Applications of whole-genome high-density SNP genotyping

Assessment of DNA quality for whole genome library preparation

In recent years, more sophisticated DNA technologies for genotyping have enabled considerable progress in various fields such as clinical genetics, archaeogenetics and forensic genetics. DNA samples previously rejected as too challenging to analyze due to low amounts of degraded DNA can now provide useful information. To increase the chances of success with the new methodologies, it is crucial to know the fragment size of the template DNA molecules, and whether the DNA in a sample is mostly single or double stranded. With this knowledge, an appropriate library preparation method can be chosen, and the DNA shearing parameters of the protocol can be adjusted to the DNA fragment size in the sample. In this study, we first developed and evaluated a user-friendly fluorometry-based protocol for estimation of DNA strandedness. We also evaluated different capillary electrophoresis methods for estimation of DNA fragmentation levels. Next, we applied the developed methodologies to a broad variety of DNA samples processed with different DNA extraction protocols. Our findings show that both the applied DNA extraction method and the sample type affect the DNA strandedness and fragmentation. The established protocols and the gained knowledge will be applicable for future sequencing-based high-density SNP genotyping in various fields.

Single nucleotide polymorphisms cumulating to genetic variation for fertility in crossbred (Bos taurus × Bos indicus) bull spermatozoa

Spermatozoa from high-fertile (HF) and low-fertile (LF) breeding bulls were subjected to high-throughput next-generation sequencing to identify important Single nucleotide polymorphisms (SNPs) and novel variants associated with fertility. A total of 77,038 genome-wide SNPs were identified, among which, 10,788 were novel variants. A total of 42,290 and 34,748 variants were recorded with 6115 and 4673 novel variants in in HF and LF bulls, respectively. Higher number of SNPs were identified in HF compared to LF bulls. GO analysis of filtered genes with significant variations in HF bulls indicated their involvement in oxidative phosphorylation and metabolic pathways. GO analysis of filtered genes with significant variation in LF bulls revealed their involvement in Ca2++ ion binding, structural constituent of ribosome, and biological processes like translation and ribosomal small subunit assembly. The study identified SNPs in candidate genes including TPT1, BOLA-DRA, CD74, RPS17, RPS28, RPS29, RPL14, RPL13, and RPS27A, which are linked to sperm functionality, survival, oxidative stress, and bull fertility. The identified SNPs could be used in selection of bulls for high fertility and the variation in these genes could be established as an explanation for the fertility differences in bulls upon validation in large number of bulls.

Long-range linkage disequilibrium in French beef cattle breeds

BACKGROUND

Linkage disequilibrium (LD) is a key parameter to study the history of populations and to identify and fine map quantitative trait loci (QTL) and it has been studied for many years in animal populations. The advent of new genotyping technologies has allowed whole-genome LD studies in most cattle populations. However, to date, long-range LD (LRLD) between distant variants on the genome has not been investigated in detail in cattle. Here, we present the first comprehensive study of LRLD in French beef cattle by analysing data on 672 Charolais (CHA), 462 Limousine (LIM) and 326 Blonde d'Aquitaine (BLA) individuals that were genotyped on the Illumina BovineHD Beadchip. Furthermore, whole-genome LD and haplotype block structure were analysed in these three breeds.

RESULTS

We computed linkage disequilibrium (r²) values for 5.9, 5.6 and 6.0 billion pairs of SNPs on the 29 autosomes of CHA, LIM and BLA, respectively. Mean r² values drop to less than 0.1 for distances between SNPs greater than 120 kb. However, for the first time, we detected the existence of LRLD in the three main French beef breeds. In total, 598, 266, and 795 LRLD events (r² ≥ 0.6) were detected in CHA, LIM and BLA, respectively. Each breed had predominantly population-specific LRLD interactions, although shared LRLD events occurred in a number of regions (55 LRLD events were shared between two breeds and nine between the three breeds). Examples of possible functional gene interactions and QTL co-location were observed with some of these LRLD events, which suggests epistatic selection.

CONCLUSIONS

We identified long-range linkage disequilibrium for the first time in French beef cattle populations. Epistatic selection may be the main source of the observed LRLD events, but other forces may also be involved. LRLD information should be accounted for in genome-wide association studies.

Mapping genes that predict treatment outcome in admixed populations

There is great interest in characterizing the genetic architecture underlying drug response. For many drugs, gene-based dosing models explain a considerable amount of the overall variation in treatment outcome. As such, prescription drug labels are increasingly being modified to contain pharmacogenetic information. Genetic data must, however, be interpreted within the context of relevant clinical covariates. Even the most predictive models improve with the addition of data related to biogeographical ancestry. The current review explores analytical strategies that leverage population structure to more fully characterize genetic determinants of outcome in large clinical practice-based cohorts. The success of this approach will depend upon several key factors: (1) the availability of outcome data from groups of admixed individuals (that is, populations recombined over multiple generations), (2) a measurable difference in treatment outcome (that is, efficacy and toxicity end points), and (3) a measurable difference in allele frequency between the ancestral populations.

