High-throughput SNP Genotyping Platforms Research Articles

Rare copy number variant analysis in case-control studies using snp array data: a scalable and automated data analysis pipeline.

Rare copy number variants (CNVs) significantly influence the human genome and may contribute to disease susceptibility. High-throughput SNP genotyping platforms provide data that can be used for CNV detection, but it requires the complex pipelining of bioinformatic tools. Here, we propose a flexible bioinformatic pipeline for rare CNV analysis from human SNP array data. The pipeline consists of two major sub-pipelines: (1) Calling and quality control (QC) analysis, and (2) Rare CNV analysis. It is implemented in Snakemake following a rule-based structure that enables automation and scalability while maintaining flexibility. Our pipeline automates the detection and analysis of rare CNVs. It implements a rigorous CNV quality control, assesses the frequencies of these rare CNVs in patients versus controls, and evaluates the impact of CNVs on specific genes or pathways. We hence aim to provide an efficient yet flexible bioinformatic framework to investigate rare CNVs in biomedical research.

Development and Application of a Target Capture Sequencing SNP-Genotyping Platform in Rice.

The development of efficient, robust, and high-throughput SNP genotyping platforms is pivotal for crop genetics and breeding. Recently, SNP genotyping platforms based on target capture sequencing, which is very flexible in terms of the number of SNP markers, have been developed for maize, cassava, and fava bean. We aimed to develop a target capture sequencing SNP genotyping platform for rice. A target capture sequencing panel containing 2565 SNPs, including 1225 SNPs informative for japonica and 1339 SNPs informative for indica, was developed. This platform was used in diversity analysis of 50 rice varieties. Of the 2565 SNP markers, 2341 (91.3%) produced useful polymorphic genotype data, enabling the production of a phylogenetic tree of the 50 varieties. The mean number of markers polymorphic between any two varieties was 854. The platform was used for QTL mapping of preharvest sprouting (PHS) resistance in an F8 recombinant inbred line population derived from the cross Odae × Joun. A genetic map comprising 475 markers was constructed, and two QTLs for PHS resistance were identified on chromosomes 4 and 11. This system is a powerful tool for rice genetics and breeding and will facilitate QTL studies and gene mapping, germplasm diversity analysis, and marker-assisted selection.

Comparative analysis of inbreeding parameters and runs of homozygosity islands in 2 Italian autochthonous cattle breeds mainly raised in the Parmigiano-Reggiano cheese production region

Reggiana and Modenese are autochthonous cattle breeds, reared in the North of Italy, that can be mainly distinguished for their standard coat color (Reggiana is red, whereas Modenese is white with some pale gray shades). Almost all milk produced by these breeds is transformed into 2 mono-breed branded Parmigiano-Reggiano cheeses, from which farmers receive the economic incomes needed for the sustainable conservation of these animal genetic resources. After the setting up of their herd books in 1960s, these breeds experienced a strong reduction in the population size that was subsequently reverted starting in the 1990s (Reggiana) or more recently (Modenese) reaching at present a total of about 2,800 and 500 registered cows, respectively. Due to the small population size of these breeds, inbreeding is a very important cause of concern for their conservation programs. Inbreeding is traditionally estimated using pedigree data, which are summarized in an inbreeding coefficient calculated at the individual level (FPED). However, incompleteness of pedigree information and registration errors can affect the effectiveness of conservation strategies. High-throughput SNP genotyping platforms allow investigation of inbreeding using genome information that can overcome the limits of pedigree data. Several approaches have been proposed to estimate genomic inbreeding, with the use of runs of homozygosity (ROH) considered to be the more appropriate. In this study, several pedigree and genomic inbreeding parameters, calculated using the whole herd book populations or considering genotyping information (GeneSeek GGP Bovine 150K) from 1,684 Reggiana cattle and 323 Modenese cattle, were compared. Average inbreeding values per year were used to calculate effective population size. Reggiana breed had generally lower genomic inbreeding values than Modenese breed. The low correlation between pedigree-based and genomic-based parameters (ranging from 0.187 to 0.195 and 0.319 to 0.323 in the Reggiana and Modenese breeds, respectively) reflected the common problems of local populations in which pedigree records are not complete. The high proportion of short ROH over the total number of ROH indicates no major recent inbreeding events in both breeds. ROH islands spread over the genome of the 2 breeds (15 in Reggiana and 14 in Modenese) identified several signatures of selection. Some of these included genes affecting milk production traits, stature, body conformation traits (with a main ROH island in both breeds on BTA6 containing the ABCG2, NCAPG, and LCORL genes) and coat color (on BTA13 in Modenese containing the ASIP gene). In conclusion, this work provides an extensive comparative analysis of pedigree and genomic inbreeding parameters and relevant genomic information that will be useful in the conservation strategies of these 2 iconic local cattle breeds.

