家蚕非滞育红卵突变体Re-nd突变基因的定位克隆

【目的】家蚕Bombyx mori非滞育红卵突变体Re-nd是唯一在非滞育状态下卵色呈现鲜红色的突变品种。本研究通过基因连锁分析和定位克隆的方法确定Re-nd的突变基因所在的染色体及紧密连锁位置,为后续Re-nd的功能研究及应用奠定基础。【方法】以家蚕卵色突变体Re-nd和野生型大造进行杂交,配制基因连锁分析群体材料和定位克隆群体材料;针对家蚕全染色体进行SNP标记开发,利用BC₁代群体材料进行基因连锁分析,确定Re-nd的突变基因所在的染色体;针对定位的Re nd的突变基因所在染色体进行SNP标记开发,利用BC₁群体材料对Re-nd的突变基因进行定位克隆。【结果】基因连锁分析结果显示Re-nd的突变表型与第6号染色体上的SNP标记完全连锁;初步定位克隆结果显示Re-nd的突变基因位于SNP标记SNP7和SNP17之间,物理距离4.04 Mb;以SNP7和SNP17之间筛选出的6个SNP标记和25个重组个体进行精细定位克隆,结果显示Re-nd的突变基因所在的区域位于SNP10和SNP12两个SNP标记之间的nscaf2853上,物理距离949.3 kb左右。【结论】将Re-nd的突变基因定位于第6号染色体的2个SNP标记SNP10和SNP12之间,物理距离约949.3 kb。本研究为后续Re-nd突变基因的精细定位及功能应用研究奠定了基础。     

Detection of single nucleotide polymorphisms

Single nucleotide polymorphism (SNP) detection technologies are used to scan for new polymorphisms and to determine the allele(s) of a known polymorphism in target sequences. SNP detection technologies have evolved from labor intensive, time consuming, and expensive processes to some of the most highly automated, efficient, and relatively inexpensive methods. Driven by the Human Genome Project, these technologies are now maturing and robust strategies are found in both SNP discovery and genotyping areas. The nearly completed human genome sequence provides the reference against which all other sequencing data can be compared. Global SNP discovery is therefore only limited by the amount of funding available for the activity. Local, target, SNP discovery relies mostly on direct DNA sequencing or on denaturing high performance liquid chromatography (dHPLC). The number of SNP genotyping methods has exploded in recent years and many robust methods are currently available. The demand for SNP genotyping is great, however, and no one method is able to meet the needs of all studies using SNPs. Despite the considerable gains over the last decade, new approaches must be developed to lower the cost and increase the speed of SNP detection.     

Shift of bacterial community structure in two Thai soil series affected by silver nanoparticles using ARISA

In this study we examined the influence of silver nanoparticles (SNP) on the bacterial community and microbial processes in two soils from Thailand, a Ayutthaya (Ay) and Kamphaengsaen soil series (Ks). Results of this analysis revealed that SNP did not affect to pH, electrical conductivity, cation exchange capacity, and organic matter in both the Ay and Ks series. Automated ribosomal intergenic spacer analysis (ARISA) analysis profiles showed that bacterial community decreased with increasing SNP concentration. Pearson’s correlation coefficient and multidimensional scaling analyses indicated that the effects of SNP on the bacterial community structure depended more on soil types than SNP application rates and incubation periods. Additionally, the results showed that SNP application rates affected on amount of CO₂ emissions, while SNP application rates had no effect on N mineralization in both soil types. This study is the first investigation of the effects of SNP on bacterial community using ARISA analysis. Our results might be useful to evaluate the risk associated with the applications of SNP for consumer products and agricultural practices.     

SNP ID-info: SNP ID searching and visualization platform

Many association studies provide the relationship between single nucleotide polymorphisms (SNPs), diseases and cancers, without giving a SNP ID, however. Here, we developed the SNP ID-info freeware to provide the SNP IDs within inputting genetic and physical information of genomes. The program provides an "SNP-ePCR" function to generate the full-sequence using primers and template inputs. In "SNPosition," sequence from SNP-ePCR or direct input is fed to match the SNP IDs from SNP fasta-sequence. In "SNP search" and "SNP fasta" function, information of SNPs within the cytogenetic band, contig position, and keyword input are acceptable. Finally, the SNP ID neighboring environment for inputs is completely visualized in the order of contig position and marked with SNP and flanking hits. The SNP identification problems inherent in NCBI SNP BLAST are also avoided. In conclusion, the SNP ID-info provides a visualized SNP ID environment for multiple inputs and assists systematic SNP association studies. The server and user manual are available at http://bio.kuas.edu.tw/snpid-info.     