Utility of the pooling approach as applied to whole genome association scans with high-density Affymetrix microarrays

Background

We report an attempt to extend the previously successful approach of combining SNP (single nucleotide polymorphism) microarrays and DNA pooling (SNP-MaP) employing high-density microarrays. Whereas earlier studies employed a range of Affymetrix SNP microarrays comprising from 10 K to 500 K SNPs, this most recent investigation used the 6.0 chip which displays 906,600 SNP probes and 946,000 probes for the interrogation of CNVs (copy number variations). The genotyping assay using the Affymetrix SNP 6.0 array is highly demanding on sample quality due to the small feature size, low redundancy, and lack of mismatch probes.

Findings

In the first study published so far using this microarray on pooled DNA, we found that pooled cheek swab DNA could not accurately predict real allele frequencies of the samples that comprised the pools. In contrast, the allele frequency estimates using blood DNA pools were reasonable, although inferior compared to those obtained with previously employed Affymetrix microarrays. However, it might be possible to improve performance by developing improved analysis methods.

Conclusions

Despite the decreasing costs of genome-wide individual genotyping, the pooling approach may have applications in very large-scale case-control association studies. In such cases, our study suggests that high-quality DNA preparations and lower density platforms should be preferred.

Genome wide association studies for milk production traits in Chinese Holstein population

Genome-wide association studies (GWAS) based on high throughput SNP genotyping technologies open a broad avenue for exploring genes associated with milk production traits in dairy cattle. Motivated by pinpointing novel quantitative trait nucleotide (QTN) across Bos Taurus genome, the present study is to perform GWAS to identify genes affecting milk production traits using current state-of-the-art SNP genotyping technology, i.e., the Illumina BovineSNP50 BeadChip. In the analyses, the five most commonly evaluated milk production traits are involved, including milk yield (MY), milk fat yield (FY), milk protein yield (PY), milk fat percentage (FP) and milk protein percentage (PP). Estimated breeding values (EBVs) of 2,093 daughters from 14 paternal half-sib families are considered as phenotypes within the framework of a daughter design. Association tests between each trait and the 54K SNPs are achieved via two different analysis approaches, a paternal transmission disequilibrium test (TDT)-based approach (L1-TDT) and a mixed model based regression analysis (MMRA). In total, 105 SNPs were detected to be significantly associated genome-wise with one or multiple milk production traits. Of the 105 SNPs, 38 were commonly detected by both methods, while four and 63 were solely detected by L1-TDT and MMRA, respectively. The majority (86 out of 105) of the significant SNPs is located within the reported QTL regions and some are within or close to the reported candidate genes. In particular, two SNPs, ARS-BFGL-NGS-4939 and BFGL-NGS-118998, are located close to the DGAT1 gene (160bp apart) and within the GHR gene, respectively. Our findings herein not only provide confirmatory evidences for previously findings, but also explore a suite of novel SNPs associated with milk production traits, and thus form a solid basis for eventually unraveling the causal mutations for milk production traits in dairy cattle.

Copy number variations in the human genome and strategies for analysis

The structure and sequence of the genome is immensely variable in the human population. Segmental copy number variants (CNVs) contribute to the extensive phenotypic diversity among humans and have been shown to associate with disease susceptibility. In this article, we provide a detailed review of human genetic variations and the experimental approaches used to discover, catalog, and genotype CNVs.

Two-stage genome-wide association study identifies integrin beta 5 as having potential role in bull fertility

Background

Fertility is one of the most critical factors controlling biological and financial performance of animal production systems and genetic improvement of lines. The objective of this study was to identify molecular defects in the sperm that are responsible for uncompensable fertility in Holstein bulls. We performed a comprehensive genome wide analysis of single nucleotide polymorphisms (SNP) for bull fertility followed by a second-stage replication in additional bulls for a restricted set of markers.

Results

In the Phase I association study, we genotyped the genomic sperm DNA of 10 low-fertility and 10 high-fertility bulls using Bovine SNP Gene Chips containing approximately 10,000 random SNP markers. In these animals, 8,207 markers were found to be polymorphic, 97 of which were significantly associated with fertility (p < 0.01). In the Phase II study, we tested the four most significant SNP from the Phase I study in 101 low-fertility and 100 high-fertility bulls, with two SNPs (rs29024867 and rs41257187) significantly replicated. Rs29024867 corresponds to a nucleotide change of C → G 2,190 bp 3' of the collagen type I alpha 2 gene on chromosome 4, while the rs41257187 (C → T) is in the coding region of integrin beta 5 gene on chromosome 1. The SNP rs41257187 induces a synonymous (Proline → Proline), suggesting disequilibrium with the true causative locus (i), but we found that the incubation of bull spermatozoa with integrin beta 5 antibodies significantly decreased the ability to fertilize oocytes. Our findings suggest that the bovine sperm integrin beta 5 protein plays a role during fertilization and could serve as a positional or functional marker of bull fertility.

