroughly reveal the genetic background of these disorders, Genome-Wide Interaction Studies have started to gain importance. In recent years, various statistical approaches, such as entropy-based methods, have been suggested for revealing these non-additive interactions between variants. This study presents a novel prioritization workflow integrating two-step Random Forest (RF) modeling and entropy analysis after PLINK filtering. PLINK-RF-RF workflow is followed by an entropy-based 3-way interaction information (3WII) method to capture the hidden patterns resulting from non-linear relationships between genotypes in Late-Onset Alzheimer Disease to discover early and differential diagnosis markers.ResultsThree models from different datasets are developed by integrating PLINK-RF-RF analysis and entropy-based three-way interaction information (3WII) calculation method, which enables the detection of the third-order interactions, which are not primarily considered in epistatic interaction studies. A reduced SNP set is selected for all three datasets by 3WII analysis by PLINK filtering and prioritization of SNP with RF-RF modeling, promising as a model minimization approach. Among SNPs revealed by 3WII, 4 SNPs out of 19 from GenADA, 1 SNP out of 27 from ADNI, and 4 SNPs out of 106 from NCRAD are mapped to genes directly associated with Alzheimer Disease. Additionally, several SNPs are associated with other neurological disorders. Also, the genes the variants mapped to in all datasets are significantly enriched in calcium ion binding, extracellular matrix, external encapsulating structure, and RUNX1 regulates estrogen receptor-mediated transcription pathways. Therefore, these functional pathways are proposed for further examination for a possible LOAD association. Besides, all 3WII variants are proposed as candidate biomarkers for the genotyping-based LOAD diagnosis.ConclusionThe entropy approach performed in this study reveals the complex genetic interactions that significantly contribute to LOAD risk. We benefited from the entropy-based 3WII as a model minimization step and determined the significant 3-way interactions between the prioritized SNPs by PLINK-RF-RF. This framework is a promising approach for disease association studies, which can also be modified by integrating other machine learning and entropy-based interaction methods.

Improving gene expression prediction models through the use of meta‐analysis summary statistics

AbstractBackgroundBy focusing on a gene‐level rather than a single‐variant basis, transcriptome‐wide association studies (TWAS) assess the role of genetically regulated gene expression in disease risk and improve statistical power by reducing the multiple testing burden by over a 100‐fold. A key limitation of current TWAS approaches is the requirement for dense individual‐level datasets with both genotypes and gene expression measures to produce effective expression prediction models. We sought to improve the statistical power of AD TWAS by leveraging easily accessible summary statistics taken from large‐scale expression quantitative trait loci (eQTL) meta‐analyses.MethodWe used eQTLGen Whole Blood cis‐eQTL data (N = 31,684) as our source of meta‐analysis summary statistics. To perform gene‐based elastic‐net regressions using the summary statistics, we used the lassosum R package (Mak et al. 2017) with 1000 Genomes data as a genomic reference and GTEx v8 genotype, normalized Whole Blood gene expression and covariate data (N = 670) as a testing panel for model validation and dynamic selection of the optimal mixing/penalty parameters. The SNP‐weights generated by our models were subsequently applied to AD meta‐analysis summary statistics from Kunkle et al. 2019 (AD Cases = 21,982; Controls = 44,944) and Bellenguez et al. 2022 (AD/ ‘proxy’ AD Cases = 111,326; Controls = 677,663) via S‐PrediXcan to assess whether predicted gene expression is associated with AD risk.ResultWe found on average a 3.3% (SD: 0.05%) significant improvement in the predictive performance for 93.3% of genes models (N = 2,787) when using the same SNPs as the PredictDB model. Using models with the restricted SNP set, we identified four genome‐wide significant gene expression models (CTDNEP, p = 3.2e‐9; STK4, p = 1.6e‐13; RPS14, p = 8.8e‐38; POLR1H, p = 5.6e‐45) using Kunkle et al. 2019 summary statistics relative to only one using the PredictDB model (FBXO46, p = 6.6e‐15). When applying the Bellenguez et al. 2022 summary statistics, which contains a broader AD phenotype thru the inclusion of ‘proxy’ cases, we identified 12 genome‐wide significant gene expression models (Range: CHRNA2, p = 2.6e‐8; SLC24A4, p = 4.5e‐16) associated with AD.ConclusionOur findings show AD association studies based on TWAS approaches may be improved by meta‐analysis reference panels.