Comparing single-nucleotide polymorphism marker-based and microsatellite marker-based linkage analyses

We compared linkage analysis results for an alcoholism trait, ALDX1 (DSM-III-R and Feigner criteria) using a nonparametric linkage analysis method, which takes into account allele sharing among several affected persons, for both microsatellite and single-nucleotide polymorphism (SNP) markers (Affymetrix and Illumina) in the Collaborative Study on the Genetics of Alcoholism (COGA) dataset provided to participants at the Genetic Analysis Workshop 14 (GAW14). The two sets of linkage results from the dense Affymetrix SNP markers and less densely spaced Illumina SNP markers are very similar. The linkage analysis results from microsatellite and SNP markers are generally similar, but the match is not perfect. Strong linkage peaks were found on chromosome 7 in three sets of linkage analyses using both SNP and microsatellite marker data. We also observed that for SNP markers, using the given genetic map and using the map by converting 1 megabase pair (1 Mb) to 1 centimorgan (cM), did not change the linkage results. We recommend the use of the 1 Mb-to-1 cM converted map in a first round of linkage analysis with SNP markers in which map integration is an issue.     

Novel single nucleotide polymorphisms of the bovine methyltransferase 3b gene and their association with meat quality traits in beef cattle

DNA methylation is essential for adipose deposition in mammals. We screened SNPs of the bovine DNA methyltransferase 3b (DNMT3b) gene in Snow Dragon beef, a commercial beef cattle population in China. Nine SNPs were found in the population and three of six novel SNPs were chosen for genotyping and analyzing a possible association with 16 meat quality traits. The frequencies of the alleles and genotypes of the three SNPs in Snow Dragon beef were similar to those in their terminal-paternal breed, Wagyu. Association analysis disclosed that SNP1 was not associated with any of the traits; SNP2 was significantly associated with lean meat color score and chuck short rib score, and SNP3 had a significant effect on dressing percentage and back-fat thickness in the beef population. The individuals with genotype GG for SNP2 had a 25.7% increase in lean meat color score and a 146% increase in chuck short rib score, compared with genotype AA. The cattle with genotype AG for SNP3 had 35.7 and 24% increases in dressing percentage and 28.8 and 29.2% increases in back-fat thickness, compared with genotypes GG and AA, respectively. Genotypic combination analysis revealed significant interactions between SNP1 and SNP2 and between SNP2 and SNP3 for the traits rib-eye area and live weight. We conclude that there is considerable evidence that DNMT3b is a determiner of beef quality traits.     

Single nucleotide polymorphism genotyping by mini-primer allele-specific amplification with universal reporter primers for identification of degraded DNA

Single nucleotide polymorphism (SNP) is informative for human identification, and much shorter regions are targeted in analysis of biallelic SNP compared with highly polymorphic short tandem repeat (STR). Therefore, SNP genotyping is expected to be more sensitive than STR genotyping of degraded human DNA. To achieve simple, economical, and sensitive SNP genotyping for identification of degraded human DNA, we developed 18 loci for a SNP genotyping technique based on the mini-primer allele-specific amplification (ASA) combined with universal reporter primers (URP). The URP/ASA-based genotyping consisted of two amplifications followed by detection using capillary electrophoresis. The sizes of the target genome fragments ranged from 40 to 67 bp in length. In the Japanese population, the frequencies of minor alleles of 18 SNPs ranged from 0.36 to 0.50, and these SNPs are informative for identification. The success rate of SNP genotyping was much higher than that of STR genotyping of artificially degraded DNA. Moreover, we applied this genotyping method to case samples and showed successful SNP genotyping of severely degraded DNA from a 4-year buffered formalin-fixed tissue sample for human identification.     

Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers

Background

At the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits (APR, ASI).

Methods

Dense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length.

Results

RR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls.

Conclusions

Accurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ~ 3,000 to 5,000 evenly spaced SNP.     

Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

Automated SNP genotype clustering algorithm to improve data completeness in high-throughput SNP genotyping datasets from custom arrays

High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

Preventive SNP–SNP interactions in the mitochondrial displacement loop (D-loop) from chronic dialysis patients