Construction of a Genetic Map of RILs Derived from Wheat (T. aestivum L.) Varieties Pamyati Azieva × Paragon Using High-Throughput SNP Genotyping Platform KASP—Kompetitive Allele Specific PCR

The main purposes of the study were (i) to develop a mapping population, (ii) to construct its genetic map for further identification of genes associated with important agronomic traits. To the best of our knowledge this is the first segregating population and genetic map developed for Kazakh bread wheat. The work is an example of how plant breeding programs in Kazakhstan have started successfully using next generation plant breeding methods to carry out systematic plant breeding to enhance final grain harvest. The KASP technology and SNP DNA-markers have been exploited to genotype and build a genetic map. The total length of the map was 1376 cM. A total 157, which spanned A, B and D subgenomes, out of the initial 178 SNP markers used formed 26 linkage groups leaving 1 duplicated and 20 unassigned markers. As threshold distance between markers was set ≤30 cM, two linkage groups were obtained for chromosomes such as 2А, 2В, 2D, 3A, 5A, 6B and 7A. Kosambi mapping function was employed to calculate recombination units. RILs were developed through SSD method up to F4 generation. Almost 97% of identified alleles were useful in evaluating the population’s genetic diversity; the remaining 3% showed no outcome. As a result, 77 DNA markers were mapped for A, 74 for B and 27 for D genomes. The mapping population will be genotyped using a high marker density array platform such as Illumina iSelect to obtain a genetic map with a relatively high coverage. Then, the population and high-resolution genetic map will be used to identify genes influencing wheat adaptation in Kazakhstan.

Analyses of African common bean (Phaseolus vulgaris L.) germplasm using a SNP fingerprinting platform: diversity, quality control and molecular breeding

Common bean (Phaseolus vulgaris L.) is an important staple crop for smallholder farmers, particularly in Eastern and Southern Africa. To support common bean breeding and seed dissemination, a high throughput SNP genotyping platform with 1500 established SNP assays has been developed at a genotyping service provider which allows breeders without their own genotyping infrastructure to outsource such service. A set of 708 genotypes mainly composed of germplasm from African breeders and CIAT breeding program were assembled and genotyped with over 800 SNPs. Diversity analysis revealed that both Mesoamerican and Andean gene pools are in use, with an emphasis on large seeded Andean genotypes, which represents the known regional preferences. The analysis of genetic similarities among germplasm entries revealed duplicated lines with different names as well as distinct SNP patterns in identically named samples. Overall, a worrying number of inconsistencies was identified in this data set of very diverse origins. This exemplifies the necessity to develop and use a cost-effective fingerprinting platform to ensure germplasm purity for research, sharing and seed dissemination. The genetic data also allows to visualize introgressions, to identify heterozygous regions to evaluate hybridization success and to employ marker-assisted selection. This study presents a new resource for the common bean community, a SNP genotyping platform, a large SNP data set and a number of applications on how to utilize this information to improve the efficiency and quality of seed handling activities, breeding, and seed dissemination through molecular tools.

Targeted resequencing of coding DNA sequences for SNP discovery in nonmodel species.