An Analytic Framework for the Prediction of Gvl Minor Histocompatibility Antigens to Prevent or Treat Post-Transplant Relapse

The therapeutic effect of allo-HSCT relies on T cell alloreactivity driven by genetic nonidentity between donor and recipient (D-R) pairs. In the setting of HLA-matched transplants, endogenous proteins in recipient cells differing from those of the donor due to genetic polymorphisms, can provide distinct HLA-binding peptides, serving as minor histocompatibility antigens (mHAgs). Hematopoietically-restricted mHAgs represent ideal targets to separate graft-versus-leukemia (GvL) from graft-versus-host-disease (GvHD) effects. To date,however, the quest for optimal GvL mHAgs has resulted in the identification of only a handful of targets, raising concerns on the clinical feasibility of their targeting. We hypothesized that systematic mHAg prediction from whole-exome sequencing (WES) of D-R pairs could help identifying novel candidate mHAgs with an acceptable safety profile for therapeutical targeting. We focused on AML/MDS, the most common indications for allo-HSCT in adults. To capture the transcriptional heterogeneity of malignant myeloid cells, we applied a single-cell based classifier to define genes expressed by leukemic cells in the Beat AML cohort, and then removed those expressed in non-hemopoietic tissues, per the GTEx RNA and protein database. Similarly, a broader set of pan-hematopoietic genes was defined starting from the expression profiles of 18 mature hematopoietic lineages and hematopoietic stem and precursor cells. Through this process, we identified 259 genes with preferential expression in AML and 615 broadly expressed in the hematopoietic compartment. We applied this analytical pipeline to WES from 220 HLA-matched D-R pairs transplanted at DFCI. To discover mHAgs with optimal GvL potential, we focused on the subgroup of 45 patients with long-term survival in the absence of both relapse and GvHD requiring systemic treatment (i.e. GvHD-free/relapse-free survival [GRFS]). For these patients experiencing purely GvL, we analyzed the individual repertoire of predicted GvL mHAgs, and found 86 SNPs recurring in ≥5 GRFS patients. These GvL SNPs were enriched in the subgroup of GRFS patients versus those without GRFS, suggesting that their overrepresentation was not dictated by high allelic frequency in the overall cohort. Using the median number of recurring GvL mHAgs in the GRFS subgroup (‘GRFS mHAgs‘) as a cutoff to stratify the entire study cohort, we found that a higher GRFS mHAg load was associated with a protective effect against relapse ( p=0.009) and conferred a benefit in 2-year overall survival ( p=0.03). Furthermore, HLA class I immunopeptidome analysis of 5 AML cell lines revealed evidence of presentation for 10 epitopes predicted from genes in the overall GvL filter, two of which were also part of the GRFS mHAg set. Given the limited size and lack of ethnic diversity of our discovery cohort (composed mainly of patients of European ancestry), we used genomic data from the 1000 Genomes project to estimate the feasibility of targeting our GRFS mHAg set in a broader population via in silico modeling of allo-HSCT. We simulated 2270 D-R pairs from 844 individuals across 5 populations of disparate ancestry (AFR, EAS, EUR, SAS, and AMR), mirroring a typical URD search in the NMDP registry. To gauge the feasibility of generating personalized mHAg-specific immunotherapy (i.e. cancer vaccines, adoptive cellular therapy) based on the GRFS mHAg set, we estimated that a 10-SNP design (i.e., the number of GRFS SNPs used for correlative analyses in our discovery cohort) would be possible for ~90% of the 1000 Genomes simulated cohort (EUR: 99%, EAS: 72%, SAS: 91%, AFR: 69%, AMR: 74%). Through a similar process, we calculated population coverage simulating a T cell-based approach targeting ≥1 epitope from the GRFS mHAg set. First, we identified 18 HLA alleles that could ensure ~99% coverage across diverse ethnic backgrounds. Second, for each HLA allele, the 3 most frequently predicted (and with highest agretopicity) epitopes from the GRFS SNP set were selected, defining a pool of 54 epitopes. We thus calculated that ≥1 epitopes could be potentially targeted for 81% of the simulated D-R pairs (EUR: 91%, EAS: 78%, SAS: 71%, AFR: 62%, and AMR:85%). Overall, we developed a robust approach to identify novel GvL mHAgs, which could form the basis for systematic personalized immunotherapy following allo-HSCT.

