Single Nucleotide Polymorphism Chip Data Research Articles

Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs.

Preselected variants associated with the trait of interest from genome-wide association studies (GWASs) are available to improve genomic prediction in pigs. The objectives of this study were to use preselected variants from a large GWAS meta-analysis to assess the impact of single-nucleotide polymorphism (SNP) preselection strategies on genome prediction of growth and carcass traits in pigs. We genotyped 1018 Large White pigs using medium (50k) SNP arrays and then imputed SNPs to sequence level by utilizing a reference panel of 1602 whole-genome sequencing samples. We tested the effects of different proportions of selected top SNPs across different SNP preselection strategies on genomic prediction. Finally, we compared the prediction accuracies by employing genomic best linear unbiased prediction (GBLUP), genomic feature BLUP and three weighted GBLUP models. SNP preselection strategies showed an average improvement in accuracy ranging from 0.3 to 2% in comparison to the SNP chip data. The accuracy of genomic prediction exhibited a pattern of initial increase followed by decrease, or continuous decrease across various SNP preselection strategies, as the proportion of selected top SNPs increased. The highest level of prediction accuracy was observed when utilizing 1 or 5% of top SNPs. Compared with the GBLUP model, the utilization of estimated marker effects from a GWAS meta-analysis as SNP weights in the BLUP|GA model improved the accuracy of genomic prediction in different SNP preselection strategies. The new SNP preselection strategies gained from this study bring opportunities for genomic prediction in limited-size populations in pigs.

Genome-wide association studies for egg quality traits in White Leghorn layers using low-pass sequencing and SNP chip data.

Low-pass sequencing data have been proposed as an alternative to single nucleotide polymorphism (SNP) chips in genome-wide association studies (GWAS) of several species. However, it has not been used in layer chickens yet. This study aims at comparing the GWAS results of White Leghorn chickens using low-pass sequencing data (1×) and 54 k SNP chip data. Ten commercially relevant egg quality traits including albumen height, shell strength, shell colour, egg weight and yolk weight collected from up to 1,420 White Leghorn chickens were analysed. The results showed that the genomic heritability estimates based on low-pass sequencing data were higher than those based on SNP chip data. Although two GWAS analyses showed similar overall landscape for most traits, low-pass sequencing captured some significant SNPs that were not on the SNP chip. In GWAS analysis using 54 k SNP chip data, after including more individuals (up to 5,700), additional significant SNPs not detected by low-pass sequencing data were found. In conclusion, GWAS using low-pass sequencing data showed similar results to those with SNP chip data and may require much larger sample sizes to show measurable advantages.

Genomic Prediction Accuracy Using Haplotypes Defined by Size and Hierarchical Clustering Based on Linkage Disequilibrium.

Genomic prediction is an effective way to estimate the genomic breeding values from genetic information based on statistical methods such as best linear unbiased prediction (BLUP). The used of haplotype, clusters of linked single nucleotide polymorphism (SNP) as markers instead of individual SNPs can improve the accuracy of genomic prediction. Since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with a cluster of markers is higher compared to an individual marker. To make haplotypes efficient in genomic prediction, finding optimal ways to define haplotypes is essential. In this study, 770K or 50K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 3,498 cattle. Using SNP chip data, haplotype was defined in three different ways based on 1) the number of SNPs included, 2) length of haplotypes (bp), and 3) agglomerative hierarchical clustering based on LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; 5, 10, 20 or 50 SNPs on average per haplotype. A linear mixed model using haplotype to calculated the covariance matrix was applied for testing the prediction accuracy of each haplotype size. Also, conventional SNP-based linear mixed model was tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight (CWT), eye muscle area (EMA) and backfat thickness (BFT) were used as the phenotypes. This study reveals that using haplotypes generally showed increased accuracy compared to conventional SNP-based model for CWT and EMA, but found to be small or no increase in accuracy for BFT. LD clustering-based haplotypes specifically the five SNPs size showed the highest prediction accuracy for CWT and EMA. Meanwhile, the highest accuracy was obtained when length-based haplotypes with five SNPs were used for BFT. The maximum gain in accuracy was 1.3% from cross-validation and 4.6% from forward validation for EMA, suggesting that genomic prediction accuracy can be increased by using haplotypes. However, the improvement from using haplotypes may depend on the trait of interest. In addition, when the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles, thereby reducing computational costs. Therefore, finding optimal ways to define haplotypes and using the haplotype alleles as markers can improve the accuracy of genomic prediction.