Targeted capture coupled with high-throughput sequencing can be used to gain information about nuclear sequence variation at hundreds to thousands of loci. Divergent reference capture makes use of molecular data of one species to enrich target loci in other (related) species. This is particularly valuable for nonmodel organisms, for which often no a priori knowledge exists regarding these loci. Here, we have used targeted capture to obtain data for 809 nuclear coding DNA sequences (CDS) in a nonmodel organism, the Eurasian lynx Lynx lynx, using baits designed with the help of the published genome of a related model organism (the domestic cat Felis catus). Using this approach, we were able to survey intraspecific variation at hundreds of nuclear loci in L.lynx across the species' European range. A large set of biallelic candidate SNPs was then evaluated using a high-throughput SNP genotyping platform (Fluidigm), which we then reduced to a final 96 SNP-panel based on assay performance and reliability; validation was carried out with 100 additional Eurasian lynx samples not included in the SNP discovery phase. The 96 SNP-panel developed from CDS performed very successfully in the identification of individuals and in population genetic structure inference (including the assignment of individuals to their source population). In keeping with recent studies, our results show that genic SNPs can be valuable for genetic monitoring of wildlife species.

Genotyping of the ABCG2 gene using Matrix-Associated Laser Desorption/Ionisation, Time-of-Flight Mass Spectrometry.

The aim of this study was to evaluate the applicability of genotyping of the ABCG2 gene using MALDI-TOF MS and to estimate the allele frequency in the Japanese population. Jr (a-) phenotype has a prevalence of approximately 0·05% among Japanese blood donors; DNA-based genotyping was conducted to investigate the molecular basis of the Jr (a-) phenotype along with serological typing. To detect all SNPs of the ABCG2 gene, a high-throughput SNP genotyping platform is needed. Overall, 1004 Jr (a-) blood samples were collected from blood donors in Japan and pre-genotyped. To detect the SNPs of the ABCG2 gene using MALDI-TOF MS, polymerase chain reaction and unextend primer were designed. In total, 205 Jr (a-) samples were genotyped using MALDI-TOF MS analysis. The SNPs of 1004 Jr (a-) samples were identified using the HRM analysis and DNA sequencing, and 799 of 1004 (80%) Jr (a-) samples had the homozygous for c.376 T. The designed primers for MALDI-TOF MS perfectly detected the SNPs of the ABCG2 gene. A total of 205 Jr (a-) samples were genotyped using MALDI-TOF MS. Calling failures occurred in only two samples with the mutations c.736CT to c.376C and c.421C to c.421CA. The concordance rate between the pre-genotyped and MALDI-TOF MS-based genotyping results was very high (99·02%) for all ABCG2 alleles. Jr (a-) Japanese donors had almost the homozygous for c.376 T. However, detections of more than 20 SNPs of the ABCG2 gene for the JR blood group genotyping are needed. MALDI-TOF MS-based genotyping was highly concordant with the pre-genotyped results for all ABCG2 alleles.

QTL Scanning for Rice Yield Using a Whole Genome SNP Array

High-throughput SNP genotyping is widely used for plant genetic studies. Recently, a RICE6K SNP array has been developed based on the Illumina Bead Array platform and Infinium SNP assay technology for genome-wide evaluation of allelic variations and breeding applications. In this study, the RICE6K SNP array was used to genotype a recombinant inbred line (RIL) population derived from the cross between the indica variety, Zhenshan 97, and the japonica variety, Xizang 2. A total of 3324 SNP markers of high quality were identified and were grouped into 1495 recombination bins in the RIL population. A high-density linkage map, consisting of the 1495 bins, was developed, covering 1591.2 cM and with average length of 1.1 cM per bin. Segregation distortions were observed in 24 regions of the 11 chromosomes in the RILs. One half of the distorted regions contained fertility genes that had been previously reported. A total of 23 QTLs were identified for yield. Seven QTLs were firstly detected in this study. The positive alleles from about half of the identified QTLs came from Zhenshan 97 and they had lower phenotypic values than Xizang 2. This indicated that favorable alleles for breeding were dispersed in both parents and pyramiding favorable alleles could develop elite lines. The size of the mapping population for QTL analysis using high throughput SNP genotyping platform is also discussed.

