Set Of Single Nucleotide Polymorphisms Research Articles

Genetic markers associated with divergent selection against the parasite Marteilia cochillia in common cockle (Cerastoderma edule) using transcriptomics and population genomics data

The common cockle (Cerastoderma edule) plays an important role in marine ecosystems and represents a valuable socioeconomic resource for coastal communities. In 2012, the cockle beds from Rı́a de Arousa (Galicia, NW Spain) were seriously decimated by the protozoan Marteilia cochillia responsible for marteiliosis. We aimed to identify single nucleotide polymorphisms (SNP) markers potentially associated with resilience to marteiliosis to be used in marker-assisted selection programs for restoring affected cockle beds and recovering their production. For this, we carried out a population genomics approach using 2b-RADseq, where 38 naive samples (before the first detection of M. cochillia in 2012) from two beds of Rı́a de Arousa were compared with 39 affected samples collected in 2018/2019 (after several years of marteiliosis occurring in the area), collected either before (15 non-exposed samples) or during (24 exposed samples) the marteiliosis outbreak. Additionally, 767 differentially expressed genes (DEG) from a previous transcriptomic study addressed during the aforementioned 2018/19 marteiliosis outbreak, were evaluated to identify SNPs showing signals of selection. Using 2b-RADseq, 9,154 SNPs were genotyped and among them, 110 consistent outliers for divergent selection were identified. This set of SNPs was able to discriminate the samples according to their marteiliosis status (naive vs affected; exposed vs non-exposed), while another 123 SNPs were identified linked to DEGs associated with the level of infection across a temporal series. Finally, combining the population genomics and transcriptomics information, we selected the 60 most reliable SNPs associated with marteiliosis resilience. These SNPs were close to or within DEGs, and many of them were related to immune response (phagocytosis and cell adhesion), defence, such as apoptosis, stress, and cellular cycle, among other functions. This set of SNPs will eventually be validated to develop a cost-effective genotyping tool for their application for obtaining cockle-resilient strains for marteiliosis.

SNP panel for evaluation of genetic variability and relationship in roe deer (Capreolus capreolus).

Blood samples from forty-six roe deer ( Capreolus capreolus) acquired during officially approved hunting in six hunting divisions throughout Poland were used to isolate the genomic DNA. All individuals were genotyped by MD_Bovine BeadChip (Illumina) for 46.750 Single Nucleotide Polymorphism (SNP) markers. SNPs of inappropriate clusters, with a marker call rate lower than 90% and with a minor allele frequency (MAF) lower than 0.01, located on sex chromosomes and mitochondrial DNA, were removed. Altogether, 21.033 SNP markers were included for further analysis. Observed and expected heterozygosity amounted to 0.098 and 0.119, respectively. Among 21.033 markers, a panel of 148 SNPs were selected for relationship analysis. They were unlinked and had a MAF higher than 0.2. This set of SNPs showed a probability of parentage exclusion of 1.29x10 -6 and 2.37x10 -19 for one, and two known parents, respectively. The probability of identity was estimated at 1.8x10 -40. The probabilities obtained in this study are sufficient for the monitoring and effective management of the genetic diversity of roe deer in Poland and is a cost-effective complementary tool for forensic applications.

SNPs Sets in Codifying Genes for Xenobiotics-Processing Enzymes Are Associated with COPD Secondary to Biomass-Burning Smoke.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide; the main risk factors associated with the suffering are tobacco smoking (TS) and chronic exposure to biomass-burning smoke (BBS). Different biological pathways have been associated with COPD, especially xenobiotic or drug metabolism enzymes. This research aims to identify single nucleotide polymorphisms (SNPs) profiles associated with COPD from two expositional sources: tobacco smoking and BBS. One thousand-five hundred Mexican mestizo subjects were included in the study and divided into those exposed to biomass-burning smoke and smokers. Genome-wide exome genotyping was carried out using Infinium Exome-24 kit arrays v. 1.2. Data quality control was conducted using PLINK 1.07. For clinical and demographic data analysis, Rstudio was used. Eight SNPs were found associated with COPD secondary to TS and seven SNPs were conserved when data were analyzed by genotype. When haplotype analyses were carried out, five blocks were predicted. In COPD secondary to BBS, 24 SNPs in MGST3 and CYP family genes were associated. Seven blocks of haplotypes were associated with COPD-BBS. SNPs in the ARNT2 and CYP46A1 genes are associated with COPD secondary to TS, while in the BBS comparison, SNPs in CYP2C8, CYP2C9, MGST3, and MGST1 genes were associated with increased COPD risk.