Genetic Diversity of Indigenous Pigs from South China Area Revealed by SNP Array.

Simple SummaryThe pig is one of the most important livestock animals, providing the majority of protein for humans. The population genetics analysis of pigs not only helps humans understand the domestication of the pig but also helps breeders in the genetic improvement of pigs. In this study, the population genetics of 11 pig breeds of South China were analyzed with the help of single nucleotide polymorphism (SNP) chips. The results showed that the genetic diversity of South China indigenous pigs is declining rapidly, and gene introgression from commercial pigs to indigenous pigs was detected. Selection signature analysis showed differences among South China indigenous pig breeds, commercial pig breeds, and wild pig breeds were present for meat quality and growth. Our study deepened understanding of the conservation status and selection mechanisms of Chinese indigenous pigs.To investigate the genetic diversity, population structure, extent of linkage disequilibrium (LD), effective population size (Ne), and selection signatures in indigenous pigs from Guangdong and Guangxi in China, 226 pigs belonging to ten diverse populations were genotyped using single nucleotide polymorphism (SNP) chips. The genetic divergence between Chinese and Western pigs was determined based on the SNP chip data. Low genetic diversity of Dahuabai (DHB), Luchuan (LC), Lantang (LT), and Meihua (MH) pigs, and introgression of Western pigs into Longlin (LL), MH, and Yuedonghei (YDH) pigs were detected. Analysis of the extent of LD showed that indigenous pigs had low LD when pairwise SNP distance was short and high LD when pairwise SNP distance was long. Effective population size analysis showed a rapid decrease for Chinese indigenous pigs, and some pig populations had a relatively small Ne. This result indicated the loss of genetic diversity in indigenous pigs, and introgression from Western commercial pigs. Selection signatures detected in this study overlapped with meat quality traits, such as drip loss, intramuscular fat content, meat color b*, and average backfat thickness. Our study deepened understanding of the conservation status and domestication of Chinese indigenous pigs.

The GBA p.Trp378Gly mutation is a probable French-Canadian founder mutation causing Gaucher disease and synucleinopathies.

Biallelic GBA mutations cause Gaucher disease (GD), and heterozygous carriers are at risk for synucleinopathies. No founder GBA mutations in French-Canadians are known. GBA was fully sequenced using targeted next generation and Sanger sequencing in French-Canadian Parkinson disease (PD) patients (n = 436), rapid eye movement (REM)-sleep behavior disorder (RBD) patients (n = 189) and controls (n = 891). Haplotype, identity-by-descent (IBD) and principal component analyses (PCA) were performed using single nucleotide polymorphism-chip data. Data on GD patients from Toronto and Montreal were collected from patients' files. A GBA p.Trp378Gly mutation was identified in two RBD and four PD patients (1% of all patients combined), and not in controls. The two RBD patients had converted to DLB within 3 years of their diagnosis. Haplotype, IBD and PCA analysis demonstrated that this mutation is from a single founder. Out of 167 GD patients screened, 15 (9.0%) carried the p.Trp378Gly mutation, all in trans with p.Asn370Ser. Three (20%) of the GD patients with the p.Trp378Gly mutation had developed Parkinsonism, and 11 patients had family history of PD. The p.Trp378Gly mutation is the first French-Canadian founder GBA mutation to be described, which leads to synucleinopathies and to GD type 1 when in compound heterozygosity with p.Asn370Ser.

Genome-wide association study on chicken carcass traits using sequence data imputed from SNP array.

Chicken carcass traits are economically important for the chicken industry. Detecting which genes affect chicken carcass traits is of great benefit to the genetic improvement of this important agricultural species. To investigate the genetic mechanism of carcass traits in chickens, we carried out a genome-wide association study (GWAS). A total of 435 Chinese indigenous chickens were phenotyped for carcass weight (CW), eviscerated weight with giblets (EWG), and eviscerated weight (EW) after slaughter at 91days and were genotyped using a 600-K single nucleotide polymorphism (SNP) genotyping array. Twenty-four birds were selected for sequencing, and the 600K SNP panel data were imputed to sequence data with the 24 birds as the reference. Univariate GWASs were performed with GEMMA software using the whole genome sequence data imputed from SNP chip data. Finally, 3, 25, and 63 suggestively significant SNPs were identified to be associated with carcass weight (CW), eviscerated weight with giblets (EWG), and eviscerated weight (EW), respectively. Six candidate genes, RNF219, SCEL, MYCBP2, ETS1, APLP2, and PRDM10 were detected. SCEL and MYCBP2 were potentially associated with these three traits, RNF219 and APLP2 were potentially associated with EWG and EW, and ETS1 and PRDM10 were only potentially associated with EWG and EW, respectively. Compared with forefathers' research, 10 reported QTLs associated with CW were located within a 5-Mb distance near the SNPs with P value lower than 1×10-5. This study enriched the knowledge of the genetic mechanisms of chicken carcass traits.