A High Throughput Genotyping Approach Reveals Distinctive Autosomal Genetic Signatures for European and Near Eastern Wild Boar

The lack of a Near Eastern genetic signature in modern European porcine breeds indicates that, although domestic pigs from the Fertile Crescent entered Europe during the Neolithic, they were completely replaced by their European counterparts in a short window of time. Whilst the absence of such genetic signature has been convincingly demonstrated at the mitochondrial level, variation at the autosomal genomes of European and Near Eastern Sus scrofa has not been compared yet. Herewith, we have explored the genetic relationships among 43 wild boar from Europe (N = 21), Near East (N = 19) and Korea (N = 3), and 40 Iberian (N = 16), Canarian (N = 4) and Mangalitza (N = 20) pigs by using a high throughput SNP genotyping platform. After data filtering, 37,167 autosomal SNPs were used to perform population genetics analyses. A multidimensional scaling plot based on genome-wide identity-by-state pairwise distances inferred with PLINK showed that Near Eastern and European wild boar populations are genetically differentiated. Maximum likelihood trees built with TreeMix supported this conclusion i.e. an early population split between Near Eastern and European Sus scrofa was observed. Moreover, analysis of the data with Structure evidenced that the sampled Iberian, Canarian and Mangalitza pigs did not carry any autosomal signature compatible with a Near Eastern ancestry, a finding that agrees well with previous mitochondrial studies.

Technical note: High fidelity of whole-genome amplified sheep (Ovis aries) deoxyribonucleic acid using a high-density single nucleotide polymorphism array-based genotyping platform1

Advances in high-throughput genotyping technologies have afforded researchers the opportunity to study ever-increasing numbers of SNP in animal genomes. However, many studies encounter difficulties in obtaining sufficient quantities of high-quality DNA for such analyses, particularly when the source biological material is limited or degraded. The recent development of in vitro whole-genome amplification approaches has permitted researchers to circumvent these challenges by increasing the amount of usable DNA in normally small-quantity samples. Here, we assess the performance of whole-genome amplification products generated from ovine genomic DNA using a high-throughput SNP genotyping platform, the newly developed Illumina ovineSNP50 BeadChip. Our results demonstrate a high genotype call rate for conventional genomic DNA and whole-genome amplified genomic DNA. The data also reveal an exceptionally high concordance rate ( > or = 99%) between the genotypes generated from whole-genome amplified products and their conventional genomic DNA counterparts. This study supports the use of whole-genome amplification as a viable solution for the analysis of high-density SNP genotypic data using compromised or limited starting material.

Assignment of chromosomal locations for unassigned SNPs/scaffolds based on pair-wise linkage disequilibrium estimates

BackgroundRecent developments of high-density SNP chips across a number of species require accurate genetic maps. Despite rapid advances in genome sequence assembly and availability of a number of tools for creating genetic maps, the exact genome location for a number of SNPs from these SNP chips still remains unknown. We have developed a locus ordering procedure based on linkage disequilibrium (LODE) which provides estimation of the chromosomal positions of unaligned SNPs and scaffolds. It also provides an alternative means for verification of genetic maps. We exemplified LODE in cattle.ResultsThe utility of the LODE procedure was demonstrated using data from 1,943 bulls genotyped for 73,569 SNPs across three different SNP chips. First, the utility of the procedure was tested by analysing the masked positions of 1,500 randomly-chosen SNPs with known locations (50 from each chromosome), representing three classes of minor allele frequencies (MAF), namely >0.05, 0.01<MAF ≤ 0.05 and 0.001<MAF ≤ 0.01. The efficiency (percentage of masked SNPs that could be assigned a location) was 96.7%, 30.6% and 2.0%; with an accuracy (the percentage of SNPs assigned correctly) of 99.9%, 98.9% and 33.3% in the three classes of MAF, respectively. The average precision for placement of the SNPs was 914, 3,137 and 6,853 kb, respectively. Secondly, 4,688 of 5,314 SNPs unpositioned in the Btau4.0 assembly were positioned using the LODE procedure. Based on these results, the positions of 485 unordered scaffolds were determined. The procedure was also used to validate the genome positions of 53,068 SNPs placed on Btau4.0 bovine assembly, resulting in identification of problem areas in the assembly. Finally, the accuracy of the LODE procedure was independently validated by comparative mapping on the hg18 human assembly.ConclusionThe LODE procedure described in this study is an efficient and accurate method for positioning SNPs (MAF>0.05), for validating and checking the quality of a genome assembly, and offers a means for positioning of unordered scaffolds containing SNPs. The LODE procedure will be helpful in refining genome sequence assemblies, especially those being created from next-generation sequencing where high-throughput SNP discovery and genotyping platforms are integrated components of genome analysis.

