How many SNPs does a genome-wide haplotype map require?

Identification of Candidate Genes for Salinity and Anaerobic Tolerance at the Germination Stage in Rice by Genome-Wide Association Analyses

Multiple stress tolerance at the seed germination stage is crucial for better crop establishment in the direct-seeded rice ecosystem. Therefore, identifying rice genes/quantitative trait loci (QTLs) associated with salinity and anaerobic tolerance at the germination stage is a prerequisite for adaptive breeding. Here, we studied 498 highly diverse rice accessions Xian (Indica) and Geng (Japonica), and six traits that are highly associated with salinity and anaerobic tolerance at germination stage were measured. A high-density 2.8M Single Nucleotide Polymorphisms (SNP) genotype map generated from the 3,000 Rice Genomes Project (3KRGP) was used for mapping through a genome-wide association study. In total, 99 loci harboring 117 QTLs were detected in different populations, 54, 21, and 42 of which were associated with anaerobic, salinity, and combined (anaerobic and salinity) stress tolerance. Nineteen QTLs were close to the reported loci for abiotic stress tolerance, whereas two regions on chromosome 4 (qSGr4a/qCL4c/qRI4d and qAGr4/qSGr4b) and one region on chromosome 10 (qRI10/qCL10/ qSGr10b/qBM10) were associated with anaerobic and salinity related traits. Further haplotype analysis detected 25 promising candidates genes significantly associated with the target traits. Two known genes (OsMT2B and OsTPP7) significantly associated with grain yield and its related traits under saline and anaerobic stress conditions were identified. In this study, we identified the genes involved in auxin efflux (Os09g0491740) and transportation (Os01g0976100), whereas we identified multistress responses gene OsMT2B (Os01g0974200) and a major gene OsTPP7 (Os09g0369400) involved in anaerobic germination and coleoptile elongation on chromosome 9. These promising candidates provide valuable resources for validating potential salt and anaerobic tolerance genes and will facilitate direct-seeded rice breeding for salt and anaerobic tolerance through marker-assisted selection or gene editing.

Discovering Genome-Wide Tag SNPs Based on the Mutual Information of the Variants

Exploring linkage disequilibrium (LD) patterns among the single nucleotide polymorphism (SNP) sites can improve the accuracy and cost-effectiveness of genomic association studies, whereby representative (tag) SNPs are identified to sufficiently represent the genomic diversity in populations. There has been considerable amount of effort in developing efficient algorithms to select tag SNPs from the growing large-scale data sets. Methods using the classical pairwise-LD and multi-locus LD measures have been proposed that aim to reduce the computational complexity and to increase the accuracy, respectively. The present work solves the tag SNP selection problem by efficiently balancing the computational complexity and accuracy, and improves the coverage in genomic diversity in a cost-effective manner. The employed algorithm makes use of mutual information to explore the multi-locus association between SNPs and can handle different data types and conditions. Experiments with benchmark HapMap data sets show comparable or better performance against the state-of-the-art algorithms. In particular, as a novel application, the genome-wide SNP tagging is performed in the 1000 Genomes Project data sets, and produced a well-annotated database of tagging variants that capture the common genotype diversity in 2,504 samples from 26 human populations. Compared to conventional methods, the algorithm requires as input only the genotype (or haplotype) sequences, can scale up to genome-wide analyses, and produces accurate solutions with more information-rich output, providing an improved platform for researchers towards the subsequent association studies.

Polymorphisms in maternal and fetal genes encoding for proteins involved in extracellular matrix metabolism alter the risk for small-for-gestational-age

OBJECTIVE

To examine the association between maternal and fetal genetic variants and small-for-gestational-age (SGA).

METHODS

A case-control study was conducted in patients with SGA neonates (530 maternal and 436 fetal) and controls (599 maternal and 628 fetal); 190 candidate genes and 775 SNPs were studied. Single-locus, multi-locus and haplotype association analyses were performed on maternal and fetal data with logistic regression, multifactor dimensionality reduction (MDR) analysis, and haplotype-based association with 2 and 3 marker sliding windows, respectively. Ingenuity pathway analysis (IPA) software was used to assess pathways that associate with SGA.

RESULTS

The most significant single-locus association in maternal data was with a SNP in tissue inhibitor of metalloproteinase 2 (TIMP2) (rs2277698 OR = 1.71, 95% CI [1.26-2.32], p = 0.0006) while in the fetus it was with a SNP in fibronectin 1 isoform 3 preproprotein (FN1) (rs3796123, OR = 1.46, 95% CI [1.20-1.78], p = 0.0001). Both SNPs were adjusted for potential confounders (maternal body mass index and fetal sex). Haplotype analyses resulted in associations in α 1 type I collagen preproprotein (COL1A1, rs1007086-rs2141279-rs17639446, global p = 0.006) in mothers and FN1 (rs2304573-rs1250204-rs1250215, global p = 0.045) in fetuses. Multi-locus analyses with MDR identified a two SNP model with maternal variants collagen type V α 2 (COL5A2) and plasminogen activator urokinase (PLAU) predicting SGA outcome correctly 59% of the time (p = 0.035).

CONCLUSIONS

Genetic variants in extracellular matrix-related genes showed significant single-locus association with SGA. These data are consistent with other studies that have observed elevated circulating fibronectin concentrations in association with increased risk of SGA. The present study supports the hypothesis that DNA variants can partially explain the risk of SGA in a cohort of Hispanic women.