Conclusion

We have identified molecular markers associated with bull fertility and established that at least one of the genes harboring such variation has a role in fertility. The findings are important in understanding mechanisms of uncompensatory infertility in bulls, and in other male mammals. The findings set the stage for more hypothesis-driven research aimed at discovering the role of variation in the genome that affect fertility and that can be used to identify molecular mechanisms of development.

High-resolution haplotype block structure in the cattle genome

Background

The Bovine HapMap Consortium has generated assay panels to genotype ~30,000 single nucleotide polymorphisms (SNPs) from 501 animals sampled from 19 worldwide taurine and indicine breeds, plus two outgroup species (Anoa and Water Buffalo). Within the larger set of SNPs we targeted 101 high density regions spanning up to 7.6 Mb with an average density of approximately one SNP per 4 kb, and characterized the linkage disequilibrium (LD) and haplotype block structure within individual breeds and groups of breeds in relation to their geographic origin and use.

Results

From the 101 targeted high-density regions on bovine chromosomes 6, 14, and 25, between 57 and 95% of the SNPs were informative in the individual breeds. The regions of high LD extend up to ~100 kb and the size of haplotype blocks ranges between 30 bases and 75 kb (10.3 kb average). On the scale from 1–100 kb the extent of LD and haplotype block structure in cattle has high similarity to humans. The estimation of effective population sizes over the previous 10,000 generations conforms to two main events in cattle history: the initiation of cattle domestication (~12,000 years ago), and the intensification of population isolation and current population bottleneck that breeds have experienced worldwide within the last ~700 years. Haplotype block density correlation, block boundary discordances, and haplotype sharing analyses were consistent in revealing unexpected similarities between some beef and dairy breeds, making them non-differentiable. Clustering techniques permitted grouping of breeds into different clades given their similarities and dissimilarities in genetic structure.

Conclusion

This work presents the first high-resolution analysis of haplotype block structure in worldwide cattle samples. Several novel results were obtained. First, cattle and human share a high similarity in LD and haplotype block structure on the scale of 1–100 kb. Second, unexpected similarities in haplotype block structure between dairy and beef breeds make them non-differentiable. Finally, our findings suggest that ~30,000 uniformly distributed SNPs would be necessary to construct a complete genome LD map in Bos taurus breeds, and ~580,000 SNPs would be necessary to characterize the haplotype block structure across the complete cattle genome.

Research priorities in spasmodic dysphonia

OBJECTIVE

To identify research priorities to increase understanding of the pathogenesis, diagnosis, and improved treatment of spasmodic dysphonia.

STUDY DESIGN AND SETTING

A multidisciplinary working group was formed that included both scientists and clinicians from multiple disciplines (otolaryngology, neurology, speech pathology, genetics, and neuroscience) to review currently available information on spasmodic dysphonia and to identify research priorities.

RESULTS

Operational definitions for spasmodic dysphonia at different levels of certainty were recommended for diagnosis and recommendations made for a multicenter multidisciplinary validation study.

CONCLUSIONS

The highest priority is to characterize the disorder and identify risk factors that may contribute to its onset. Future research should compare and contrast spasmodic dysphonia with other forms of focal dystonia. Development of animal models is recommended to explore hypotheses related to pathogenesis. Improved understanding of the pathophysiology of spasmodic dysphonia should provide the basis for developing new treatment options and exploratory clinical trials.

SIGNIFICANCE

This document should foster future research to improve the care of patients with this chronic debilitating voice and speech disorder by otolaryngology, neurology, and speech pathology.

Using mtDNA sequences to estimate SNP parameters in ESTs

Discovery of single nucleotide polymorphisms (SNPs) requires analysis of redundant sequences such as those available in large public databases. The ability to detect SNPs, especially those of low frequency, is dependent on the depth and scale of the discovery effort. Large numbers of SNPs have been identified by mining large-scale EST surveys and whole genome sequencing projects. These surveys however are subject to ascertainment bias and the inherent errors in large-scale single pass sequencing efforts. For example, the number of steps involved in the construction and sequencing of cDNA libraries make ESTs highly error prone, resulting in an increased frequency of nonvalid SNPs obtained in these surveys. Sequences of mtDNA genes are often incorporated into cDNA libraries as an artifact of the library construction process and are typically either subtracted from cDNA libraries or are considered superfluous when evaluating the information content of EST datasets. Sequences of mtDNA genes provide a unique resource for the analysis of SNP parameters in EST projects. This study uses sequences from four turkey muscle cDNA libraries to demonstrate how mtDNA sequences gleaned from collections of ESTs can be used to estimate SNP parameters and thus help predict the validity of SNPs.