Short communication: Genomic prediction using imputed whole-genome sequence variants in Brown Swiss Cattle

The accuracy of genomic prediction determines response to selection. It has been hypothesized that accuracy of genomic breeding values can be increased by a higher density of variants. We used imputed whole-genome sequence data and various single nucleotide polymorphism (SNP) selection criteria to estimate genomic breeding values in Brown Swiss cattle. The extreme scenarios were 50K SNP chip data and whole-genome sequence data with intermediate scenarios using linkage disequilibrium-pruned whole-genome sequence variants, only variants predicted to be missense, or the top 50K variants from genome-wide association studies. We estimated genomic breeding values for 3 traits (somatic cell score, nonreturn rate in heifers, and stature) and found differences in accuracy levels between traits. However, among different SNP sets, accuracy was very similar. In our analyses, sequence data led to a marginal increase in accuracy for 1 trait and was lower than 50K for the other traits. We concluded that the inclusion of imputed whole-genome sequence data does not lead to increased accuracy of genomic prediction with the methods.

Founder Effect of the RETC611Y Mutation in Multiple Endocrine Neoplasia 2A in Denmark: A Nationwide Study.

Multiple endocrine neoplasia (MEN) 2A and 2B are caused by REarranged during Transfection (RET) germline mutations. In a recent nationwide study, an unusually high prevalence (33%) of families with the C611Y mutation was reported, and it was hypothesized that this might be due to a founder effect. The first nationwide study of haplotypes in MEN2A families was conducted, with the aim of investigating the relatedness and occurrence of de novo mutations among Danish families carrying similar mutations. The study included 21 apparently unrelated MEN2A families identified from a nationwide Danish RET cohort from 1994 to 2014. Twelve, two, two, three, and two families carried the C611Y, C618F, C618Y, C620R, and C634R mutations, respectively. Single nucleotide polymorphism chip data and identity by descent analysis were used to assess relatedness. A common founder mutation was found among all 12 C611Y families and between both C618Y families. No relatedness was identified in the remaining families. The data suggest that all families with the C611Y germline mutation in Denmark originate from a recent common ancestor, probably explaining the unusually high prevalence of this mutation. Additionally, the results indicate that the C611Y mutation rarely arises de novo, thus underlining the need for thorough multigenerational genetic work up in carriers of this mutation.

Archaic Adaptive Introgression in TBX15/WARS2.

A recent study conducted the first genome-wide scan for selection in Inuit from Greenland using single nucleotide polymorphism chip data. Here, we report that selection in the region with the second most extreme signal of positive selection in Greenlandic Inuit favored a deeply divergent haplotype that is closely related to the sequence in the Denisovan genome, and was likely introgressed from an archaic population. The region contains two genes, WARS2 and TBX15, and has previously been associated with adipose tissue differentiation and body-fat distribution in humans. We show that the adaptively introgressed allele has been under selection in a much larger geographic region than just Greenland. Furthermore, it is associated with changes in expression of WARS2 and TBX15 in multiple tissues including the adrenal gland and subcutaneous adipose tissue, and with regional DNA methylation changes in TBX15.

Validation of multiple single nucleotide variation calls by additional exome analysis with a semiconductor sequencer to supplement data of whole-genome sequencing of a human population.

BackgroundValidation of single nucleotide variations in whole-genome sequencing is critical for studying disease-related variations in large populations. A combination of different types of next-generation sequencers for analyzing individual genomes may be an efficient means of validating multiple single nucleotide variations calls simultaneously.ResultsHere, we analyzed 12 independent Japanese genomes using two next-generation sequencing platforms: the Illumina HiSeq 2500 platform for whole-genome sequencing (average depth 32.4×), and the Ion Proton semiconductor sequencer for whole exome sequencing (average depth 109×). Single nucleotide polymorphism (SNP) calls based on the Illumina Human Omni 2.5-8 SNP chip data were used as the reference. We compared the variant calls for the 12 samples, and found that the concordance between the two next-generation sequencing platforms varied between 83% and 97%.ConclusionsOur results show the versatility and usefulness of the combination of exome sequencing with whole-genome sequencing in studies of human population genetics and demonstrate that combining data from multiple sequencing platforms is an efficient approach to validate and supplement SNP calls.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-673) contains supplementary material, which is available to authorized users.