A genetic association study of maternal and fetal candidate genes that predispose to preterm prelabor rupture of membranes (PROM)

OBJECTIVE

We sought to determine whether maternal/fetal single-nucleotide polymorphisms (SNPs) in candidate genes are associated with preterm prelabor rupture of membranes (pPROM).

STUDY DESIGN

A case-control study was conducted in patients with pPROM (225 mothers and 155 fetuses) and 599 mothers and 628 fetuses with a normal pregnancy; 190 candidate genes and 775 SNPs were studied. Single locus/haplotype association analyses were performed; false discovery rate was used to correct for multiple testing (q* = 0.15).

RESULTS

First, a SNP in tissue inhibitor of metalloproteinase 2 in mothers was significantly associated with pPROM (odds ratio, 2.12; 95% confidence interval, 1.47-3.07; P = .000068), and this association remained significant after correction for multiple comparisons. Second, haplotypes for Alpha 3 type IV collagen isoform precursor in the mother were associated with pPROM (global P = .003). Third, multilocus analysis identified a 3-locus model, which included maternal SNPs in collagen type I alpha 2, defensin alpha 5 gene, and endothelin 1.

CONCLUSION

DNA variants in a maternal gene involved in extracellular matrix metabolism doubled the risk of pPROM.

Identification of fetal and maternal single nucleotide polymorphisms in candidate genes that predispose to spontaneous preterm labor with intact membranes

OBJECTIVE

The purpose of this study was to determine whether maternal/fetal single nucleotide polymorphisms (SNPs) in candidate genes are associated with spontaneous preterm labor/delivery.

STUDY DESIGN

A genetic association study was conducted in 223 mothers and 179 fetuses (preterm labor with intact membranes who delivered <37 weeks of gestation [preterm birth (PTB)]), and 599 mothers and 628 fetuses (normal pregnancy); 190 candidate genes and 775 SNPs were studied. Single locus/haplotype association analyses were performed; the false discovery rate was used to correct for multiple testing.

RESULTS

The strongest single locus associations with PTB were interleukin-6 receptor 1 (fetus; P=.000148) and tissue inhibitor of metalloproteinase 2 (mother; P=.000197), which remained significant after correction for multiple comparisons. Global haplotype analysis indicated an association between a fetal DNA variant in insulin-like growth factor F2 and maternal alpha 3 type IV collagen isoform 1 (global, P=.004 and .007, respectively).

CONCLUSION

An SNP involved in controlling fetal inflammation (interleukin-6 receptor 1) and DNA variants in maternal genes encoding for proteins involved in extracellular matrix metabolism approximately doubled the risk of PTB.

An utter refutation of the "fundamental theorem of the HapMap"

The International HapMap Project was proposed in order to quantify linkage disequilibrium (LD) relationships among human DNA polymorphisms in an assortment of populations, in order to facilitate the process of selecting a minimal set of markers that could capture most of the signal from the untyped markers in a genome-wide association study. The central dogma can be summarized by the argument that if a marker is in tight LD with a polymorphism that directly impacts disease risk, as measured by the metric r(2), then one would be able to detect an association between the marker and disease with sample size that was increased by a factor of 1/r(2) over that needed to detect the effect of the functional variant directly. This "fundamental theorem" holds, however, only if one assumes that the LD between loci and the etiological effect of the functional variant are independent of each other, that they are statistically independent of all other etiological factors (in exposure and action), that sampling is prospective, and that the estimates of r(2) are accurate. None of these are standard operating assumptions, however. We describe the ramifications of these implicit assumptions, and provide simple examples in which the effects of a functional variant could be unequivocally detected if it were directly genotyped, even as markers in high LD with the functional variant would never show association with disease, even in infinite sample sizes. Both theoretical and empirical refutation of the central dogma of genome-wide association studies is thus presented.

Conceptualizing race in research

BACKGROUND

The use of race as a variable in research continues to spark debate about whether it should be used, as well as the implications it has for research on health differences. Given this continued controversy, we examined how investigators interpret the concept of "race" and whether their views of race are reflected in their published work.

METHODS

Thirty-three semistructured interviews were conducted with investigators from 3 southeastern universities to discuss recruitment of participants, the use of race as a variable in research and analyses, and their assessment of the National Institutes of Health mandate on the inclusion of women and minorities. The interview data were analyzed using the principles of constant comparative method, theme identification and pattern investigation. Up to 2 publications for each respondent were also used to assess the use of race in their research.

RESULTS

Results reflect a spectrum of views on the definition of race, from biological to social. Findings also suggest that investigators think critically about the use and implications of using race in their research, although this is not consistently reflected in their published work.

CONCLUSION

In our view, authors, journal editors and peer reviewers have an important role in moving this debate forward, and advocate that they engage more directly in shaping the process. When reporting results by race, investigators should provide a statement on the theory or conceptual framework underlying the hypothesized racial differences in health examined in the study. They should be also cautious in invoking either biological or social constructions of race, thus demonstrating an appreciation of the nuances and implications of using this variable.