Extent of genome-wide linkage disequilibrium in Australian Holstein-Friesian cattle based on a high-density SNP panel

BACKGROUND

The extent of linkage disequilibrium (LD) within a population determines the number of markers that will be required for successful association mapping and marker-assisted selection. Most studies on LD in cattle reported to date are based on microsatellite markers or small numbers of single nucleotide polymorphisms (SNPs) covering one or only a few chromosomes. This is the first comprehensive study on the extent of LD in cattle by analyzing data on 1,546 Holstein-Friesian bulls genotyped for 15,036 SNP markers covering all regions of all autosomes. Furthermore, most studies in cattle have used relatively small sample sizes and, consequently, may have had biased estimates of measures commonly used to describe LD. We examine minimum sample sizes required to estimate LD without bias and loss in accuracy. Finally, relatively little information is available on comparative LD structures including other mammalian species such as human and mouse, and we compare LD structure in cattle with public-domain data from both human and mouse.

RESULTS

We computed three LD estimates, D', Dvol and r2, for 1,566,890 syntenic SNP pairs and a sample of 365,400 non-syntenic pairs. Mean D' is 0.189 among syntenic SNPs, and 0.105 among non-syntenic SNPs; mean r2 is 0.024 among syntenic SNPs and 0.0032 among non-syntenic SNPs. All three measures of LD for syntenic pairs decline with distance; the decline is much steeper for r2 than for D' and Dvol. The value of D' and Dvol are quite similar. Significant LD in cattle extends to 40 kb (when estimated as r2) and 8.2 Mb (when estimated as D'). The mean values for LD at large physical distances are close to those for non-syntenic SNPs. Minor allelic frequency threshold affects the distribution and extent of LD. For unbiased and accurate estimates of LD across marker intervals spanning < 1 kb to > 50 Mb, minimum sample sizes of 400 (for D') and 75 (for r2) are required. The bias due to small samples sizes increases with inter-marker interval. LD in cattle is much less extensive than in a mouse population created from crossing inbred lines, and more extensive than in humans.

CONCLUSION

For association mapping in Holstein-Friesian cattle, for a given design, at least one SNP is required for each 40 kb, giving a total requirement of at least 75,000 SNPs for a low power whole-genome scan (median r2 > 0.19) and up to 300,000 markers at 10 kb intervals for a high power genome scan (median r2 > 0.62). For estimation of LD by D' and Dvol with sufficient precision, a sample size of at least 400 is required, whereas for r2 a minimum sample of 75 is adequate.

General aspects of the genetics of SLE

The application of genetic techniques to the study of systemic lupus erythematosus (SLE) has identified candidate genes with diverse immunological function. There is a growing understanding that susceptibility to SLE is due to a complex interaction of multiple genes and environmental factors, and that many of these may be shared with other autoimmune diseases. In this first of a series of review articles we outline our current understanding of SLE genetics, in particular summarising the results of recent association studies.

A review of current approaches to identifying human genes involved in myopia

The prevalence of myopia is high in many parts of the world, particularly among the Orientals such as Chinese and Japanese. Like other complex diseases such as diabetes and hypertension, myopia is likely to be caused by both genetic and environmental factors, and possibly their interactions. Owing to multiple genes with small effects, genetic heterogeneity and phenotypic complexity, the study of the genetics of myopia poses a complex challenge. This paper reviews the current approaches to the genetic analysis of complex diseases and how these can be applied to the identification of genes that predispose humans to myopia. These approaches include parametric linkage analysis, non-parametric linkage analysis like allele-sharing methods and genetic association studies. Basic concepts, advantages and disadvantages of these approaches are discussed and explained using examples from the literature on myopia. Microsatellites and single nucleotide polymorphisms are common genetic markers in the human genome and are indispensable tools for gene mapping. High throughput genotyping of millions of such markers has become feasible and efficient with recent technological advances. In turn, this makes the identification of myopia susceptibility genes a reality.

Genomic and Proteomic Biomarker Discovery in Neurological Disease

Technology for high-throughout scanning of the human genome and its encoded proteins have rapidly developed to allow systematic analyses of human disease. Application of these technologies is becoming an increasingly effective approach for identifying the biological basis of genetically complex neurological diseases. This review will highlight significant findings resulting from the use of a multitude of genomic and proteomic technologies toward biomarker discovery in neurological disorders. Though substantial discoveries have been made, there is clearly significant promise and potential remaining to be fully realized through increasing use of and further development of -omic technologies.