From Wisent to the Lab and Back Again—A Complex SNP Set for Population Management as an Effective Tool in European Bison Conservation

Proper management and genetic monitoring of the modern European bison (Bison bonasus) population is one of the most important responsibilities for this species’ conservation. Up-to-date, complex genetic analysis performed using a consistent molecular method is needed for population management as a tool to further validate and maintain the genetic diversity of the species. The identification of the genetic line when pedigree data are missing, as well as the identification of parentage and individuals, are crucial for this purpose. The aim of our research was to create a small but informative panel of SNP (single-nucleotide polymorphism) markers that can be used for routine genotyping of the European bison at low cost. In our study, we used a custom-designed microarray to genotype a large number of European bison, totaling 455 samples from two genetic lines. The results of this analysis allowed us to select highly informative markers. In this paper, we present an effective single nucleotide polymorphism set, divided into separate panels to perform genetic analyses of European bison, which is needed for population monitoring and management. We proposed a total of 20 SNPs to detect hybridization with Bos taurus and Bison bison, a panel of 50 SNPs for individuals and parentage identification, as well as a panel of 30 SNPs for assessing membership of the genetic line. These panels can be used together or independently depending on the research goal and can be applied using various methods.

IPP: An Intelligent Privacy-Preserving Scheme for Detecting Interactions in Genome Association Studies.

Analyzing K-order Single Nucleotide Polymorphism (SNP) interactions through the statistics of Genome-Wide Association Studies (GWAS) is crucial for discovering pathogenic causes of human complex diseases and controlling risk genetic variants of diverse disorders. We propose a method based on Ant Colony Optimization (ACO) algorithm to detect gene interactions for GWAS - an Intelligent Privacy-Preserving scheme (IPP). Initially, we design a multi-objective search algorithm to discover the candidate SNP sets related to disease phenotype, which utilizes Differential Privacy (DP) method by disturbing the multi-objective function to construct a rational epistatic privacy protection strategy. Furthermore, the global path selection strategy composed of two probabilistic methods is proposed to reduce the probability of falling into the local optimum. We use simulated models and a real dataset of Rheumatoid Arthritis (RA) to compare IPP with four popular methods to detect K-order SNPs, the experimental results show that IPP can guarantee the search accuracy effectively and enhance the detecting ability of various models. Further, the privacy budget experiments indicate that the range of privacy budget in IPP is reasonable and make the framework more stable.

Disease patterns of coronary heart disease and type 2 diabetes harbored distinct and shared genetic architecture

Coronary heart disease (CHD) and type 2 diabetes (T2D) are two complex diseases with complex interrelationships. However, the genetic architecture of the two diseases is often studied independently by the individual single-nucleotide polymorphism (SNP) approach. Here, we presented a genotypic-phenotypic framework for deciphering the genetic architecture underlying the disease patterns of CHD and T2D. A data-driven SNP-set approach was performed in a genome-wide association study consisting of subpopulations with different disease patterns of CHD and T2D (comorbidity, CHD without T2D, T2D without CHD and all none). We applied nonsmooth nonnegative matrix factorization (nsNMF) clustering to generate SNP sets interacting the information of SNP and subject. Relationships between SNP sets and phenotype sets harboring different disease patterns were then assessed, and we further co-clustered the SNP sets into a genetic network to topologically elucidate the genetic architecture composed of SNP sets. We identified 23 non-identical SNP sets with significant association with CHD or T2D (SNP-set based association test, P < 3.70 × [Formula: see text]). Among them, disease patterns involving CHD and T2D were related to distinct SNP sets (Hypergeometric test, P < 2.17 × [Formula: see text]). Accordingly, numerous genes (e.g., KLKs, GRM8, SHANK2) and pathways (e.g., fatty acid metabolism) were diversely implicated in different subtypes and related pathophysiological processes. Finally, we showed that the genetic architecture for disease patterns of CHD and T2D was composed of disjoint genetic networks (heterogeneity), with common genes contributing to it (pleiotropy). The SNP-set approach deciphered the complexity of both genotype and phenotype as well as their complex relationships. Different disease patterns of CHD and T2D share distinct genetic architectures, for which lipid metabolism related to fibrosis may be an atherogenic pathway that is specifically activated by diabetes. Our findings provide new insights for exploring new biological pathways.