[Toward a non-empirical treatment for rheumatoid arthritis based on its molecular pathology]

Rheumatoid arthritis (RA) is a chronic, disabbling disease that affects individuals during the productive years of their lives. Modern treatment for RA includes the so called "biologic" therapy, which is based on recombinant proteins that modify the biologic processes. These agents have potent therapeutic effects and different mechanisms of action. Nevertheless, therapeutic failure still prevails. Treatment that prevents disability in RA must be started in an early manner, before the development of complications and, ideally, with a minimum possibility of therapeutic failure. As yet, there are no clinical or laboratory criteria to identify those patients with a higher probability of responding to particular types of therapy, delaying control of RA ad affecting the prevention of incapacity. Research into gene diversity through single-nucleotide polymorphisms (SNPs) by means of microarray systems, allows the detailed analysis of gene factors associated to a given disease. SNPs have been recently applied to the study of RA, where the major polymorphisms associated to RA occur primarily in genes that code for proteins related to the initiation of an immune response and/or the control of cellular activity in the immune system, in addition to genes related to tissue repair. The specific meaning of these findings is in its initial stages of research. On the other hand, proteomics relate to the analysis of protein expression profiles at multiple levels. Both types of studies will contribute to the knowledge of patterns of gene expression in RA compared to the general population, and will allow an understanding of the pathogenesis of RA. Moreover, proteomic and genomic profiles can be employed to designs probes that identify individuals with the risk of developing RA, individually predict the response to different therapeutic modalities (pharmacogenomics) and for the follow-up of the biologic response to therapy.

SNP genotyping: technologies and biomedical applications

Single nucleotide polymorphisms (SNPs) are the most frequently occurring genetic variation in the human genome, with the total number of SNPs reported in public SNP databases currently exceeding 9 million. SNPs are important markers in many studies that link sequence variations to phenotypic changes; such studies are expected to advance the understanding of human physiology and elucidate the molecular bases of diseases. For this reason, over the past several years a great deal of effort has been devoted to developing accurate, rapid, and cost-effective technologies for SNP analysis, yielding a large number of distinct approaches. This article presents a review of SNP genotyping techniques and examines their principles of genotype determination in terms of allele differentiation strategies and detection methods. Further, several current biomedical applications of SNP genotyping are discussed.

An entropy-based approach for testing genetic epistasis underlying complex diseases

The genetic basis of complex diseases is expected to be highly heterogeneous, with complex interactions among multiple disease loci and environment factors. Due to the multi-dimensional property of interactions among large number of genetic loci, efficient statistical approach has not been well developed to handle the high-order epistatic complexity. In this article, we introduce a new approach for testing genetic epistasis in multiple loci using an entropy-based statistic for a case-only design. The entropy-based statistic asymptotically follows a chi(2) distribution. Computer simulations show that the entropy-based approach has better control of type I error and higher power compared to the standard chi(2) test. Motivated by a schizophrenia data set, we propose a method for measuring and testing the relative entropy of a clinical phenotype, through which one can test the contribution or interaction of multiple disease loci to a clinical phenotype. A sequential forward selection procedure is proposed to construct a genetic interaction network which is illustrated through a tree-based diagram. The network information clearly shows the relative importance of a set of genetic loci on a clinical phenotype. To show the utility of the new entropy-based approach, it is applied to analyze two real data sets, a schizophrenia data set and a published malaria data set. Our approach provides a fast and testable framework for genetic epistasis study in a case-only design.

Cancer development and progression

Cancer development and progression is a complex process that involves a host of functional and genetic abnormalities. Genomic perturbations and the gene expression they lead to, can now be globally identified with the use of DNA microarray. This relatively new technology has forever changed the scale of biological investigation. The enormous amount of data generated via a single chip has led to major global studies of the cellular processes underlying malignant transformation and progression. The multiplicity of platforms from different proprietors has offered investigators flexibility in their experimental design. Additionally, there are several more recent microarrays whose designs were inspired by the nucleotide-based technology. These include protein, multi-tissue, cell, and interference RNA microarrays. Combinations of microarray and other contemporary scientific methods, such as, laser capture microdissection (LCM), comparative genomic hybridization (CGH), single nucleotide polymorphism analysis (SNP) and chromatin immunoprecipitation (ChIP), have created entirely new fields of interest in the more global quest to better define the molecular basis of malignancy. In addition to basic science applications, many clinical inquiries have been performed. These queries have shown microarray to have clinical utility in cancer diagnosis, risk stratification, and patient management.

Informative SNP selection methods based on SNP prediction

The search for the association between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. For these studies, it is essential to use a small subset of informative SNPs, i.e., tag SNPs, accurately representing the rest of the SNPs. Tag SNP selection can achieve: 1) considerable budget savings by genotyping only a limited number of SNPs and computationally inferring all other SNPs or 2) necessary reduction of the huge SNP sets (obtained, e.g., from Affymetrix) for further fine haplotype analysis. In this paper, we show that the tag SNP selection strongly depends on how the chosen tags will be used-advantage of one tag set over another can only be considered with respect to a certain prediction method. We show how to separate tag selection from SNP prediction and propose greedy and local-minimization algorithms for tag SNP selection. We give two novel approaches to SNP prediction based on multiple linear regression (MLR) and support vector machines (SVMs). An extensive experimental study on various datasets including ten regions from hapMap project shows that the MLR prediction combined with stepwise tag selection uses fewer tags than the state-of-the-art method of Halperin et al. The MLR-based method also uses on average 30% fewer tags than IdSelect for statistical covering all SNPs. The tag selection based on SVM SNP prediction uses fewer tags to achieve the same prediction accuracy as the methods of Halldorsson et al.