Linkage disequilibrium levels in Bos indicus and Bos taurus cattle using medium and high density SNP chip data and different minor allele frequency distributions

Linkage disequilibrium (LD), the observed correlation between alleles at different loci in the genome, is a determinant parameter in many applications of molecular genetics. With the wider use of genomic technologies in animal breeding and animal genetics, it is worthwhile revising and improving the current knowledge and understanding of cattle LD.This study analyzes levels of LD assessed through the r2 measurement in seven breeds of cattle from both indicine (Bos indicus) and taurine (Bos taurus) (sub)species, genotyped with a high density panel (HD) of over 777,000 single nucleotide polymorphism (SNP) markers. Average levels of LD were assessed for inter-marker distances from 0kb up to 10Mb. Two more SNP panels were built in silico from the original genotypes to represent a lower density SNP chip including approximately 50,000 SNPs (50K) and a panel of 249,000 SNPs chosen to comply with specific minor allele frequency (MAF) distributions.The results show higher levels of LD than previously reported, especially at short inter-marker distances of less than 20kb, assessed through the use of the higher density panel. Negligible impact of MAF changes in the estimation of r2 was observed, supporting the use of the HD panel for LD-based methodologies even when ascertainment bias in the choice of SNPs is suspected. The 50K SNP panel indicated lower levels of LD in the first 100kb and highly unreliable LD estimates for distances shorter than 50kb. Overall, lower levels of LD were observed for all indicine breeds as compared to taurine breeds, with larger differences assessed in the first 100kb, suggesting the need for use of higher SNP densities in this (sub)species when applying LD-dependent methods, as compared to most taurine breeds.

Low budget analysis of Direct-To-Consumer genomic testing familial data

Direct-to-consumer (DTC) genetic testing is a recent commercial endeavor that allows the general public to access personal genomic data. The growing availability of personal genomic data has in turn stimulated the development of non-commercial tools for DTC data analysis. Despite this new wealth of public resources, no systematic research has been carried out to assess these tools for interpretation of DTC data. Here, we provide an initial analysis benchmark in the context of a whole family, using single nucleotide polymorphism (SNP) data. Five blood-related DTC SNP chip data tests were analyzed in conjunction with one whole exome sequence. We report findings related to genomic similarity between individuals, genetic risks and an overall assessment of data quality; thus providing an evaluation of the current potential of public domain analysis tools for personal genomics. We envisage that as the use of personal genome tests spreads to the general population, publicly available tools will have a more prominent role in the interpretation of genomic data in the context of health risks and ancestry.

Genome-wide search for genetic modulators in gene regulatory pathways: Weighted window-based peak identification algorithm

Genome-wide gene expression and genotype data have been integratively analyzed in expression quantitative trait loci (eQTL) studies to elucidate the genetics of gene transcription. Most eQTL analyses have focused on identifying polymorphic genetic variants that influence the expression levels of individual genes, and such analyses may have limitations in explaining gene regulatory pathways that are likely to involve multiple genes and their genetic and/or non-genetic modulators. We have developed a novel two-step method for identifying potential genetic modulators of transcription processes for multiple genes in a biological pathway. We proposed a new weighted window-based peak identification algorithm to improve the detection of genetic modulators for individual genes and employed a Poisson-based test to search for master genetic modulators of multiple genes. Here, we have illustrated this two-step approach by analyzing the gene expression data in the Centre d'Etude du Polymorphisme Humain (CEPH) lymphoblast cells and single nucleotide polymorphism chip data.

SNP HiTLink: a high-throughput linkage analysis system employing dense SNP data

BackgroundDuring this recent decade, microarray-based single nucleotide polymorphism (SNP) data are becoming more widely used as markers for linkage analysis in the identification of loci for disease-associated genes. Although microarray-based SNP analyses have markedly reduced genotyping time and cost compared with microsatellite-based analyses, applying these enormous data to linkage analysis programs is a time-consuming step, thus, necessitating a high-throughput platform.ResultsWe have developed SNP HiTLink (SNP High Throughput Linkage analysis system). In this system, SNP chip data of the Affymetrix Mapping 100 k/500 k array set and Genome-Wide Human SNP array 5.0/6.0 can be directly imported and passed to parametric or model-free linkage analysis programs; MLINK, Superlink, Merlin and Allegro. Various marker-selecting functions are implemented to avoid the effect of typing-error data, markers in linkage equilibrium or to select informative data.ConclusionThe results using the 100 k SNP dataset were comparable or even superior to those obtained from analyses using microsatellite markers in terms of LOD scores obtained. General personal computers are sufficient to execute the process, as runtime for whole-genome analysis was less than a few hours. This system can be widely applied to linkage analysis using microarray-based SNP data and with which one can expect high-throughput and reliable linkage analysis.