Terry Burke - recipient of the 2009Molecular EcologyPrize

Terry Burke - recipient of the 2009<i>Molecular Ecology</i>Prize

Advances in genetics of restless legs syndrome

Recent advances in genetic studies on restless legs syndrome (RLS) have led to a series of important discoveries that greatly expand our knowledge on the molecular basis of this disease. RLS is a common and complex disease. Several genetic epidemiological studies and twin studies have characterized the genetic components of RLS and suggest that it is a highly heritable trait with heritability estimates of about 50%. The five chromosomal positions (12q13-23, 14q13-21, 9p24-22, 2q33, and 20p13) have been determined either by model-based linkage analysis or by model-free linkage analysis, which have provided the linkage maps for positional cloning of the underlying genes or susceptibility genes for this disorder. Most recent genome-wide association studies based on high-throughput SNP genotyping platforms have also defined three significantly associated RLS regions (6p21.2, 2p 14, and 15q23). By integrating with our recent genetic studies for this neurological disorder, we reviewed the most important findings achieved in genetic studies of RLS.

Cis-regulatory Variation Is Typically Polyallelic in Drosophila

Gene expression levels vary heritably, with approximately 25-35% of the loci affecting expression acting in cis. We characterized standing cis-regulatory variation among 16 wild-derived strains of Drosophila melanogaster. Our experiment's robust biological and technical replication enabled precise estimates of variation in allelic expression on a high-throughput SNP genotyping platform. We observed concordant, significant differential allelic expression (DAE) in 7/10 genes queried with multiple SNPs, and every member of a set of eight additional, one-assay genes suggest significant DAE. Four of the high-confidence, multiple-assay genes harbor three or more statistically distinguishable allelic classes, often at intermediate frequency. Numerous intermediate-frequency, detectable regulatory polymorphisms cast doubt on a model in which cis-acting variation is a product of deleterious mutations of large effect. Comparing our data to predictions of population genetics theory using coalescent simulations, we estimate that a typical gene harbors 7-15 cis-regulatory sites (nucleotides) at which a selectively neutral mutation would elicit an observable expression phenotype. If standing cis-regulatory variation is actually slightly deleterious, the true mutational target size is larger.