TAMGeS: a Three-array Method for Genotyping of SNPs by a dual-colour approach

Background

Many of the most effective high-throughput protocols for SNP genotyping employ microarrays. Genotypes are assessed by comparing the signal intensities that derive from the hybridization of different allele-specific probes labelled either by using four fluorescent dyes, one for each base, or by using only two dyes and investigating the polymorphic alleles two by two on separate arrays. The employment of only two dyes makes it possible to use a dual-laser scanner, which has the advantage of being present in every microarray laboratory. However, this protocol may present some drawbacks. To infer all the six possible genotypes it is necessary to compare signals from two arrays, but this comparison not always is successful. A number of systematic errors in the experimental protocol, in fact, may differently affect signal intensities on separate arrays. Here we present TAMGeS (Three-Array Method for Genotyping of SNPs), an exhaustive method for SNP genotyping through SBE (Single Base Extension) and dual-colour microarrays, which makes the comparison of signals on distinct arrays reliable by using a third array and a data handling method for signal normalization based on bilinear regression theory.

Results

We tested the effectiveness of the proposed method by evaluating the results obtained from the direct comparison of the two arrays or by applying TAMGeS, both on experimental and synthetic data. With synthetic data, TAMGeS reduced the frequency of errors by an order of magnitude, when the incidence of systematic errors was not negligible. With the experimental data, produced by genotyping 25 SNPs in 437 subjects, TAMGeS reduced the percentage of missing genotypes from 54% (Two-Array Method) to 14.5%. Allelic and genotypic call rates were 99.3% and 99.5%, respectively. The normalization procedure takes into account also systematic errors, which can be generated by a time-delayed assay, thus making the protocol more flexible.

Conclusion

TAMGeS represents an innovative method, which proved to be very effective in producing reliable SNP genotyping data by dual-colour microarrays. The requirement of a third array is well balanced by the strong enhancement in data quality and by the greater flexibility of the experimental protocol.

Candidate-gene association study of mothers with pre-eclampsia, and their infants, analyzing 775 SNPs in 190 genes

Pre-eclampsia (PE) affects 5-7% of pregnancies in the US, and is a leading cause of maternal death and perinatal morbidity and mortality worldwide. To identify genes with a role in PE, we conducted a large-scale association study evaluating 775 SNPs in 190 candidate genes selected for a potential role in obstetrical complications. SNP discovery was performed by DNA sequencing, and genotyping was carried out in a high-throughput facility using the MassARRAY(TM) System. Women with PE (n = 394) and their offspring (n = 324) were compared with control women (n = 602) and their offspring (n = 631) from the same hospital-based population. Haplotypes were estimated for each gene using the EM algorithm, and empirical p values were obtained for a logistic regression-based score test, adjusted for significant covariates. An interaction model between maternal and offspring genotypes was also evaluated. The most significant findings for association with PE were COL1A1 (p = 0.0011) and IL1A (p = 0.0014) for the maternal genotype, and PLAUR (p = 0.0008) for the offspring genotype. Common candidate genes for PE, including MTHFR and NOS3, were not significantly associated with PE. For the interaction model, SNPs within IGF1 (p = 0.0035) and IL4R (p = 0.0036) gave the most significant results. This study is one of the most comprehensive genetic association studies of PE to date, including an evaluation of offspring genotypes that have rarely been considered in previous studies. Although we did not identify statistically significant evidence of association for any of the candidate loci evaluated here after adjusting for multiple testing using the false discovery rate, additional compelling evidence exists, including multiple SNPs with nominally significant p values in COL1A1 and the IL1A region, and previous reports of association for IL1A, to support continued interest in these genes as candidates for PE. Identification of the genetic regulators of PE may have broader implications, since women with PE are at increased risk of death from cardiovascular diseases later in life.

The genetics of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease caused by the interaction of genetic susceptibility and environmental influences. There is increasing evidence that genes link to disease pathogenesis and heterogeneity by causing variation in protease anti-protease systems, defence against oxidative stress and inflammation. The main methods of genomic research for complex disease traits are described, together with the genes implicated in COPD thus far, their roles in disease causation and the future for this area of investigation.

An initial map of insertion and deletion (INDEL) variation in the human genome

Although many studies have been conducted to identify single nucleotide polymorphisms (SNPs) in humans, few studies have been conducted to identify alternative forms of natural genetic variation, such as insertion and deletion (INDEL) polymorphisms. In this report, we describe an initial map of human INDEL variation that contains 415,436 unique INDEL polymorphisms. These INDELs were identified with a computational approach using DNA re-sequencing traces that originally were generated for SNP discovery projects. They range from 1 bp to 9989 bp in length and are split almost equally between insertions and deletions, relative to the chimpanzee genome sequence. Five major classes of INDELs were identified, including (1) insertions and deletions of single-base pairs, (2) monomeric base pair expansions, (3) multi-base pair expansions of 2-15 bp repeat units, (4) transposon insertions, and (5) INDELs containing random DNA sequences. Our INDELs are distributed throughout the human genome with an average density of one INDEL per 7.2 kb of DNA. Variation hotspots were identified with up to 48-fold regional increases in INDEL and/or SNP variation compared with the chromosomal averages for the same chromosomes. Over 148,000 INDELs (35.7%) were identified within known genes, and 5542 of these INDELs were located in the promoters and exons of genes, where gene function would be expected to be influenced the greatest. All INDELs in this study have been deposited into dbSNP and have been integrated into maps of human genetic variation that are available to the research community.