Chronic dialysis association study involving individual single nucleotide polymorphisms (SNPs) in the mitochondrial displacement loop (D-loop) has previously been reported. However, possible SNP–SNP interactions for SNPs in the D-loop which could be associated with a reduced risk for chronic dialysis were not investigated. The purpose of this study was to propose an effective algorithm to identify protective SNP–SNP interactions in the D-loop from chronic dialysis patients. We introduce ISGA that uses an initialization strategy for genetic algorithms (GA) to improve the computational analysis for protective SNP–SNP interactions. ISGA generates genotype patterns with combined SNPs (SNP barcodes) for chronic dialysis. Using our previously reported 77 SNPs in the D-loop, the algorithm-generated protective SNP barcodes for chronic dialysis were evaluated. ISGA provides the SNP barcodes with the maximum frequency differences of occurrence between the cases and controls. The identified SNP barcodes with the lowest odds ratio (OR) values were regarded as the best preventive SNP barcodes against chronic dialysis. The best ISGA-generated SNP barcodes (two to nine SNPs) are more closely associated with the prevention of chronic dialysis when more SNPs are chosen (OR = 0.64 to 0.32; 95% confidence interval = 0.882 to 0.198). The cumulative effects of SNP–SNP interactions were more dominant in ISGA rather than in GA without the initialization strategy. We provide a fast identification of chronic dialysis-associated protective SNP barcodes and demonstrate that the SNP–SNP interactions may have a cumulative effect on prediction for chronic dialysis.     

Evaluation of genotyping concordance for commercial bovine SNP arrays using quality‐assurance samples

SNP arrays are widely used in genetic research and agricultural genomics applications, and the quality of SNP genotyping data is of paramount importance. In the present study, SNP genotyping concordance and discordance were evaluated for commercial bovine SNP arrays based on two types of quality assurance (QA) samples provided by Neogen GeneSeek. The genotyping discordance rates (GDRs) between chips were on average between 0.06% and 0.37% based on the QA type I data and between 0.05% and 0.15% based on the QA type II data. The average genotyping error rate (GER) pertaining to single SNP chips, based on the QA type II data, varied between 0.02% and 0.08% per SNP and between 0.01% and 0.06% per sample. These results indicate that genotyping concordance rate was high (i.e. from 99.63% to 99.99%). Nevertheless, mitochondrial and Y chromosome SNPs had considerably elevated GDRs and GERs compared to the SNPs on the 29 autosomes and X chromosome. The majority of genotyping errors resulted from single allotyping errors, which also included the opposite instances for allele ‘dropout’ (i.e. from AB to AA or BB). Simultaneous allotyping errors on both alleles (e.g. mistaking AA for BB or vice versa) were relatively rare. Finally, a list of SNPs with a GER greater than 1% is provided. Interpretation of association effects of these SNPs, for example in genome‐wide association studies, needs to be taken with caution. The genotyping concordance information needs to be considered in the optimal design of future bovine SNP arrays.     

Maintaining genetic diversity using molecular coancestry: the effect of marker density and effective population size

Background

The most efficient method to maintain genetic diversity in populations under conservation programmes is to optimize, for each potential parent, the number of offspring left to the next generation by minimizing the global coancestry. Coancestry is usually calculated from genealogical data but molecular markers can be used to replace genealogical coancestry with molecular coancestry. Recent studies showed that optimizing contributions based on coancestry calculated from a large number of SNP markers can maintain higher levels of diversity than optimizing contributions based on genealogical data. In this study, we investigated how SNP density and effective population size impact the use of molecular coancestry to maintain diversity.

Results

At low SNP densities, the genetic diversity maintained using genealogical coancestry for optimization was higher than that maintained using molecular coancestry. The performance of molecular coancestry improved with increasing marker density, and, for the scenarios evaluated, it was as efficient as genealogical coancestry if SNP density reached at least 3 times the effective population size.However, increasing SNP density resulted in reduced returns in terms of maintained diversity. While a benefit of 12% was achieved when marker density increased from 10 to 100 SNP/Morgan, the benefit was only 2% when it increased from 100 to 500 SNP/Morgan.

Conclusions

The marker density of most SNP chips already available for farm animals is sufficient for molecular coancestry to outperform genealogical coancestry in conservation programmes aimed at maintaining genetic diversity. For the purpose of effectively maintaining genetic diversity, a marker density of around 500 SNPs/Morgan can be considered as the most cost effective density when developing SNP chips for new species. Since the costs to develop SNP chips are decreasing, chips with 500 SNPs/Morgan should become available in a short-term horizon for non domestic species.     

Use of partial least squares regression to predict single nucleotide polymorphism marker genotypes when some animals are genotyped with a low-density panel

High-density single nucleotide polymorphism (SNP) platforms are currently used in genomic selection (GS) programs to enhance the selection response. However, the genotyping of a large number of animals with high-throughput platforms is rather expensive and may represent a constraint for a large-scale implementation of GS. The use of low-density marker (LDM) platforms could overcome this problem, but different SNP chips may be required for each trait and/or breed. In this study, a strategy of imputation independent from trait and breed is proposed. A simulated population of 5865 individuals with a genome of 6000 SNP equally distributed on six chromosomes was considered. First, reference and prediction populations were generated by mimicking high- and low-density SNP platforms, respectively. Then, the partial least squares regression (PLSR) technique was applied to reconstruct the missing SNP in the low-density chip. The proportion of SNP correctly reconstructed by the PLSR method ranged from 0.78 to 0.97 when 90% and 50%, respectively, of genotypes were predicted. Moreover, data sets consisting of a mixture of actual and PLSR-predicted SNP or only actual SNP were used to predict genomic breeding values (GEBVs). Correlations between GEBV and true breeding values varied from 0.74 to 0.76, respectively. The results of the study indicate that the PLSR technique can be considered a reliable computational strategy for predicting SNP genotypes in an LDM platform with reasonable accuracy.     