Molecular Genotyping in Transfusion Medicine

“Blood-Matching Goes Genetic,” a recent News Focus article in Science (1), illustrates the growing awareness of the impact of the genomics revolution on transfusion medicine and its potential to transform the way blood is selected for transfusion. Antibody-based technology has been the basis for blood group typing since the discovery of the ABO blood groups by Landsteiner and colleagues. With the availability of single-nucleotide polymorphism (SNP)1 genotyping, the blood bank is poised to become one of the primary laboratory disciplines to benefit from the PCR-technology revolution. DNA-testing methods are applicable to transfusion medicine because the genes encoding blood group antigens and the genetic polymorphisms associated with the antigens are known. Similar to many disease markers, most of the antigens in blood groups result from SNPs inherited in a straightforward Mendelian manner. SNPs account for much of human genome diversity, and the ability to link SNPs to disease or disease risk, and even to individual variation in drug responses, makes SNP profiling a technology likely to contribute to fulfilling the promise of personalized medicine. The appreciation of this potential has fueled the development of high-throughput SNP-genotyping platforms, which are currently being applied in the prediction of blood group antigens (2). The use of such platforms enables rapid, cost-effective screening for multiple clinically significant blood groups in a single assay. The field of transfusion medicine can now consider individualized transfusion treatment, i.e., matching donors with patients at multiple blood group loci in addition to the ABO and RhD systems. This approach holds promise for reducing, and potentially eliminating, alloimmunization. SNP typing allows the evaluation of blood group antigens in circumstances in which it is not possible with antibody-based methods. For example, in patients receiving chronic or massive transfusions, the presence of donor red blood cells (RBCs) makes typing inaccurate. In patients …

Successful amplification of degraded DNA for use with high-throughput SNP genotyping platforms

Highly accurate and high-throughput SNP genotyping platforms are increasingly popular but the performance of suboptimal DNA samples remains unclear. The aim of our study was to determine the best platform, amplification technique, and loading concentration to maximize genotype accuracy and call rate using degraded samples. We amplified high-molecular weight genomic DNA samples recently extracted from whole blood and degraded DNA samples extracted from 50-year-old patient sera. Two whole-genome amplification (WGA) methodologies were used: an isothermal multiple displacement amplification method (MDA) and a fragmentation-PCR-based method (GenomePlex [GPLEX]; Sigma-Aldrich, St. Louis, MO). Duplicate runs were performed on genome-wide dense SNP arrays (Nsp-Mendel; Affymetrix) and custom SNP platforms based on molecular inversion probes (Targeted Genotyping [TG]; Affymetrix) and BeadArray technology (Golden Gate [GG]; Illumina). Miscalls and no-calls on Mendel arrays were correlated with each other, with confidence scores from the Bayesian calling algorithm, and with average probe intensity. Degraded DNA amplified with MDA gave low call rates and concordance across all platforms at standard loading concentrations. The call rate with MDA on GG was improved when a 5 x concentration of amplified DNA was used. The GPLEX amplification gave high call rate and concordance for degraded DNA at standard and higher loading concentrations on both TG and GG platforms. Based on these analyses, after standard filtering for SNP and sample performance, we were able to achieve a mean call rate of 99.7% and concordance 99.7% using degraded samples amplified by GPLEX on GG technology at 2 x loading concentration. These findings may be useful for investigators planning case-control association studies with patient samples of suboptimal quality.

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 3 K 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.

026.Endometriosis — linkage, positional cloning and genome wide association

Endometriosis is a complex disease which affects up to 10% of women in their reproductive years. Common symptoms include severe dysmenorrhea and pelvic pain. The disease is associated with subfertility and some malignancies. Genetic and environmental factors both influence endometriosis. The aim of our studies is to identify genetic variation contributing to endometriosis and define pathways leading to disease. We recruited a large cohort of affected sister pair (ASP) families where two sisters have had surgically confirmed disease and conducted a 10�cM genome scan. The results of the linkage analysis identified one chromosomal region with significant linkage and one region of suggestive linkage. The regions implicated by these studies are generally of the order of 20–30�cM and include several hundred genes. Locating the gene or genes contributing to disease within the region is a challenging task. The best approach to the problem is association studies using a high density of SNP markers. The recent development of human SNP maps and high throughput SNP genotyping platforms makes this task easier. We have developed high throughput SNP typing at QIMR using the Sequenom MassARRAY platform. The method allows multiple SNP assays to be genotyped on the same sample in a single experiment. Throughput and genotyping costs depend critically on this level of multiplexing and we routinely genotype 6–8 SNPs in a single assay. We are using bioinformatics and functional approaches to develop a priority list of genes to screen early in the project. SNP markers in these genes are being genotyped using the MassARRAY platform to search for genes contributing to endometriosis. In the future, genome wide association studies with our families may locate additional genes contributing to endometriosis.