Pharmacogenomics

So far no pharmacogenetic/genomic study has been conducted specifically for anxiety disorders. Some of the presented results, however, do pertain to such disorders. For example, pharmacokinetic aspects of antidepressant drug therapy likely also apply to patients with anxiety disorders, and several genetic polymorphisms in the cytochrome P450 (CYP) gene family and drug transporter molecules, such as the multidrug resistance (MDR) gene type 1, have been reported to influence the pharmacokinetics of antidepressant drugs. At this stage of pharmacogenomics research, it is difficult to interpret the relevance of pharmacodynamic-genetic association studies conducted in depressed patients for anxiety disorders. A number of studies have reported an influence of polymorphisms of genes mostly in the serotonergic pathway on the response to antidepressant drugs in patients suffering from depression. In order to know whether they can be extrapolated to patients with anxiety disorders, clinical studies are warranted. Despite all the shortcomings of the currently available pharmacogenetic studies, this field holds great promise for the treatment of anxiety disorders. In the future, psychiatrists may be able to base treatment decisions (i.e., the type and dose of prescribed drug) on more objective parameters than only the diagnostic algorithms used now. This will limit unwanted side effects and adverse drug reactions, and could reduce time to response, resulting in a more individualized pharmacotherapy.

Multiple linear regression for index SNP selection on unphased genotypes

The search for the association between complex diseases and single nucleotide polymorphism (SNPs) or haplotypes has recently received great attention. Recent successes in high throughput genotyping technologies drastically increase the length of available SNP sequences. This elevates the importance for the use of a small subset of informative SNPs, called index SNPs, accurately representing the rest of the SNPs (i.e., the rest of the SNPs can be highly predicted from the index SNPs). Index SNP selection achieves the compaction of huge unphased genotype data (obtained, e.g., from Affimetrix Map Array) in order to make feasible fine genotype analysis. In this paper we propose a novel index SNP selection on unphased genotypes based on multiple linear regression (MLR) SNP prediction. We measure the quality of our index SNP selection algorithm by comparing actual SNPs with the SNPs computationally predicted from chosen index SNPs. We obtain an extremely good prediction rates and compression. For example, for region ENm010 (123 SNPs), we can use 2% of SNPs for representing all SNPs with 93.5% accuracy. An experimental study on 4 ENCODE regions from HapMap shows that our method uses significantly fewer index SNPs (e.g., up to two times less index SNPs to reach 90% prediction accuracy) than the state-of-the-art method of Halperin et al. for genotypes.

Strong correlation between meiotic crossovers and haplotype structure in a 2.5-Mb region on the long arm of chromosome 21

Although the haplotype structure of the human genome has been studied in great detail, very little is known about the mechanisms underlying its formation. To investigate the role of meiotic recombination on haplotype block formation, single nucleotide polymorphisms were selected at a high density from a 2.5-Mb region of human chromosome 21. Direct analysis of meiotic recombination by high-throughput multiplex genotyping of 662 single sperm identifies 41 recombinants. The crossovers were nonrandomly distributed within 16 small areas. All, except one, of these crossovers fall in areas where the haplotype structure exhibits breakdown, displaying a strong statistically positive association between crossovers and haplotype block breaks. The data also indicate a particular clustered distribution of recombination hotspots within the region. This finding supports the hypothesis that meiotic recombination makes a primary contribution to haplotype block formation in the human genome.

Shaking the tree: mapping complex disease genes with linkage disequilibrium

Much effort and expense are being spent internationally to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, the technology for detecting and genotyping single nucleotide polymorphisms (SNPs) has undergone rapid development, yielding extensive catalogues of these polymorphisms across the genome. Population-based maps of the correlations amongst SNPs (linkage disequilibrium) are now being developed to accelerate the discovery of genes for complex human diseases. These genomic advances coincide with an increasing recognition of the importance of very large sample sizes for studying genetic effects. Together, these new genetic and epidemiological data hold renewed promise for the identification of susceptibility genes for complex traits. We review the state of knowledge about the structure of the human genome as related to SNPs and linkage disequilibrium, discuss the potential applications of this knowledge to mapping complex disease genes, and consider the issues facing whole genome association scanning using SNPs.

Recent developments in genomewide association scans: a workshop summary and review

With the imminent availability of ultra-high-volume genotyping platforms (on the order of 100,000-1,000,000 genotypes per sample) at a manageable cost, there is growing interest in the possibility of conducting genomewide association studies for a variety of diseases but, so far, little consensus on methods to design and analyze them. In April 2005, an international group of >100 investigators convened at the University of Southern California over the course of 2 days to compare notes on planned or ongoing studies and to debate alternative technologies, study designs, and statistical methods. This report summarizes these discussions in the context of the relevant literature. A broad consensus emerged that the time was now ripe for launching such studies, and several common themes were identified--most notably the considerable efficiency gains of multistage sampling design, specifically those made by testing only a portion of the subjects with a high-density genomewide technology, followed by testing additional subjects and/or additional SNPs at regions identified by this initial scan.

Definition and clinical importance of haplotypes

Advances in genotyping and sequencing technologies, coupled with the development of sophisticated statistical methods, have afforded investigators novel opportunities to define the role of sequence variation in the development of common human diseases. At the forefront of these investigations is the use of dense maps of single-nucleotide polymorphisms (SNPs) and the haplotypes derived from these polymorphisms. Here we review basic concepts of high-density genetic maps of SNPs and haplotypes and how they are typically generated and used in human genetic research. We also provide useful examples and tools available for researchers interested in incorporating haplotypes into their studies. Finally, we discuss the latest concepts for the analysis of haplotypes related to human disease, including haplotype blocks, the International HapMap Project, and the future directions of these resources.