Heterogeneity in convergence behaviour of the single-step SNP-BLUP model across different effects and animal groups

BackgroundThe single-step model is becoming increasingly popular for national genetic evaluations of dairy cattle due to the benefits that it offers such as joint breeding value estimation for genotyped and ungenotyped animals. However, the complexity of the model due to a large number of correlated effects can lead to significant computational challenges, especially in terms of accuracy and efficiency of the preconditioned conjugate gradient method used for the estimation. The aim of this study was to investigate the effect of pedigree depth on the model's overall convergence rate as well as on the convergence of different components of the model, in the context of the single-step single nucleotide polymorphism best linear unbiased prediction (SNP-BLUP) model.ResultsThe results demonstrate that the dataset with a truncated pedigree converged twice as fast as the full dataset. Still, both datasets showed very high Pearson correlations between predicted breeding values. In addition, by comparing the top 50 bulls between the two datasets we found a high correlation between their rankings. We also analysed the specific convergence patterns underlying different animal groups and model effects, which revealed heterogeneity in convergence behaviour. Effects of SNPs converged the fastest while those of genetic groups converged the slowest, which reflects the difference in information content available in the dataset for those effects. Pre-selection criteria for the SNP set based on minor allele frequency had no impact on either the rate or pattern of their convergence. Among different groups of individuals, genotyped animals with phenotype data converged the fastest, while non-genotyped animals without own records required the largest number of iterations.ConclusionsWe conclude that pedigree structure markedly impacts the convergence rate of the optimisation which is more efficient for the truncated than for the full dataset.

Genome-wide analysis of the harbour porpoise (Phocoena phocoena) indicates isolation-by-distance across the North Atlantic and potential local adaptation in adjacent waters

The harbour porpoise (Phocoena phocoena), a highly mobile cetacean species of the Northern Hemisphere, inhabits basins that vary broadly in salinity, temperature, and food availability; such variation can drive divergent adaptation among local populations. To shed light on range-wide population structure and local adaptation, we generated ddRAD sequencing data spanning the entire North Atlantic and the Baltic Sea, as well as the Black Sea as an outgroup, and mapped this data to the high-quality draft genome of the species. We identified 11,978 genome-wide SNPs from 150 individuals, which we used for population genetic inferences. Our results support genetic differentiation between North Atlantic and Baltic Sea populations, with Kattegat as a transition zone. Across the North Atlantic the population differentiation is subtle from west to east, congruent with an isolation-by-distance pattern, but indicates a separation of southern North Sea harbour porpoises. We identified genomic outlier regions, i.e., scaffold regions where SNPs with high FST across North Atlantic populations co-occur. Together with the draft genome annotation, these regions could point towards candidate genes for differential local adaptation processes among populations. Furthermore, they enable the development of a SNP panel for routine population assignment which will be useful in a conservation and management context. We identified six outlier loci putatively under positive selection, based on the population structure inferred from the complete SNP set. Our study highlights the value of genome resources in conservation and management and provides a crucial additional resource for the study of harbour porpoise evolution and phylogeny.

DT-PICS: An Efficient and Cost-Effective SNP Selection Method for the Germplasm Identification of Arabidopsis.

Germplasm identification is essential for plant breeding and conservation. In this study, we developed a new method, DT-PICS, for efficient and cost-effective SNP selection in germplasm identification. The method, based on the decision tree concept, could efficiently select the most informative SNPs for germplasm identification by recursively partitioning the dataset based on their overall high PIC values, instead of considering individual SNP features. This method reduces redundancy in SNP selection and enhances the efficiency and automation of the selection process. DT-PICS demonstrated significant advantages in both the training and testing datasets and exhibited good performance on independent prediction, which validates its effectiveness. Thirteen simplified SNP sets were extracted from 749,636 SNPs in 1135 Arabidopsis varieties resequencing datasets, including a total of 769 DT-PICS SNPs, with an average of 59 SNPs per set. Each simplified SNP set could distinguish between the 1135 Arabidopsis varieties. Simulations demonstrated that using a combination of two simplified SNP sets for identification can effectively increase the fault tolerance in independent validation. In the testing dataset, two potentially mislabeled varieties (ICE169 and Star-8) were identified. For 68 same-named varieties, the identification process achieved 94.97% accuracy and only 30 shared markers on average; for 12 different-named varieties, the germplasm to be tested could be effectively distinguished from 1,134 other varieties while grouping extremely similar varieties (Col-0) together, reflecting their actual genetic relatedness. The results suggest that the DT-PICS provides an efficient and accurate approach to SNP selection in germplasm identification and management, offering strong support for future plant breeding and conservation efforts.

Preliminary Investigation of Essentially Derived Variety of Tea Tree and Development of SNP Markers.