Translational research in late-life mood disorders: implications for future intervention and prevention research

Clinical and epidemiological studies have consistently observed the heterogeneous symptomatology and course of geriatric depression. Given the importance of genetic and environmental risk factors, aging processes, neurodegenerative and cerebrovascular disease processes, and medical comorbidity, the integration of basic and clinical neuroscience research approaches is critical for the understanding of the variability in illness course, as well as the development of prevention and intervention strategies that are more effective. These considerations were the impetus for a workshop, sponsored by the Geriatrics Research Branch in the Division of Adult Translational Research and Treatment Development of the National Institute of Mental Health that was held on September 7-8, 2005. The primary goal of the workshop was to bring together investigators in geriatric psychiatry research with researchers in specific topic areas outside of geriatric mental health to identify priority areas to advance translational research in geriatric depression. As described in this report, the workshop focused on a discussion of the development and application of integrative approaches combining genetics and neuroimaging methods to understand such complex issues as treatment response variability, the role of medical comorbidity in depression, and the potential overlap between depression and dementia. Future directions for integrative research were identified. Understanding the nature of geriatric depression requires the application of translational research and interdisciplinary research approaches. Geriatric depression could serve as a model for translational research integrating basic and clinical neuroscience approaches that would have implications for the study of other neuropsychiatric disorders.

"Higher order" addiction molecular genetics: convergent data from genome-wide association in humans and mice

Family, adoption and twin data each support substantial heritability for addictions. Most of this heritable influence is not substance-specific. The overlapping genetic vulnerability for developing dependence on a variety of addictive substances suggests large roles for "higher order" pharamacogenomics in addiction molecular genetics. We and others have now completed genome-wide association (GWA) studies of DNAs from individuals with dependence on a variety of addictive substances versus appropriate controls. Recently reported replicated GWA observations identify a number of genes based on comparisons between controls and European-American and African-American polysubstance abusers. Here we review the convergence between these results and data that compares control samples and (a) alcohol-dependent European-Americans, (b) methamphetamine-dependent Asians and (c) nicotine dependent samples from European backgrounds. We also compare these human data to quantitative trait locus (QTL) results from studies of addiction-related phenotypes in mice that focus on alcohol, methamphetamine and barbiturates. These comparisons support a genetic architecture built from largely polygenic contributions of common allelic variants to dependence on a variety of legal and illegal substances. Many of the gene variants identified in this way are likely to alter specification and maintenance of neuronal connections.

Genomics-based food-borne pathogen testing and diagnostics: possibilities for the U.S. Department of Agriculture's Food Safety and Inspection Service

The use of genomic technologies at the U.S. Department of Agriculture could enhance inspection, monitoring, and risk assessment capabilities within its Food Safety and Inspection Service (FSIS). Molecular assays capable of detecting hundreds of microbial DNA sequences within a single food sample that identify food-borne pathogens of concern and characterize their traits most relevant to human health risk are of great interest for FSIS. For example, a high-density assay, or combination of assays, could screen FSIS inspected food for pathogens relevant to public health (e.g., Salmonella, Listeria, and toxic E. coli) as well as their associated virulence factors and antibiotic resistance genes. Because most genotype assays can be completed in one working day with a minimum of reagents, use of such assays could potentially save FSIS a significant amount of cost/time for analyses. Further, a genotype assay can detect specific microbial traits relevant to human health risk based on the DNA sequence of toxin producing genes, antibiotic resistance alleles, and more. By combining rapid analysis with specific data on human health risks, information from such high-density genotype assays could provide expanded support for test and hold situations, recalls, outbreak management, and microbial risk assessments (e.g., provide data needed for food-borne illness source attribution). Environ. Mol. Mutagen.

Chromosomal abnormality at 6p25.1-25.3 identifies a susceptibility locus for hypothalamic hamartoma associated with epilepsy

The pathogenesis of hypothalamic hamartoma (HH) associated with epilepsy is unknown. We have identified an individual with HH and refractory epilepsy exhibiting subtle dysmorphic features. High-resolution karyotype identified a duplication of the terminal end of 6p (6p25.1-25.3), confirmed by fluorescent in situ-hybridization (FISH). Copy number analysis with high-density (250K) single nucleotide polymorphism (SNP) genotyping microarrays characterized the abnormality as a series of amplified regions between 1.4 Mb and 10.2 Mb, with a small tandem deletion from 8.8 Mb to 9.7 Mb. There are 38 RefSeq genes within the duplicated regions, and no known coding sequences within the deletion. This unique patient helps identify 6p25.1-25.3 as a possible susceptibility locus for sporadic HH.