Development of molecular inversion probes for soybean progeny genomic selection genotyping.

Increasing rate of genetic gain for key agronomic traits through genomic selection requires the development of new molecular methods to run genome-wide single-nucleotide polymorphisms (SNPs). The main limitation of current methods is the cost is too high to screen breeding populations. Molecular inversion probes (MIPs) are a targeted genotyping-by-sequencing (GBS) method that could be used for soybean [Glycine max (L.) Merr.] that is both cost-effective, high-throughput, and provides high data quality to screen breeder's germplasm for genomic selection. A 1K MIP SNP set was developed for soybean with uniformly distributed markers across the genome. The SNPs were selected to maximize the number of informative markers in germplasm being tested in soybean breeding programs located in the northern-central and middle-southern regions of the United States. The 1K SNP MIP set was tested on diverse germplasm and a recombinant inbred line (RIL) population. Targeted sequencing with MIPs obtained an 85% enrichment for the targeted SNPs. The MIP genotyping accuracy was 93% overall, whereas homozygous call accuracy was 98% with <10% missing data. The accuracy of MIPs combined with its low per-sample cost makes it a powerful tool to enable genomic selection within soybean breeding programs.

Investigation of the causal relationship between ALS and autoimmune disorders: a Mendelian randomization study

BackgroundEpidemiological studies have reported an association between amyotrophic lateral sclerosis (ALS) and different autoimmune disorders. This study aims to explore the causal relationship between autoimmune disorders and ALS using Mendelian randomization (MR).MethodsTo test the genetically predicted effects of liability towards immune-related outcomes on ALS risk, we used summary statistics from the largest European genome-wide association studies (GWAS) for these disorders in a two-sample MR setting. To do this, we extracted single nucleotide polymorphisms (SNPs) from the GWAS, which strongly associated with the 12 traits, and queried their effects in a large European ALS GWAS (27,265 cases and 110,881 controls). To avoid bias in our MR instruments related to the complex linkage disequilibrium structure of the human leukocyte antigen (HLA) region, we excluded SNPs within this region from the analyses. We computed inverse-variance weighted (IVW) MR estimates and undertook sensitivity analyses using MR methods robust to horizontal pleiotropy. We also performed a reverse MR analysis testing the causal effects of ALS on the above autoimmune traits.ResultsAfter applying Bonferroni correction for multiple testing, our MR analyses showed that the liability to autoimmune disorders does not affect ALS risk. Our reverse MR analysis also did not support the effects of liability to ALS on other autoimmune disorders. The results of the main IVW MR analyses were generally supported by our sensitivity MR analyses. The variance in the exposures explained by the sets of SNPs used as MR instruments ranged from 8.1 × 10−4 to 0.31. Our MR study was well-powered to detect effects as small as an odds ratio (OR) of 1.045 for ALS in the main MR and as small as an OR of 1.32 in the reverse MR.ConclusionOur MR study does not support a relationship between liability to autoimmune disorders and ALS risk in the European population. The associations observed in epidemiological studies could be partly attributed to shared biology or environmental confounders.

Three assays for in-solution enrichment of ancient human DNA at more than a million SNPs.

The strategy of in-solution enrichment for hundreds of thousands of single-nucleotide polymorphisms (SNPs) has been used to analyze >70% of individuals with genome-scale ancient DNA published to date. This approach makes it economical to study ancient samples with low proportions of human DNA and increases the rate of conversion of sampled remains into interpretable data. So far, nearly all such data have been generated using a set of bait sequences targeting about 1.24 million SNPs (the "1240k reagent"), but synthesis of the reagent has been cost-effective for only a few laboratories. In 2021, two companies, Daicel Arbor Biosciences and Twist Bioscience, made available assays that target the same core set of SNPs along with supplementary content. We test all three assays on a common set of 27 ancient DNA libraries and show that all three are effective at enriching many hundreds of thousands of SNPs. For all assays, one round of enrichment produces data that are as useful as two. In our testing, the "Twist Ancient DNA" assay produces the highest coverages, greatest uniformity on targeted positions, and almost no bias toward enriching one allele more than another relative to shotgun sequencing. We also identify hundreds of thousands of targeted SNPs for which there is minimal allelic bias when comparing 1240k data to either shotgun or Twist data. This facilitates coanalysis of the large data sets that have been generated using 1240k and Twist capture, as well as shotgun sequencing approaches.

