Genetic Associations with Sex in the Sunflower Sea Star, Pycnopodia helianthoides.

SNPs, Haplotypes, and Cancer: Applications in Molecular Epidemiology

Genetics and biology of asthma 2010: La' ci darem la mano…

Over the past few years, single nucleotide polymorphisms (SNPs) have been proposed as the next generation of markers for the identification of loci associated with complex diseases and for pharmacogenetic applications (Lander and Schork 1994; Lander 1996; Risch and Merikangas 1996; Kruglyak 1997; Schafer and Hawkins 1998). SNPs are frequently present in the genome with a density of at least one common (>20% allele frequency) SNP per kilobase pair (Lai et al. 1998; Sachidanandam et al. 2001). They are mostly biallelic ( 1.6 million SNPs in the public databases (Sachidanandam et al. 2001). In this article, I will attempt to summarize what we know about SNPs and identify some of the challenges that await us in the application of SNPs in research and medicine. The first questions most people would ask are, how many SNPs are there in the human genome and have we identified most of the SNPs? The frequently cited rate of 1 SNP/kb suggests that there are 3 million common SNPs in the human genome. However, recent data have indicated that the number of SNPs in the human genome is potentially much more than 3 million. The first indication came from the comparison of the Celera SNP database with the public data. Celera Genomics claimed to contain over 3.5 million putative SNPs in their database. However, only 400,000 of their SNPs were redundant when compared to the publicly available 1.6 million. The second line of evidence came from our own experiments. We have isolated >1000 SNPs in a 20megabase region by re-sequencing eight individuals (not the same DNA source as the TSC SNPs). The overlap between our SNPs (∼1,000) and the TSC SNPs in this region is ∼5% (instead of the expected 50% if the total number of common SNP is around 3 million). These results suggest that there are potentially 10 million or more common SNPs in the human population. A theoretical modeling experiment has also predicted that there are more than 10 million SNPs in the genome (Kruglyak and Nickerson 2001). There are two important implications in the usage of SNPs as a genetic tool if there are indeed over 10 million SNPs in the human genome. The first implication is that the SNP(s) you are looking for might not be discovered yet. The second implication is the need to select a representative set of SNPs out of the 1.6 million to cover the genome. The first problem is a difficult one since it is impossible to know whether the SNP(s) of interest is present in the current databases. There are two potential solutions. The first solution is to design experiments that combine SNP discovery and genotyping (Brenner et al. 2000). However, this approach has not been demonstrated for whole genome SNP scan and could be costly even if it is technically feasible. The second solution, which is suitable for both implications mentioned above, is the development of a comprehensive whole genome SNP marker set that has a high likelihood of detecting the SNP(s) of interest by linkage disequilibrium or association (see section below on marker set development) (Jorde 2000). So how do we design a marker set that covers the genome as completely as possible? There are many suggestions and computer models using linkage disequilibrium (LD) as a guide and striking a balance between number of markers and information content (Kruglyak 1999; Jorde 2000). A number of recent studies have indicated that an average spacing of 30 kb provides a good balance (i.e., 100,000 SNPs for whole genome) (Collins 1999; Huttley et al. 1999; Goddard et al. 2000; Jorde 2000). In addiE-MAIL ehl21107@GlaxoWellcome.com; FAX (919) 315-0113. Article and publication are at www.genome.org/cgi/ doi/10.1101/gr.192301. Insight/Outlook

Evaluation of Published Single-Nucleotide Polymorphisms (SNPs) Associated with Cytomegalovirus Infection and Disease after Hematopoietic Cell Transplantation in a Large Caucasian Cohort

The aims of this PhD research are to identify and characterise genome diversity in Triticum aestivum (bread wheat). This research will establish a process for the identification of large numbers of single nucleotide polymorphisms (SNPs) and other genetic variations in Triticum aestivum. Single nucleotide polymorphisms (SNPs) are the most abundant type of molecular genetic marker and can be used for producing high-resolution genetic maps, marker-trait association studies and marker assisted breeding. Large polyploid genomes, such as wheat, present a challenge for SNP discovery due to the potential presence of multiple homoeologues for each gene. AutoSNPdb has been successfully applied to identify SNPs from Sanger sequence data for several species, including barley, rice and Brassica, but the volume of data required to accurately call SNPs in the complex genome of wheat has prevented its application to this important crop. DNA sequencing technology has been revolutionised by the introduction of next generation sequencing, and it is now possible to generate several million sequence reads in a timely and cost effective manner. Wheat transcriptome sequence data has been generated using Roche 454 Life Sciences technology. This data has been applied for SNP discovery using a modified autoSNPdb method, which integrates SNP and gene annotation information with a graphical viewer. A total of 4,694,141 sequence reads from three bread wheat varieties were assembled to identify a total of 38,928 candidate SNPs. Each SNP is within an assembly complete with annotation, enabling the selection of polymorphism within genes of interest. The discovery of large numbers of genomic SNPs across 16 Australian diverse bread wheat varieties has been completed using a novel algorithm SGSautoSNP. More than 10x whole genome shotgun Illumina paired read sequence data was generated through a bioplatforms collaboration and the data mapped to the draft assemblies of chromosomes 7A, 7B and 7D. Over 4 million inter-varietal SNPs were identified. SNP density varied along the lengths chromosome syntenic builds as well as between genomes. SNP density analysis and SNP transition/transversion ratio analysis provide insights into the evolution and breeding history of this important crop. Both the SNP density and SNP transition/transversion ratios across the D genome are significantly lower than across the A and B genomes. This difference reflects the evolutionary history of this crop. In addition, genes within low SNP density regions may have been selected during domestication and breeding. Furthermore, this SNP resource permits the application of high resolution skim based genotyping by sequencing (GBS), trait association and analysis of structural variation in populations. An integrated database and portal for wheat genome resource, WheatGenome.info, has been established and published online. This portal provides a variety of web-based systems hosting wheat genome and genomic data, including genomic SNPs, to support wheat research and crop improvement. This research provides approaches to understand the effect of sequence variation on the form and function of wheat growth and development and response to environment.