Single-nucleotide polymorphisms in the COL1A1 regulatory regions are associated with otosclerosis

Otosclerosis (MIM 166800) has a prevalence of 0.2-1% among white adults, making it the single most common cause of hearing impairment in this ethnic group. Although measles virus, hormones, human leukocyte antigen alleles and genetic factors have been implicated in the development of otosclerosis, its etiology remains unknown. In a focused effort to identify genetic factors in otosclerosis, we have mapped four disease loci (MIM 166800/605727/608244/608787); however, cloning the disease-causing genes in these intervals has not been successful. Here, we used a case-control study design to investigate the association between collagen type I genes and otosclerosis. We identified susceptibility and protective haplotypes in COL1A1 that are significantly associated with otosclerosis in the Caucasian population. These haplotypes alter reporter gene activity in an osteoblast cell line by affecting binding of transcription factors to cis-acting elements. Our data suggest that increased amounts of collagen alpha1(I) homotrimers are causally related to the development of otosclerosis. Consistent with this hypothesis, mouse mutants homozygous for a Col1a2 frameshift mutation on a C57BL/6J background that deposit only homotrimeric type I collagen have hearing loss.

Linking brain imaging and genomics in the study of Alzheimer's disease and aging

My colleagues and I have been using positron emission tomography (PET) and magnetic resonance imaging (MRI) to detect and track the brain changes associated with Alzheimer's disease (AD) and normal brain aging in cognitively normal persons with two copies, one copy, and no copies of the apolipoprotein E (APOE) epsilon4 allele, a common AD susceptibility gene. In this review article, I consider how brain imaging techniques could be used to evaluate putative AD prevention therapies in cognitively normal APOE epsilon4 carriers and putative age-modifying therapies in cognitively normal APOE epsilon4 noncarriers, how they could help investigate the individual and aggregate effects of putative AD risk modifiers, and how they could help guide the investigation of a molecular mechanism associated with AD vulnerability and normal neurological aging. I suggest how high-resolution genome-wide genetic and transcriptomic studies could further help in the scientific understanding of AD, aging, and other common and genetically complex phenotypes, such as variation in normal human memory performance, and in the discovery and evaluation of promising treatments for these phenotypes. Finally, I illustrate the push-pull relationship between brain imaging, genomics research, and other neuroscientific research in the study of AD and aging.

A primary assembly of a bovine haplotype block map based on a 15,036-single-nucleotide polymorphism panel genotyped in holstein-friesian cattle

Analysis of data on 1000 Holstein-Friesian bulls genotyped for 15,036 single-nucleotide polymorphisms (SNPs) has enabled genomewide identification of haplotype blocks and tag SNPs. A final subset of 9195 SNPs in Hardy-Weinberg equilibrium and mapped on autosomes on the bovine sequence assembly (release Btau 3.1) was used in this study. The average intermarker spacing was 251.8 kb. The average minor allele frequency (MAF) was 0.29 (0.05-0.5). Following recent precedents in human HapMap studies, a haplotype block was defined where 95% of combinations of SNPs within a region are in very high linkage disequilibrium. A total of 727 haplotype blocks consisting of > or =3 SNPs were identified. The average block length was 69.7 +/- 7.7 kb, which is approximately 5-10 times larger than in humans. These blocks comprised a total of 2964 SNPs and covered 50,638 kb of the sequence map, which constitutes 2.18% of the length of all autosomes. A set of tag SNPs, which will be useful for further fine-mapping studies, has been identified. Overall, the results suggest that as many as 75,000-100,000 tag SNPs would be needed to track all important haplotype blocks in the bovine genome. This would require approximately 250,000 SNPs in the discovery phase.

Integrating large-scale genotype and phenotype data

With the completion of the Human Genome Project, a new emphasis is focusing on the sequence variation and the resulting phenotype. The number of data available from genomic studies addressing this relationship is rapidly growing. In order to analyze these data as a whole, they need to be integrated, aggregated and annotated in a timely manner. The Pharmacogenetics and Pharmacogenomics Knowledge Base PharmGKB; (<www.pharmgkb.org>) assembles and disseminates these data and their associated metadata that are needed for unambiguous identification and replication. Assembling these data in a timely manner is challenging, and the scalability of these data produce major challenges for a knowledge base such as PharmGKB. However, it is only through rapid global meta-annotation of these data that we will understand the relationship between specific genotype(s) and the related phenotype. PharmGKB has confronted these challenges, and these experiences and solutions can benefit all genome communities.