SNP discovery in associating genetic variation with human disease phenotypes

With the completion of the human genome project, attention is now rapidly shifting towards the study of individual genetic variation. The most abundant source of genetic variation in the human genome is represented by single nucleotide polymorphisms (SNPs), which can account for heritable inter-individual differences in complex phenotypes. Identification of SNPs that contribute to susceptibility to common diseases will provide highly accurate diagnostic information that will facilitate early diagnosis, prevention, and treatment of human diseases. Over the past several years, the advancement of increasingly high-throughput and cost-effective methods to discover and measure SNPs has begun to open the door towards this endeavor. Genetic association studies are considered to be an effective approach towards the detection of SNPs with moderate effects, as in most common diseases with complex phenotypes. This requires careful study design, analysis and interpretation. In this review, we discuss genetic association studies and address the prospect for candidate gene association studies, comparing the strengths and weaknesses of indirect and direct study designs. Our focus is on the continuous need for SNP discovery methods and the use of currently available prescreening methods for large-scale genetic epidemiological research until more advanced sequencing methods currently under development will become available.

A genome-wide scalable SNP genotyping assay using microarray technology

Oligonucleotide probe arrays have enabled massively parallel analysis of gene expression levels from a single cDNA sample. Application of microarray technology to analyzing genomic DNA has been stymied by the sequence complexity of the entire human genome. A robust, single base-resolution direct genomic assay would extend the reach of microarray technology. We developed an array-based whole-genome genotyping assay that does not require PCR and enables effectively unlimited multiplexing. The assay achieves a high signal-to-noise ratio by combining specific hybridization of picomolar concentrations of whole genome-amplified DNA to arrayed probes with allele-specific primer extension and signal amplification. As proof of principle, we genotyped several hundred previously characterized SNPs. The conversion rate, call rate and accuracy were comparable to those of high-performance PCR-based genotyping assays.

Systems biology: new approaches to old environmental health problems

The environment plays a pivotal role as a human health determinant and presence of hazardous pollutants in the environment is often implicated in human disease. That pollutants cause human diseases however is often controversial because data connecting exposure to environmental hazards and human diseases are not well defined, except for some cancers and syndromes such as asthma. Understanding the complex nature of human-environment interactions and the role they play in determining the state of human health is one of the more compelling problems in public health. We are becoming more aware that the reductionist approach promulgated by current methods has not, and will not yield answers to the broad questions of population health risk analysis. If substantive applications of environment-gene interactions are to be made, it is important to move to a systems level approach, to take advantage of epidemiology and molecular genomic advances. Systems biology is the integration of genomics, transcriptomics, proteomics, and metabolomics together with computer technology approaches to elucidate environmentally caused disease in humans. We discuss the applications of environmental systems biology as a route to solution of environmental health problems.

Optimal Selection of SNP Markers for Disease Association Studies

Genetic association studies with population samples hold the promise of uncovering the susceptibility genes underlying the heritability of complex or common disease. Most association studies rely on the use of surrogate markers, single-nucleotide polymorphism (SNP) being the most suitable due to their abundance and ease of scoring. SNP marker selection is aimed to increase the chances that at least one typed SNP would be in linkage disequilibrium (LD) with the disease causative variant, while at the same time controlling the cost of the study in terms of the number of markers genotyped and samples. Empirical studies reporting block-like segments in the genome with high LD and low haplotype diversity have motivated a marker selection strategy whereby subsets of SNPs that 'tag' the common haplotypes of a region are picked for genotyping, avoiding typing redundant SNPs. Based on these initial observations, a plethora of 'tagging' algorithms for selecting minimum informative subsets of SNPs has recently appeared in the literature. These differ mostly in two major aspects: the quality or correlation measure used to define tagging and the algorithm used for the minimization of the final number of tagging SNPs. In this review we describe the available tagging algorithms utilizing a 3-step unifying framework, point out their methodological and conceptual differences, and make an assessment of their assumptions, performance, and scalability.

Natural genetic variation caused by transposable elements in humans

Transposons and transposon-like repetitive elements collectively occupy 44% of the human genome sequence. In an effort to measure the levels of genetic variation that are caused by human transposons, we have developed a new method to broadly detect transposon insertion polymorphisms of all kinds in humans. We began by identifying 606,093 insertion and deletion (indel) polymorphisms in the genomes of diverse humans. We then screened these polymorphisms to detect indels that were caused by de novo transposon insertions. Our method was highly efficient and led to the identification of 605 nonredundant transposon insertion polymorphisms in 36 diverse humans. We estimate that this represents 25-35% of approximately 2075 common transposon polymorphisms in human populations. Because we identified all transposon insertion polymorphisms with a single method, we could evaluate the relative levels of variation that were caused by each transposon class. The average human in our study was estimated to harbor 1283 Alu insertion polymorphisms, 180 L1 polymorphisms, 56 SVA polymorphisms, and 17 polymorphisms related to other forms of mobilized DNA. Overall, our study provides significant steps toward (i) measuring the genetic variation that is caused by transposon insertions in humans and (ii) identifying the transposon copies that produce this variation.

Recovering frequencies of known haplotype blocks from single-nucleotide polymorphism allele frequencies

Prospects for large-scale association studies rely on economical methods and powerful analysis. Representing available SNPs by small subsets and measuring allele frequencies on pooled DNA samples each improve genotyping cost effectiveness, while haplotype analysis may highlight associations in otherwise underpowered studies. This manuscript provides the mathematical framework to integrate these methodologies.