Exploration of the Genetic Basis of GVHD by Genetic Association Studies

When the predatory sea star Pycnopodia helianthoides was placed upstream, the sea urchin Strongylocentrotus purpuratus responded defensively by extending and opening its globiferous pedicellariae. No pedicellaria response was given in control seawater or when the sea star was downstream. The snail Tegula funebralis responded by moving up vertical surfaces when Pycnopodia helianthoides or when Pisaster ochraceus were placed upstream. When these sea stars were introduced downstream, the snail's response was not significantly different from that in control seawater. Water collected from an aquarium containing a single sea star was sufficient to trigger the response of S. purpuratus and T. funebralis; the physical presence of the sea star was not essential. This indicated that a chemical stimulus was involved, and the lack of responses when sea stars were downstream argued strongly against the possible additional involvement of visual or vibrational stimuli. S. purpuratus gave stronger pedicellaria responses to water flowing over an active Pycnopodia helianthoides than to water flowing over the same sea star when it was inactive. The significance of the ability to distinguish between actively foraging and inactive predators is discussed, and a mechanism is proposed to explain differences in the amount of stimulatory chemicals released by active and inactive sea stars.

THU0019 Genome-wide pathway analysis of genome-wide association studies on psoriasis and behcet’s disease

BackgroundHIV-1 set-point viral load (SPVL) is predictive of disease progression and shows variability across HIV-1-positive (HIV+) persons. Various factors may influence SPVL including viral features, environmental exposure and host genetics. To identify single nucleotide polymorphisms (SNPs) associated with SPVL, we performed a genome-wide association study (GWAS) on a subset of participants from the Strategic Timing of AntiRetroviral Treatment (START) study covering a demographically diverse population. Methods. Consenting participants were antiretroviral therapy (ART)-naïve and SPVL was taken as log10(HIV RNA) at study entry. Genotypic data were generated on a custom content Affymetrix Axiom SNP array covering 770,558 probes. The Ensembl Gene database, assembly GRCh37.p13, was used for annotation. Principal component analysis (PCA) was used to identify population structures, and analysis of variance (ANOVA) was performed to detect associations between SNPs and SPVL. SNPs with zero variance or minor allele frequency (MAF) ≤0.05 were removed. Results. Among the 2,544 participants, PCA showed distinct population structures with strong separation between black (n = 578) and nonblack (n = 1,966) participants, Figure 1. ANOVA was performed independently on both subsets. Two SNPs located in the Major Histocompatibility Complex (MHC) class I region of chromosome six reached genome-wide significance (P < 5 × 10–8) in the non-black population: rs4418214 (P = 1.74 × 10-10), and rs57989216 (P = 3.96 × 10–8), Figure 2. Two additional SNPs, rs9264942 (P = 5.99 × 10–8) and rs7356880 (P = 9.69 × 10–8), in the same region approached significance. The minor alleles of all four SNPs were associated with lower SPVL, Figure 3. While no SNPs reached genome-wide significance in the black group, we observed similar trends toward lower SPVL for both rs4418214 and rs57989216. Conclusion. In this study we confirm the association of a previously reported SNP (rs4418214) and identify a novel candidate SNP (rs57989216) associated with lower SPVL in a population of nonblack, ART-naïve HIV+ persons. Current findings suggest that the effects of these SNPs are consistent across race groups, but further studies are required to confirm this. Our results support previous findings that variation in the MHC class I region is a major host determinant of HIV-1 control. Disclosures D. D. Murray, Centre of Excellence for Health, Immunity and Infectious diseases (CHIP), Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark: Employee, Salary. J. M. Molina, Gilead: Scientific Advisor, Consulting fee. Merck: Scientific Advisor, Consulting fee. ViiV: Scientific Advisor, Consulting fee. Teva: Scientific Advisor, Consulting fee.