PSIII-4 Late-Breaking: Genetic Marker Association Study of Heifer Pregnancy in Red Angus Cattle

Abstract Heifer pregnancy (HPG) is lowly heritable, yet economically relevant in beef cattle production systems. Hence, identifying single nucleotide polymorphisms (SNP) associated with phenotype could increase the amount of information used in the genetic evaluation of the trait. Using RNA-sequence data from lactating Holstein cows, a set of SNP (n = 69) were discovered among embryo mortality conceptuses (Conceptus-SNP) associated with maternal recognition of pregnancy on day 16 after artificial insemination (AI). To further study these SNP, our objective was to conduct analyses to identify markers associated with HPG in Red Angus. Phenotypic records and genotypes were provided by the Red Angus Association of America (RAAA). For phenotypes, a total of 59,387 cattle records, from the June 2022 RAAA genetic evaluation, were used in the analysis. For genotype data, a 47,860 SNP panel (excluding SNP on the X-chromosome) were formed by combining the historical RAAA genotype data (96,162 animals with genotypes) and genotypes from whole genome sequence of 30 prominent Red Angus AI sires. Markers were mapped to the ARS-UCD1.2 bovine reference genome and then imputed to 47,860 SNP panel using findhap software. A subset of SNP (n = 2,169 SNP; Conceptus-SNP and the multi-breed selected reduced panel [MSRP]) were extracted from the imputed genotypes file. Using PLINK software, genotype quality control procedure was performed so that animals with call rate &amp;lt; 0.9 were removed. A marker was removed if: it had minor allele frequency &amp;lt; 0.01, not in Hardy–Weinberg equilibrium (P &amp;lt; 0.00001), or in high linkage disequilibrium (r2 &amp;gt; 0.95). The final genotype file contained 96,667 animals with 2,137 SNP. A Bayes Cπ single-step super hybrid model (π = 0.99), using the BOLT software package, was used to estimate marker’s posterior probability of inclusion (PPI), the marker significance criterion in predicting HPG. A marker’s PPI was calculated as the average probability from four different Gibbs sampling chains of 75,000 iterations that included 5,000 iterations as burn-in. Markers rs42576394 (18:60583784) and rs42942055 (14:34249373) had PPI of 25.5 and 7.1%, respectively; suggesting association with HPG. None of the Conceptus-SNP had a detectable association with HPG; however, these Conceptus-SNP may be much more informative for genotype to phenotype association analyses for the trait of stayability. Such a trait would be measured with lactating mature beef cows and potentially more similar to the lactating Holstein cows from which the markers were discovered.

Genome-Wide Association Study Identifies Genetic Variants Associated with Rotator Cuff Tear-A Pilot Study.

A rotator cuff is a muscle and tendon surrounding the shoulder joint, and a rotator cuff tear can be caused by overuse or injury, which leads to great pain in affected individuals. However, rotator cuff tear is a multifactorial process whose underlying mechanism is still unclear. Many previous studies have suggested an important role of genetic predisposition, such as single-nucleotide polymorphisms (SNPs), in explaining the genesis of tendinopathy. This study aimed to identify specific genes or genetic variants associated with rotator cuff tears by performing a genome-wide association study (GWAS) using an independent case of rotator cuff tears. GWAS was performed using data from CHA Bundang Medical Center with 20 cases of rotator cuff tears, and 20 cases of healthy controls genotyped on the Illumina HiSeq 2500. Tests of association were performed using the Burrows–Wheeler Aligner (BWA) software at 284,246 SNPs. Data were filtered based on sequence ontology, minor allele frequency, and Hardy–Weinberg equilibrium values, and SNPs were considered significant if the p-value was <0.05. The tests of association revealed more than 20 significantly associated SNPs. SNPs showing the highest significance occurred in candidate genes, including LAIR2 (rs2287828, OR 9.116, p-value 5.49 × 10−4) on chromosome 19 and CRIPAK (rs9328733, OR 6, p-value 1.11 × 10−3) and REST (rs2228991, OR 8.222, p-value 1.20 × 10−3) on chromosome 4. This study attempted to identify genetic variants influencing rotator cuff tears through a genome-wide association study using a dense set of SNPs. More than 20 SNPs were significantly associated with rotator cuff tears. The major limitation of this study is that it was conducted on a small study group and requires further validation. Nevertheless, the identification of potential genetic variants related to rotator cuff injury would aid in the early detection of individuals at risk for the development of tendinopathy and will provide insight into future gene therapies.