Identification of the genetic basis for complex disorders by use of pooling-based genomewide single-nucleotide-polymorphism association studies

We report the development and validation of experimental methods, study designs, and analysis software for pooling-based genomewide association (GWA) studies that use high-throughput single-nucleotide-polymorphism (SNP) genotyping microarrays. We first describe a theoretical framework for establishing the effectiveness of pooling genomic DNA as a low-cost alternative to individually genotyping thousands of samples on high-density SNP microarrays. Next, we describe software called "GenePool," which directly analyzes SNP microarray probe intensity data and ranks SNPs by increased likelihood of being genetically associated with a trait or disorder. Finally, we apply these methods to experimental case-control data and demonstrate successful identification of published genetic susceptibility loci for a rare monogenic disease (sudden infant death with dysgenesis of the testes syndrome), a rare complex disease (progressive supranuclear palsy), and a common complex disease (Alzheimer disease) across multiple SNP genotyping platforms. On the basis of these theoretical calculations and their experimental validation, our results suggest that pooling-based GWA studies are a logical first step for determining whether major genetic associations exist in diseases with high heritability.

SNiPer-HD: improved genotype calling accuracy by an expectation-maximization algorithm for high-density SNP arrays

MOTIVATION

The technology to genotype single nucleotide polymorphisms (SNPs) at extremely high densities provides for hypothesis-free genome-wide scans for common polymorphisms associated with complex disease. However, we find that some errors introduced by commonly employed genotyping algorithms may lead to inflation of false associations between markers and phenotype.

RESULTS

We have developed a novel SNP genotype calling program, SNiPer-High Density (SNiPer-HD), for highly accurate genotype calling across hundreds of thousands of SNPs. The program employs an expectation-maximization (EM) algorithm with parameters based on a training sample set. The algorithm choice allows for highly accurate genotyping for most SNPs. Also, we introduce a quality control metric for each assayed SNP, such that poor-behaving SNPs can be filtered using a metric correlating to genotype class separation in the calling algorithm. SNiPer-HD is superior to the standard dynamic modeling algorithm and is complementary and non-redundant to other algorithms, such as BRLMM. Implementing multiple algorithms together may provide highly accurate genotyping calls, without inflation of false positives due to systematically miss-called SNPs. A reliable and accurate set of SNP genotypes for increasingly dense panels will eliminate some false association signals and false negative signals, allowing for rapid identification of disease susceptibility loci for complex traits.

AVAILABILITY

SNiPer-HD is available at TGen's website: http://www.tgen.org/neurogenomics/data.

Common Kibra alleles are associated with human memory performance

Human memory is a polygenic trait. We performed a genome-wide screen to identify memory-related gene variants. A genomic locus encoding the brain protein KIBRA was significantly associated with memory performance in three independent, cognitively normal cohorts from Switzerland and the United States. Gene expression studies showed that KIBRA was expressed in memory-related brain structures. Functional magnetic resonance imaging detected KIBRA allele-dependent differences in hippocampal activations during memory retrieval. Evidence from these experiments suggests a role for KIBRA in human memory.

Case-control studies in the genomic era: a clinician's guide

The goal of case-control association studies is to find genetic variants in the human genome that influence common traits. The Human Genome and HapMap projects have added fresh impetus to this goal by cataloguing the raw genetic data behind human DNA variation. Studies that associate these genetic variants with phenotype improve both molecular diagnostics and drug discovery and offer clinicians important opportunities to improve care of patients. In this review I focus on case-control studies, which are the most widely used design and expected to be the most powerful. I also address the problem of case-control non-replication, which is widespread despite enormous effort and use of resources. Important causes of non-replication include inadequate statistical power to detect small and moderate effects, phenotype heterogeneity, population stratification, publication bias, and multiple comparison testing.

Application of oligonucleotide arrays to high-content genetic analysis

The scope of single nucleotide polymorphism genotyping for genetic association studies has expanded recently from the use of relatively small numbers of candidate genes and markers, to include hypothesis-free, whole-genome approaches using hundreds of thousands of polymorphisms. The ability to perform such large-scale association studies has been dependant on the development of highly parallel and cost-effective genotyping platforms, of which those based on oligonucleotide arrays have proved to be the most scalable and widely adopted. It is to be expected that the new array-based genotyping methods will not only greatly expand the scope of genetic studies, but, as further content is added to arrays, will also form part of an integrated set of DNA, RNA and proteomic analyses enabling the detailed, multilayered study of complex disease-linked phenotypes.