Genome-wide association study to discover susceptible loci for gastric cancer in Korean population

Proper quality control of data prior to downstream analyses is fundamental to ensure integrity of results; quality control of genomic data is no exception. While many metrics of quality control of genomic data exist, the objective of the present study was to quantify the genotype and allele concordance rate between called single nucleotide polymorphism (SNP) genotypes differing in GenCall (GC) score; the GC score is a confidence measure assigned to each Illumina genotype call. This objective was achieved using Illumina beadchip genotype data from 771 cattle (12428767 genotypes in total post-editing) and 80 sheep (1557360 SNPs genotypes in total post-editing) each genotyped in duplicate. The called genotype with the lowest associated GC score was compared to the genotype called for the same SNP in the same duplicated animal sample but with a GC score of >0.90 (assumed to represent the true genotype). The mean genotype concordance rate for a GC score of <0.300, 0.300-0.549, and ≥0.550 in the cattle (sheep in parenthesis) was 0.9467 (0.9864), 0.9707 (0.9953), and 0.9994 (0.99997) respectively; the respective allele concordance rate was 0.9730 (0.9930), 0.9849 (0.9976), and 0.9997 (0.99998). Hence, concordance eroded as the GC score of the called genotype reduced, albeit the impact was not dramatic and was not very noticeable until a GC score of <0.55. Moreover, the impact was greater and more consistent in the cattle population than in the sheep population. Furthermore, an impact of GC score on genotype concordance rate existed even for the same SNP GenTrain value; the GenTrain value is a statistical score that depicts the shape of the genotype clusters and the relative distance between the called genotype clusters.

The sequencing of the human genome, the identification of common single-nucleotide polymorphisms (SNPs) and haplotype blocks, and advances in microarray technology have enabled the study of complex diseases at a level of detail not previously imaginable. These have aided in the design and analyses of association and linkage studies of many complex diseases including cardiovascular disease. Recent technological advances have enabled the undertaking of large-scale genome-wide association studies (GWAS) that can assay hundreds of thousands of polymorphic sites on hundreds to thousands of individuals to find genomic regions associated with disease. Although results from these experiments enable the identification of smaller regions of association compared with previous studies, as with all linkage and association studies, there is the need for the further investigation of regions of interest for the causal genes or variants. The purpose of this review is to present a detailed demonstration as to how publicly available resources can be used to easily guide more detailed research into genomic regions of interest identified in linkage and association study data. Large-scale projects, such as the Human Genome Sequencing project,1,2 have generated large volumes and varieties of annotated genomic data necessitating the development of Internet-based tools to organize and make practically available these public data. One important tool in human disease research is the web-based graphical genome browsers that use the human genome sequence as the framework on which to organize genomic annotations, providing various ways for researchers to view and extract important information. Currently, there are 3 human genome browsers that have been developed for public use: (1) the National Center for Biotechnology Information (NCBI) Map Viewer3; (2) the University of California Santa Cruz (UCSC) Genome Browser4; and (3) the European Bioinformatics Institute’s Ensembl system.5 Although these genome browsers share common features and …

7558 Background: Pathologic response to neoadjuvant chemotherapy correlates with survival in resected lung cancers. Predictive biomarkers of response are needed. Tumor-specific biomarkers have been hindered by reproducibility and specimen adequacy. Genome-wide association studies (most in SE Asia) have identified candidate SNPs that correlate with clinical outcomes after cisplatin-based chemotherapy. We evaluated whether these candidate SNPs are predictive of pathologic response to neoadjuvant chemotherapy in a US population. Methods: We aimed to correlate SNPs with near complete PR (ncPR) to neoadjuvant cisplatin + docetaxel in patients with resectable lung cancers. ncPR was defined as 90% necrosis, inflammation and fibrosis across serial 1cm sections of the resected tumor. Germline DNA was extracted and 50 candidate SNPs were genotyped by Sequenom Mass ARRAY iPLEX. SNPs were analyzed for correlation with ncPR using recessive (aa vs AA/aA) , dominant (AA vs aa/aA) and log-additive (linear increase in risk with each additional allele) genetic models. In this exploratory dataset, a p-value &lt;0.1 was considered worthy of further study. Results: 60 patients were treated and had sufficient DNA for analysis. Nine patients had a ncPR. 7 of 50 SNPs (table) correlated with pathologic response in one of the three models. Conclusions: Of the candidate SNPs identified from the literature, 7 showed a promising correlation to ncPR to neoadjuvant chemotherapy. This expands upon the experience evaluating SNPs and chemotherapy sensitivity into a North American population. Study of these 7 SNPs is ongoing in our current multimodality trial as a validation cohort. This study was funded by the Lung Cancer Research Foundation. [Table: see text]

LDL-cholesterol concentrations: a genome-wide association study

The process of sex determination in mammals normally unfolds in three distinct stages: (1) establishment of chromosomal sex at fertilization (XX or XY); (2) commitment to the appropriate pathway of gonadal differentiation with respect to chromosomal sex, through the action (or absence) of the Y chromosome gene SRY; and (3) correct development of secondary sexual characteristics, including internal and external genitalia, in accordance with gonadal sex. At any of these three steps, the process of sex determination can go awry, leading to disorders of sexual development. In this article, we review the typical mechanism and process of mammalian sex determination, with an emphasis on the well-characterized mouse and human models. We also consider aberrant mammalian sex determination, focusing on examples of sex reversal stemming from gene defects.