Finding haplotype tagging SNPs by use of principal components analysis

The immense volume and rapid growth of human genomic data, especially single nucleotide polymorphisms (SNPs), present special challenges for both biomedical researchers and automatic algorithms. One such challenge is to select an optimal subset of SNPs, commonly referred as "haplotype tagging SNPs" (htSNPs), to capture most of the haplotype diversity of each haplotype block or gene-specific region. This information-reduction process facilitates cost-effective genotyping and, subsequently, genotype-phenotype association studies. It also has implications for assessing the risk of identifying research subjects on the basis of SNP information deposited in public domain databases. We have investigated methods for selecting htSNPs by use of principal components analysis (PCA). These methods first identify eigenSNPs and then map them to actual SNPs. We evaluated two mapping strategies, greedy discard and varimax rotation, by assessing the ability of the selected htSNPs to reconstruct genotypes of non-htSNPs. We also compared these methods with two other htSNP finders, one of which is PCA based. We applied these methods to three experimental data sets and found that the PCA-based methods tend to select the smallest set of htSNPs to achieve a 90% reconstruction precision.

Pharmacogenetics of antiretroviral therapy: genetic variation of response and toxicity

The application of a pharmacogenetic approach to antiretroviral drug therapy represents a significant challenge, as treatment involves multiple drugs and drug classes with the potential for significant variability in drug–host, as well as drug–drug, interactions. However, despite this inherent complexity, considerable gains have been made in understanding how genetic factors influence the efficacy and toxicity of HIV therapy. In this review the available evidence regarding genetic variation in drug disposition will be examined, including the potential for relatively polymorphic drug-metabolizing enzymes (e.g., cytochrome P450 isoforms) and drug transporters (e.g., P-glycoprotein) to influence the disposition of HIV protease inhibitor and non-nucleoside reverse transcriptase inhibitor drugs. In addition, the role of genetic variation in determining the immune response to drug-specific antigens will be considered as a potentially significant determinant of susceptibility to idiosyncratic drug reactions (e.g., major histocompatibility complex alleles associated with abacavir hypersensitivity). The current and potential clinical utility of pharmacogenetic testing in HIV management will also be emphasized.

The potential role of genes in nonalcoholic fatty liver disease

Although most people with obesity and type 2 diabetes will have steatosis, only a minority will ever develop nonalcoholic steatohepatitis (NASH), fibrosis, and cirrhosis. Family studies suggest that genetic factors are important in disease progression, although dissecting genetic factors playing a role in NASH and fibrosis from those influencing the development established risk factors is difficult. Several approaches can be used to look for genetic factors playing a role in nonalcoholic fatty liver disease (NAFLD). In the future, genome-wide single nucleotide polymorphism (SNP) scanning of cases and controls may become feasible. To date, however,studies have relied on candidate gene, case control, allele association methodology. Recent, and as yet preliminary, studies have reported associations between steatosis severity, NASH, and fibrosis with genes whose products are involved in lipid metabolism,oxidative stress, and endotoxin-cytokine interactions. If confirmed,these associations will enhance understanding of disease pathogenesis,and accordingly, the ability to design effective therapies.

Mining Disease Susceptibility Genes through SNP Analyses and Expression Profiling Using MALDI-TOF Mass Spectrometry

To find genes that underlie disease susceptibilities, genome-wide single nucleotide polymorphisms (SNPs) have been analyzed using high-throughput matrix assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS). As a proof-of-concept for this approach, gene regions have been identified that were previously associated by others with certain diseases or traits. On the same technology platform, accurate and absolute transcriptional profiling can be performed and applied to allele expression analysis. Here, we provide a brief review of the technology and its applications to disease gene discovery.

Nutritional genomics: the next frontier in the postgenomic era

The interface between the nutritional environment and cellular/genetic processes is being referred to as “nutrigenomics.” Nutrigenomics seeks to provide a molecular genetic understanding for how common dietary chemicals (i.e., nutrition) affect health by altering the expression and/or structure of an individual’s genetic makeup. The fundamental concepts of the field are that the progression from a healthy phenotype to a chronic disease phenotype must occur by changes in gene expression or by differences in activities of proteins and enzymes and that dietary chemicals directly or indirectly regulate the expression of genomic information. We present a conceptual basis and specific examples for this new branch of genomic research that focuses on the tenets of nutritional genomics: 1) common dietary chemicals act on the human genome, either directly or indirectly, to alter gene expression or structure; 2) under certain circumstances and in some individuals, diet can be a serious risk factor for a number of diseases; 3) some diet-regulated genes (and their normal, common variants) are likely to play a role in the onset, incidence, progression, and/or severity of chronic diseases; 4) the degree to which diet influences the balance between healthy and disease states may depend on an individual’s genetic makeup; and 5) dietary intervention based on knowledge of nutritional requirement, nutritional status, and genotype (i.e., “individualized nutrition”) can be used to prevent, mitigate, or cure chronic disease.