The genetics of depression: a review

Major depressive disorder (MDD) is common and moderately heritable. Recurrence and early age at onset characterize cases with the greatest familial risk. Major depressive disorder and the neuroticism personality trait have overlapping genetic susceptibilities. Most genetic studies of MDD have considered a small set of functional polymorphisms relevant to monoaminergic neurotransmission. Meta-analyses suggest small positive associations between the polymorphism in the serotonin transporter promoter region (5-HTTLPR) and bipolar disorder, suicidal behavior, and depression-related personality traits but not yet to MDD itself. This polymorphism might also influence traits related to stress vulnerability. Newer hypotheses of depression neurobiology suggest closer study of genes related to neurotoxic and neuroprotective (neurotrophic) processes and to overactivation of the hypothalamic-pituitary axis, with mixed evidence regarding association of MDD with polymorphisms in one such gene (brain-derived neurotrophic factor [BDNF]). Several genome-wide linkage studies of MDD and related traits have been reported or are near completion. There is some evidence for convergence of linkage findings across studies, but more data are needed to permit meta-analysis. Future directions will include more intensive, systematic study of linkage candidate regions and of the whole genome for genetic association; gene expression array studies; and larger-scale studies of gene-environment interactions and of depression-related endophenotypes.

Human monogenic disorders — a source of novel drug targets

The decrease in new drug applications and approvals over the past several years results from an underlying crisis in drug target identification and validation. Model organisms are being used to address this problem, in combination with novel approaches such as the International HapMap Project. What has been underappreciated is that discovery of new drug targets can also be revived by traditional Mendelian genetics. A large fraction of the human gene repertoire remains phenotypically uncharacterized, and is likely to encode many unanticipated and novel phenotypes that will be of interest to pharmaceutical and biotechnological drug developers.

Whole-genome genotyping with the single-base extension assay

We describe an efficient, accurate and robust whole-genome genotyping (WGG) assay based on a two-color, single-base extension (SBE), single-nucleotide polymorphism (SNP)-scoring step. We report genotyping results for biallelic International HapMap quality control (QC) SNPs using a single probe per locus. We show scalability, throughput and accuracy of the system by resequencing homozygous loci from our 100k Human-1 Genotyping BeadChip.

Identification of Base Pairs in Single-Nucleotide Polymorphisms by MutS Protein-Mediated Capillary Electrophoresis

Single-nucleotide polymorphisms (SNPs) are widespread genomic variations, which are associated with serious health disorders and drug resistance. Multiple clinical applications and studies of global population genetics require fast and informative analysis of SNPs. Most of conventional methods sense the presence of the SNP but cannot identify the base pair in it. Here we report simple identification of base pairs in SNPs without DNA sequencing. Our approach is based on the unique ability of MutS protein to bind different single-nucleotide mismatches in DNA with different affinities. Conceptually, the DNA in question is mixed with reference DNA, melted, and reannealed. If the DNA in question has an SNP, the products of reannealing will have two different single-nucleotide mismatches, which provide a base-pair-specific signature of the SNP. The products of reannealing are mixed with MutS, equilibrated, and separated by equilibrium capillary electrophoresis of equilibrium mixtures with MutS in the run buffer. The pattern of migration times of DNAs with mismatches is used for unequivocal identification of the base pair in the SNP. In addition to its ability to identify base pairs in SNPs, the new analytical approach is fast, simple, highly sensitive, and requires no quantitation. It will find applications in studies of heterogeneity of base pairs in known SNPs in large human populations.

SNiPer: improved SNP genotype calling for Affymetrix 10K GeneChip microarray data

BACKGROUND

High throughput microarray-based single nucleotide polymorphism (SNP) genotyping has revolutionized the way genome-wide linkage scans and association analyses are performed. One of the key features of the array-based GeneChip Mapping 10K Array from Affymetrix is the automated SNP calling algorithm. The Affymetrix algorithm was trained on a database of ethnically diverse DNA samples to create SNP call zones that are used as static models to make genotype calls for experimental data. We describe here the implementation of clustering algorithms on large training datasets resulting in improved SNP call rates on the 10K GeneChip.

RESULTS

A database of 948 individuals genotyped on the GeneChip Mapping 10K 2.0 Array was used to identify 822 SNPs that were called consistently less than 75% of the time. These SNPs represent on average 8.25% of the total SNPs on each chromosome with chromosome 19, the most gene-rich chromosome, containing the highest proportion of poor performers (18.7%). To remedy this, we created SNiPer, a new application which uses two clustering algorithms to yield increased call rates and equivalent concordance to Affymetrix called genotypes. We include a training set for these algorithms based on individual genotypes for 705 samples. SNiPer has the capability to be retrained for lab-specific training sets. SNiPer is freely available for download at http://www.tgen.org/neurogenomics/data.

CONCLUSION

The correct calling of poor performing SNPs may prove to be key in future linkage studies performed on the 10K GeneChip. It would prove particularly invaluable for those diseases that map to chromosome 19, known to contain a high proportion of poorly performing SNPs. Our results illustrate that SNiPer can be used to increase call rates on the 10K GeneChip without sacrificing accuracy, thereby increasing the amount of valid data generated.