Assessment of two flexible and compatible SNP genotyping platforms: TaqMan SNP Genotyping Assays and the SNPlex Genotyping System

In this review we describe the principles, protocols, and applications of two commercially available SNP genotyping platforms, the TaqMan SNP Genotyping Assays and the SNPlex Genotyping System. Combined, these two technologies meet the requirements of multiple SNP applications in genetics research and pharmacogenetics. We also describe a set of SNP selection tools and validated assay resources which we developed to accelerate the cycle of experimentation on these platforms. Criteria for selecting the more appropriate of these two genotyping technologies are presented: the genetic architecture of the trait of interest, the throughput required, and the number of SNPs and samples needed for a successful study. Overall, the TaqMan assay format is suitable for low- to mid-throughput applications in which a high assay conversion rate, simple assay workflow, and low cost of automation are desirable. The SNPlex Genotyping System, on the other hand, is well suited for SNP applications in which throughput and cost-efficiency are essential, e.g., applications requiring either the testing of large numbers of SNPs and samples, or the flexibility to select various SNP subsets.     

Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

SNP2NMD: a database of human single nucleotide polymorphisms causing nonsense-mediated mRNA decay

Elucidating the effects of genetic polymorphisms on genes and gene networks is an important step in disease association studies. We developed the SNP2NMD database for human SNPs (single nucleotide polymorphisms) that result in PTCs (premature termination codons) and trigger nonsense-mediated mRNA decay (NMD). The SNP2NMD Web interfaces provide extensive genetic information on and graphical views of the queried SNP, gene, and disease terms. Availability: SNP2NMD is available from http://variome.net, or directly from http://bioportal.kobic.re.kr/SNP2NMD. Supplementary information: http://bioportal.kobic.re.kr/SNP2NMD/Wiki.jsp?page=Statistics.     

Amerindians show association to obesity with adiponectin gene SNP45 and SNP276: population genetics of a food intake control and “thrifty” gene

Adiponectin gene polymorphisms SNP45 and SNP276 have been related to metabolic syndrome (MS) and related pathologies, including obesity. However results of associations are contradictory depending on which population is studied. In the present study, these adiponectin SNPs are for the first time studied in Amerindians. Allele frequencies are obtained and comparison with obesity and other MS related parameters are performed. Amerindians were also defined by characteristic HLA genes. Our main results are: (1) SNP276 T is associated to low diastolic blood pressure in Amerindians, (2) SNP45 G allele is correlated with obesity in female but not in male Amerindians, (3) SNP45/SNP276 T/G haplotype in total obese/non-obese subjects tends to show a linkage with non-obese Amerindians, (4) SNP45/SNP276 T/T haplotype is linked to obese Amerindian males. Also, a world population study is carried out finding that SNP45 T and SNP276 T alleles are the most frequent in African Blacks and are found significantly in lower frequencies in Europeans and Asians. This together with the fact that there is a linkage of this haplotype to obese Amerindian males suggest that evolutionary forces related to famine (or population density in relation with available food) may have shaped world population adiponectin polymorphism frequencies.     

Deletion of the SNP1 trypsin protease from Stagonospora nodorum reveals another major protease expressed during infection

The wheat fungal pathogen Stagonospora nodorum produces an extracellular trypsin-like protease, SNP1, during early stages of hyphal growth on the surface of host leaves and during penetration. Variation of SNP1 mRNA levels and enzyme activity during infection, were correlated with levels of aggressiveness of three wild-type isolates. SNP1 was deleted in two wild-type isolates using a gene replacement strategy. SNP1-deleted mutants completely lacked trypsin activity in vitro and on inoculated wheat leaves, but were not reduced in pathogenicity. SNP1-deleted mutants still have 50% of the total alkaline protease activity of wild-type. This residual activity comes from a previously undetected alkaline protease with subtilisin-like substrate and inhibitor specificities, which is produced in vitro and on host leaves. We hypothesize that this subtilisin protease may act in concert with SNP1 and may compensate for the loss of trypsin protease activity in the SNP1-deletion mutants.