Utility of multi-omics data to inform genomic prediction of heifer fertility traits.

Biologically informed single nucleotide polymorphisms (SNPs) impact genomic prediction accuracy of the target traits. Our previous genomics, proteomics, and transcriptomics work identified candidate genes related to puberty and fertility in Brahman heifers. We aimed to test this biological information for capturing heritability and predicting heifer fertility traits in another breed i.e., Tropical Composite. The SNP from the identified genes including 10 kilobases (kb) region on either side were selected as biologically informed SNP set. The SNP from the rest of the Bos taurus genes including 10-kb region on either side were selected as biologically uninformed SNP set. Bovine high-density (HD) complete SNP set (628,323 SNP) was used as a control. Two populations-Tropical Composites (N = 1331) and Brahman (N = 2310)-had records for three traits: pregnancy after first mating season (PREG1, binary), first conception score (FCS, score 1 to 3), and rebreeding score (REB, score 1 to 3.5). Using the best linear unbiased prediction method, effectiveness of each SNP set to predict the traits was tested in two scenarios: a 5-fold cross-validation within Tropical Composites using biological information from Brahman studies, and application of prediction equations from one breed to the other. The accuracy of prediction was calculated as the correlation between genomic estimated breeding values and adjusted phenotypes. Results show that biologically informed SNP set estimated heritabilities not significantly better than the control HD complete SNP set in Tropical Composites; however, it captured all the observed genetic variance in PREG1 and FCS when modeled together with the biologically uninformed SNP set. In 5-fold cross-validation within Tropical Composites, the biologically informed SNP set performed marginally better (statistically insignificant) in terms of prediction accuracies (PREG1: 0.20, FCS: 0.13, and REB: 0.12) as compared to HD complete SNP set (PREG1: 0.17, FCS: 0.10, and REB: 0.11), and biologically uninformed SNP set (PREG1: 0.16, FCS: 0.10, and REB: 0.11). Across-breed use of prediction equations still remained a challenge: accuracies by all SNP sets dropped to around zero for all traits. The performance of biologically informed SNP was not significantly better than other sets in Tropical Composites. However, results indicate that biological information obtained from Brahman was successful to predict the fertility traits in Tropical Composite population.

Inflammatory bowel disease and risk of coronary heart disease : AMendelian randomization study.

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), has been reported to be associated with an increased risk of coronary heart disease (CHD); however, the causal link between IBD and CHD is unclear. We performed Mendelian randomization (MR) analysis to investigate the association between genetically predicted IBD and CHD risk. Exposure summary data were obtained from genome-wide association studies (GWAS) with cohorts of IBD (12,882cases and 21,770 controls), UC (6968cases and 20,464 controls), and CD (5956cases and 14,927 controls) of European descent to identify single nucleotide polymorphisms (SNPs) as instrumental variables. Outcome summary data were obtained from ameta-analysis of 22GWAS including 22,233cases and 64,762 controls of European descent. To estimate MR, four methods were used, including inverse variance-weighted (IVW), MR-Egger, simple mode, and weighted median methods. Sensitivity analysis was also performed. The Bonferroni method was used to correct the bias of multiple testing. Three sets of SNPs (69SNPs of IBD, 40SNPs of UC, and 58SNPs of CD) were used to estimate the causal effect between genetically predicted IBD and CHD. Using the IVW method, we found that no causal relationship between genetically predicted IBD and CHD after Bonferroni correction, and there was no causal relationship between UC/CD and the development of CHD. No evidence of significant heterogeneity and pleiotropy was found. The results of this study suggested that genetically predicted IBD may have no causal effect on CHD risk in apopulation with European ancestry.

Species-informative SNP markers for characterising freshwater prawns of genus Macrobrachium in Cameroon.