SiDCoN: A Tool to Aid Scoring of DNA Copy Number Changes in SNP Chip Data

The recent application of genome-wide, single nucleotide polymorphism (SNP) microarrays to investigate DNA copy number aberrations in cancer has provided unparalleled sensitivity for identifying genomic changes. In some instances the complexity of these changes makes them difficult to interpret, particularly when tumour samples are contaminated with normal (stromal) tissue. Current automated scoring algorithms require considerable manual data checking and correction, especially when assessing uncultured tumour specimens. To address these limitations we have developed a visual tool to aid in the analysis of DNA copy number data. Simulated DNA Copy Number (SiDCoN) is a spreadsheet-based application designed to simulate the appearance of B-allele and logR plots for all known types of tumour DNA copy number changes, in the presence or absence of stromal contamination. The system allows the user to determine the level of stromal contamination, as well as specify up to 3 different DNA copy number aberrations for up to 5000 data points (representing individual SNPs). This allows users great flexibility to assess simple or complex DNA copy number combinations. We demonstrate how this utility can be used to estimate the level of stromal contamination within tumour samples and its application in deciphering the complex heterogeneous copy number changes we have observed in a series of tumours. We believe this tool will prove useful to others working in the area, both as a training tool, and to aid in the interpretation of complex copy number changes.

Application of a BRAF Pyrosequencing Assay for Mutation Detection and Copy Number Analysis in Malignant Melanoma

Mutations in the BRAF gene are found in the majority of cutaneous malignant melanomas and subsets of other tumors. These mutations lead to constitutive activation of BRAF with increased downstream ERK (extracellular signal-regulated kinase) signaling; therefore, the development of RAF kinase inhibitors for targeted therapy is being actively pursued. A methodology that allows sensitive, cost-effective, high-throughput analysis of BRAF mutations will be needed to triage patients for specific molecular-based therapies. Pyrosequencing is a high-throughput, sequencing-by-synthesis method that is particularly useful for analysis of single nucleotide polymorphisms or hotspot mutations. Mutational analysis of BRAF is highly amenable to pyrosequencing because the majority of mutations in this gene localize to codons 600 and 601 and consist of single or dinucleotide substitutions. In this study, DNAs from a panel of melanocyte cell lines, melanoma cell lines, and melanoma tumors were used to validate a pyrosequencing assay to detect BRAF mutations. The assay demonstrates high accuracy and precision for detecting common and variant exon 15 BRAF mutations. Further, comparison of pyrosequencing data with 100K single nucleotide polymorphism microarray data allows characterization of BRAF amplification events that may accompany BRAF mutation. Pyro-sequencing serves as an excellent platform for BRAF genotyping of tumors from patients entering clinical trial.

Dissection of heterogeneous phenotypes for quantitative trait mapping

We discuss analyses of Genetic Analysis Workshop 14 data from the Collaborative Study on the Genetics of Alcoholism (COGA) as well as from a simulated complex disease, Kofendrerd personality disorder (KPD), with both genetic and phenotypic heterogeneity. Both data sets included numerous related phenotypes in addition to disease definitions. All analyses either chose from the given selection of phenotypes or defined new ones, including traits that may not have been related to alcoholism or KPD. Some contributors evaluated the genetic components of the trait. Many investigated genome-wide linkage and/or association, using microsatellites and/or single-nucleotide polymorphism (SNP) chip data. Here we will focus on methodological issues that the investigators faced. Their results depended on phenotype selection, whether continuous or discrete, the covariates included, and ethnicity of the study population. For SNP chip data, members of our group detected no difference in results for Affymetrix or Illumina chips, although higher marker density for association studies appeared to be advantageous. Overall, there were some observations that different chromosomal segments, i.e., physical locations on the p-arm, q-arm, or middle segment, might lead to possible differences in type I error rates. This finding and others highlight the importance of empirical determination of P-values to determine significance.