The continuous emergence of Essentially Derived Varieties (EDVs) in the process of tea tree breeding will endanger and affect the innovation ability and development potential of tea tree breeding. In this study, genotyping by sequencing (GBS) technology was used to screen high-quality genomic SNPs for the first time to investigate the derived relationships of 349 tea trees from 12 provinces in China. A total of 973 SNPs uniformly covering 15 tea tree chromosomes with high discrimination capacity were screened as the core SNP set. A genetic similarity analysis showed that 136 pairs of tea trees had a genetic similarity coefficient (GS) > 90%, among which 60 varieties/strains were identified as EDVs, including 22 registered varieties (19 were indisputably EDVs). Furthermore, 21 SNPs with 100% identification of 349 tea trees were selected as rapid identification markers, of which 14 SNP markers could be used for 100% identification of non-EDV. These results provide the basis for the analysis of the genetic background of tea trees in molecular-assisted breeding.

Sponge diversification in marine lakes: Implications for phylogeography and population genomic studies on sponges.

The relative influence of geography, currents, and environment on gene flow within sessile marine species remains an open question. Detecting subtle genetic differentiation at small scales is challenging in benthic populations due to large effective population sizes, general lack of resolution in genetic markers, and because barriers to dispersal often remain elusive. Marine lakes can circumvent confounding factors by providing discrete and replicated ecosystems. Using high-resolution double digest restriction-site-associated DNA sequencing (4826 Single Nucleotide Polymorphisms, SNPs), we genotyped populations of the sponge Suberites diversicolor (n = 125) to test the relative importance of spatial scales (1-1400 km), local environmental conditions, and permeability of seascape barriers in shaping population genomic structure. With the SNP dataset, we show strong intralineage population structure, even at scales <10 km (average F ST = 0.63), which was not detected previously using single markers. Most variation was explained by differentiation between populations (AMOVA: 48.8%) with signatures of population size declines and bottlenecks per lake. Although the populations were strongly structured, we did not detect significant effects of geographic distance, local environments, or degree of connection to the sea on population structure, suggesting mechanisms such as founder events with subsequent priority effects may be at play. We show that the inclusion of morphologically cryptic lineages that can be detected with the COI marker can reduce the obtained SNP set by around 90%. Future work on sponge genomics should confirm that only one lineage is included. Our results call for a reassessment of poorly dispersing benthic organisms that were previously assumed to be highly connected based on low-resolution markers.

Relationship of the gene pool of the Khants with the peoples of Western Siberia, Cis-Urals and the Altai-Sayan Region according to the data on the polymorphism of autosomic locus and the Y-chromosome.

Khanty are indigenous Siberian people living on the territory of Western Siberia, mainly on the territory of the Khanty-Mansiysk and Yamalo-Nenets Autonomous Okrugs. The present study is aimed at a comprehensive analysis of the structure of the Khanty gene pool and their comparison with other populations of the indigenous population of Southern and Western Siberia. To address the issues of genetic proximity of the Khanty with other indigenous peoples, we performed genotyping of a wide genomic set of autosomal markers using high-density biochips, as well as an expanded set of SNP and STR markers of the Y-chromosome in various ethnic groups: Khakas, Tuvans, Southern Altaians, Siberian Tatars, Chulyms (Turkic language family) and Kets (Yeniseian language family). The structure of the gene pool of the Khanty and other West Siberian and South Siberian populations was studied using a genome-wide panel of autosomal single nucleotide polymorphic markers and Y-chromosome markers. The results of the analysis of autosomal SNPs frequencies by various methods, the similarities in the composition of the Y-chromosome haplogroups and YSTR haplotypes indicate that the Khanty gene pool is quite specific. When analyzing autosomal SNPs, the Ugrian genetic component completely dominates in both samples (up to 99-100 %). The samples of the Khanty showed the maximum match in IBD blocks with each other, with a sample of the Kets, Chulyms, Tuvans, Tomsk Tatars, Khakas, Kachins, and Southern Altaians. The degree of coincidence of IBD blocks between the Khanty, Kets, and Tomsk Tatars is consistent with the results of the distribution of allele frequencies and common genetic components in these populations. According to the composition of the Y-chromosome haplogroups, the two samples of the Khanty differ significantly from each other. A detailed phylogenetic analysis of various Y-chromosome haplogroups made it possible to describe and clarify the differences in the phylogeny and structure of individual ethnospecific sublines, to determine their relationship, traces of population expansion in the Khanty gene pool. Variants of different haplogroups of the Y-chromosome in the Khanty, Khakas and Tuvans go back to their common ancestral lines. The results of a comparative analysis of male samples indicate a close genetic relationship between the Khanty and Nenets, Komi, Udmurts and Kets. The specificity of haplotypes, the discovery of various terminal SNPs confirms that the Khanty did not come into contact with other ethnic groups for a long time, except for the Nenets, which included many Khanty clans.

Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods

Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30–45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value < 0.01) lipidome-genotype relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes. Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs and participants, thus representing most distinct subgroups. We identified 30 significantly enriched biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome subgroups through which the identified genetic variants can influence and regulate plasma lipid related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the studied Finnish population that may have distinct disease trajectories and therefore could be useful in precision medicine research.

P097 Various regulatory single nucleotide polymorphisms regulate similar biological pathways in Crohn’s disease

Abstract Background Single nucleotide polymorphisms (SNP) associated with Crohn’s disease (CD) are in both protein-coding and non-coding regions of the genome. Interpreting the effect of non-coding regulatory SNPs is challenging. We previously developed the iSNP pipeline, which determines how SNPs in transcription factor (TF) binding sites and miRNA target sites affect cellular regulatory networks and contribute to disease pathogenesis. We successfully used this method in Ulcerative Colitis, and now we developed this method further to study CD. Methods We applied the iSNP pipeline to identify CD-associated SNP affected genes in 1695 genotyped CD patients. The original iSNP approach can only analyse the direct interactors of SNP affected genes. Here, we implemented a heat propagation algorithm to uncover further downstream pathways, TFs and regulatory processes involved in the pathogenesis of CD. The heat was propagated from the proteins encoded by the SNP-affected proteins through a signalling network (using OmniPath) to TFs, and from them to their target genes (using DoRothEA). Results From the disease causing SNPs on the immunochip data of the patients, we identified 47 regulatory SNPs out of which 39 SNPs were in TF binding sites, 5 were affecting miRNA-target sites and 3 were affecting both. The pipeline predicted these SNPs regulate the expression of 83 proteins; 47 of them were present in the OmniPath signalling network. The network propagation analysis identified 518 proteins that were affected significantly in CD by these 47 proteins. The affected proteins were involved in antigen presentation through MHC-I, WNT signalling, C-type lectin signalling and NF-kB signalling pathways based on Gene Ontology analysis. GNAI1, RIPK2, ATG16L1, CLTC, and CARD9 were affected in 63% of the patients. TF-target gene analysis identified a downstream regulatory network containing 2067 potentially affected genes. These genes were involved in T cell activation, other immune functions, DNA repair and autophagy regulation. Genes encoding MMP9, RARA and SSB2 were affected in 70% of patients. Conclusion The affected signalling network showed the known importance of the WNT pathway, autophagy and type I antigen presentation in CD. We extended the list of affected processes with the network propagation modelling approach, and pointed out key functions that were commonly affected in the majority of patients with CD. Thus, patient-specific, various SNP sets converge on the same, few key functions, such as fibrosis and tissue remodelling (through MMP9 or RARA), and DNA repair (through SSB2). Overall, the new network propagation model in the iSNP pipeline pointed out genes affected in the majority of CD patients, highlighting their potential role in CD pathogenesis.

Development of an Okinawa panel for biogeographic inference of Okinawans

Background The Precision ID Ancestry Panel with 165 SNP markers was unable to differentiate between mainland Japanese and Okinawa Japanese or to distinguish either of them from other East Asian populations. Aim An Okinawa panel was developed with the aim of further separating Okinawa Japanese individuals from mainland Japanese and other Asian groups. Seventy-five SNPs were selected using the most informative markers from the literature. Further, 22 SNPs were selected to separate Okinawa Japanese at minimum SNPs. Subjects and methods Samples were collected from 48 unrelated individuals from mainland Japan and 46 unrelated residents of the Okinawa prefecture. Data were evaluated by STRUCTURE, principal component, and GenoGeographer analyses. Results The 22 SNP set had similar levels of differentiation in STRUCTURE and PCA analyses as the 75 SNP set. GenoGeographer analysis showed that, out of the 46 Okinawa Japanese individuals, the 75 SNP and 22 SNP sets correctly assigned the Okinawan population as the most likely population of origin for 32 and 31 individuals, respectively. Conclusion Neither SNP set could completely differentiate between Okinawa Japanese and other Asian groups, however, these sets should be useful for crime investigation, when the sample, cost and time are limited.