Single Nucleotide Polymorphisms (SNPs) are now popular for a myriad of applications in animal and plant species including, ancestry assignment, conservation genetics, breeding, and traceability of animal products. The objective of this study was to develop a customized cost-effective SNP panel for genetic characterisation of Macrobrachium species in Cameroon. The SNPs identified in a previous characterization study were screened as viable candidates for the reduced panel. Starting from a full set of 1,814 SNPs, a total of 72 core SNPs were chosen using conventional approaches: allele frequency differentials, minor allele frequency profiles, and Wright’s Fst statistics. The discriminatory power of reduced set of informative SNPs were then tested using the admixture analysis, principal component analysis, and discriminant analysis of principal components. The panel of prioritised SNP markers (i.e., N = 72 SNPs) distinguished Macrobrachium species with 100% accuracy. However, large sample size is needed to identify more informative SNPs for discriminating genetically closely related species, including M. macrobrachion versus M. vollenhovenii and M. sollaudii versus M. dux. Overall, the findings in this study show that we can accurately characterise Macrobrachium using a small set of core SNPs which could be useful for this economically important species in Cameroon. Given the results obtained in this study, a larger independent validation sample set will be needed to confirm the discriminative capacity of this SNP panel for wider commercial and research applications.

Genomic Selection in Chinese Holsteins Using Regularized Regression Models for Feature Selection of Whole Genome Sequencing Data.

Simple SummaryGenomic selection (GS) is increasingly widely used in animal breeding, owing to its high efficiency in the genetic improvement of economic traits. In China, GS has been implemented for genetic evaluation of young bulls in dairy cattle breeding programs since 2012. GS is commonly based on single nucleotide polymorphism (SNP) chips. The cost of whole genome sequencing (WGS) has decreased tremendously in recent years, allowing increased studies of WGS-based GS. In this study, based on the imputed WGS data of approximately 8000 Chinese Holsteins, we investigated the performance of GS of milk production traits using the feature selection method of regularized regression. The results showed that WGS-based GS using regularized regression models and the commonly used linear mixed models achieved comparable prediction accuracies. For milk and protein yields, GS using a combination of SNPs selected with a regularized regression model and 50K SNP chip data achieved the best prediction performance, and GS using SNPs selected with a linear mixed model combined with 50K SNP chip data performed best for fat yield. The proposed method of GS based on WGS data, i.e., feature selection using regularization regression models, provides a valuable novel strategy for genomic selection.Genomic selection (GS) is an efficient method to improve genetically economic traits. Feature selection is an important method for GS based on whole-genome sequencing (WGS) data. We investigated the prediction performance of GS of milk production traits using imputed WGS data on 7957 Chinese Holsteins. We used two regularized regression models, least absolute shrinkage and selection operator (LASSO) and elastic net (EN) for feature selection. For comparison, we performed genome-wide association studies based on a linear mixed model (LMM), and the N single nucleotide polymorphisms (SNPs) with the lowest p-values were selected (LMMLASSO and LMMEN), where N was the number of non-zero effect SNPs selected by LASSO or EN. GS was conducted using a genomic best linear unbiased prediction (GBLUP) model and several sets of SNPs: (1) selected WGS SNPs; (2) 50K SNP chip data; (3) WGS data; and (4) a combined set of selected WGS SNPs and 50K SNP chip data. The results showed that the prediction accuracies of GS with features selected using LASSO or EN were comparable to those using features selected with LMMLASSO or LMMEN. For milk and protein yields, GS using a combination of SNPs selected with LASSO and 50K SNP chip data achieved the best prediction performance, and GS using SNPs selected with LMMLASSO combined with 50K SNP chip data performed best for fat yield. The proposed method, feature selection using regularization regression models, provides a valuable novel strategy for WGS-based GS.

Human genotype-to-phenotype predictions: Boosting accuracy with nonlinear models.

Genotype-to-phenotype prediction is a central problem of human genetics. In recent years, it has become possible to construct complex predictive models for phenotypes, thanks to the availability of large genome data sets as well as efficient and scalable machine learning tools. In this paper, we make a threefold contribution to this problem. First, we ask if state-of-the-art nonlinear predictive models, such as boosted decision trees, can be more efficient for phenotype prediction than conventional linear models. We find that this is indeed the case if model features include a sufficiently rich set of covariates, but probably not otherwise. Second, we ask if the conventional selection of single nucleotide polymorphisms (SNPs) by genome wide association studies (GWAS) can be replaced by a more efficient procedure, taking into account information in previously selected SNPs. We propose such a procedure, based on a sequential feature importance estimation with decision trees, and show that this approach indeed produced informative SNP sets that are much more compact than when selected with GWAS. Finally, we show that the highest prediction accuracy can ultimately be achieved by ensembling individual linear and nonlinear models. To the best of our knowledge, for some of the phenotypes that we consider (asthma, hypothyroidism), our results are a new state-of-the-art.

Genome-Wide Association Study of Parasite Resistance to Gastrointestinal Nematodes in Corriedale Sheep.

Selection of genetically resistant animals is one alternative to reduce the negative impact of gastrointestinal nematodes (GIN) on sheep production. The aim of this study was to identify genomic regions associated with GIN resistance in Corriedale sheep by single-step genome-wide association studies (ssGWAS) using 170, 507 and 50K single nucleotide polymorphisms (SNPs). Analysis included 19,547 lambs with faecal egg counts (FEC) records, a pedigree file of 40,056 animals and 454, 711 and 383 genotypes from 170, 507 and 50K SNPs, respectively. Genomic estimated breeding values (GEBV) were obtained with single-step genomic BLUP methodology (ssGBLUP), using a univariate animal model, which included contemporary group, type of birth and age of dam as class fixed effects and age at FEC recording as covariate. The SNP effects as wells as p-values were estimated with POSTGSF90 program. Significance level was defined by a chromosome-wise False Discovery Rate of 5%. Significant genomic regions were identified in chromosomes 1, 3, 12 and 19 with the 170 SNP set, in chromosomes 7, 12 and 24 using the 507 SNP chip and only in chromosome 7 with the 50K SNP chip. Candidate genes located in these regions, using Oar_v4.0 as reference genome, were TIMP3, TLR5, LEPR and TLR9 (170 SNPs), SYNDIG1L and MGRN1 (507 SNP chip) and INO80, TLN2, TSHR and EIF2AK4 (50K SNP chip). These results validate genomic regions associated with FEC previously identified in Corriedale and other breeds and report new candidate regions for further investigation.

Fast and accurate kinship estimation using sparse SNPs in relatively large database searches

Forensic genetic genealogy (FGG) has primarily relied upon dense single nucleotide polymorphism (SNP) profiles from forensic samples or unidentified human remains queried against online genealogy database(s) of known profiles generated with SNP microarrays or from whole genome sequencing (WGS). In these queries, SNPs are compared to database samples by locating contiguous stretches of shared SNP alleles that allow for detection of genomic segments that are identical by descent (IBD) among biological relatives (kinship). This segment-based approach, while robust for detecting distant relationships, generally requires DNA quantity and/or quality that are sometimes not available in forensic casework samples. By focusing on SNPs with maximal discriminatory power and using an algorithm designed for a sparser SNP set than those from microarray typing, performance similar to segment matching was reached even in difficult casework samples. This algorithm locates shared segments using kinship coefficients in “windows” across the genome. The windowed kinship algorithm is a modification of the PC-AiR and PC-Relate tools for genetic relatedness inference, referred to here as the “whole genome kinship” approach, that control for the presence of unknown or unspecified population substructure. Simulated and empirical data in this study, using DNA profiles comprised of 10,230 SNPs (10K multiplex) targeted by the ForenSeq™ Kintelligence Kit demonstrate that the windowed kinship approach performs comparably to segment matching for identifying first, second and third degree relationships, reasonably well for fourth degree relationships, and with fewer false kinship associations. Selection criteria for the 10K SNP PCR-based multiplex and functionality of the windowed kinship algorithm are described.

Mining High-Level Imaging Genetic Associations via Clustering AD Candidate Variants with Similar Brain Association Patterns.

Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer’s disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP–SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.

A powerful global test for spliceQTL effects.

Statistical methods to test for effects of single nucleotide polymorphisms (SNPs) on exon inclusion exist but often rely on testing of associations between multiple exon-SNP pairs, with sometimes subsequent summarization of results at the gene level. Such approaches require heavy multiple testing corrections and detect mostly events with large effect sizes. We propose here a test to find spliceQTL (splicing quantitative trait loci) effects that takes all exons and all SNPs into account simultaneously. For any chosen gene, this score-based test looks for an association between the set of exon expressions and the set of SNPs, via a random-effects model framework. It is efficient to compute and can be used if the number of SNPs is larger than the number of samples. In addition, the test is powerful in detecting effects that are relatively small for individual exon-SNP pairs but are observed for many pairs. Furthermore, test results are more often replicated across datasets than pairwise testing results. This makes our test more robust to exon-SNP pair-specific effects, which do not extend to multiple pairs within the same gene. We conclude that the test we propose here offers more power and better replicability in the search for spliceQTLeffects.