Defining haplotype blocks and tag single-nucleotide polymorphisms in the human genome

Recent studies suggest that the genome is organized into blocks of haplotypes, and efforts to create a genome-wide haplotype map of single-nucleotide polymorphisms (SNPs) are already underway. Haplotype blocks are defined algorithmically and to date several algorithms have been proposed. However, little is known about their relative performance in real data or about the impact of allele frequencies and parameter choices on the detection of haplotype blocks and the markers that tag them. Here we present a formal comparison of two major algorithms, a linkage disequilibrium (LD)-based method and a dynamic programming algorithm (DPA), in three chromosomal regions differing in gene content and recombination rate. The two methods produced strikingly different results. DPA identified fewer and larger haplotype blocks as well as a smaller set of tag SNPs than the LD method. For both methods, the results were strongly dependent on the allele frequency. Decreasing the minor allele frequency led to an up to 3.7-fold increase in the number of haplotype blocks and tag SNPs. Definition of haploytpe blocks and tag SNPs was also sensitive to parameter changes, but the results could not be reconciled simply by parameter adjustment. These results show that two major methods for detecting haplotype blocks and tag SNPs can produce different results in the same data and that these results are sensitive to marker allele frequencies and parameter choices. More information is needed to guide the choice of method, marker allele frequencies, and parameters in the development of a haplotype map.

Race, ancestry, and genes: implications for defining disease risk

Geneticists are interested in finding genes associated with disease. Because of widespread health disparities, race is a variable that is often said to be relevant in this context. The idea is that members of a preconceived "race" share common ancestry that may include genetic risk factors. Human variation has been shaped by the long-term processes of population history, and population samples that reflect that history carry statistical information about shared genetic variation or "ancestry." But race is an elusive concept and a term difficult even to define rigorously. Unfortunately, these problems are neither new nor related to recent genetic knowledge. Race is also one of the most politically charged subjects in American life because its associated sociocultural component has notoriously led to categorical treatment that has been misleading and politically misused. There are ways in which the concept of race (whether or not the term is used) can be a legitimate tool in the search for disease-associated genes. But in that context race reflects deeply confounded cultural as well as biological factors, and a careful distinction must be made between race as a statistical risk factor and causal genetic variables.

From genotype to phenotype: unraveling the complexities of cold adaptation in forest trees

Adaptation to winter cold in temperate and boreal trees involves complex genetic, physiological, and developmental processes. Genecological studies demonstrate the existence of steep genetic clines for cold adaptation traits in relation to environmental (mostly temperature related) gradients. Population differentiation is generally stronger for cold adaptation traits than for other quantitative traits and allozymes. Therefore, these traits appear to be under strong natural selection. Nonetheless, high levels of genetic variation persist within populations. The genetic control of cold adaptation traits ranges from weak to strong, with phenological traits having the highest heritabilities. Within-population genetic correlations among traits range from negligible to moderate. Generally, bud phenology and cold hardiness in the fall are genetically uncorrelated with bud phenology and cold hardiness in the spring. Analyses of quantitative trait loci indicate that cold adaptation traits are mostly controlled by multiple genes with small effects and that quantitative trait loci × environment interactions are common. Given this inherent complexity, we suggest that future research should focus on identifying and developing markers for cold adaptation candidate genes, then using multilocus, multi allelic analytical techniques to uncover the relationships between genotype and phenotype at both the individual and population levels. Ultimately, these methods may be useful for predicting the performance of genotypes in breeding programs and for better understanding the evolutionary ecology of forest trees.Key words: association genetics, cold hardiness, dormancy, genecology, bud phenology, quantitative trait loci.

Haplotypes of the cholesteryl ester transfer protein gene predict lipid-modifying response to statin therapy

Cholesteryl ester transfer protein (CETP) plays a central role in high-density lipoprotein (HDL) metabolism. Single nucleotide polymorphisms (SNPs) and haplotypes in the CETP gene were determined in 98 patients with untreated dyslipidemias and analyzed for associations with plasma CETP and plasma lipids before and during statin treatment. Individual CETP SNPs and haplotypes were both significantly associated with CETP enzyme mass and activity. However, only certain CETP haplotypes, but not individual SNPs, significantly predicted the magnitude of change in HDL cholesterol (HDL-C) and triglycerides. After adjusting for covariates and multiple testing, the TTCAAA haplotype showed a gene-dose effect in predicting the HDL-C increase (P=0.03), while the TTCAAAGGG and AAAGGG haplotypes predicted a decrease in triglycerides (P=0.04 both). This is the first study to demonstrate that SNP haplotypes derived from allelic SNP combinations in the CETP gene were more informative than single SNPs in predicting the response to lipid-modifying therapy with statins.

Single nucleotide polymorphisms (SNPs) that map to gaps in the human SNP map

An international effort is underway to generate a comprehensive haplotype map (HapMap) of the human genome represented by an estimated 300,000 to 1 million 'tag' single nucleotide polymorphisms (SNPs). Our analysis indicates that the current human SNP map is not sufficiently dense to support the HapMap project. For example, 24.6% of the genome currently lacks SNPs at the minimal density and spacing that would be required to construct even a conservative tag SNP map containing 300,000 SNPs. In an effort to improve the human SNP map, we identified 140,696 additional SNP candidates using a new bioinformatics pipeline. Over 51,000 of these SNPs mapped to the largest gaps in the human SNP map, leading to significant improvements in these regions. Our SNPs will be immediately useful for the HapMap project, and will allow for the inclusion of many additional genomic intervals in the final HapMap. Nevertheless, our results also indicate that additional SNP discovery projects will be required both to define the haplotype architecture of the human genome and to construct comprehensive tag SNP maps that will be useful for genetic linkage studies in humans.

Linkage disequilibrium and inference of ancestral recombination in 538 single-nucleotide polymorphism clusters across the human genome

The prospect of using linkage disequilibrium (LD) for fine-scale mapping in humans has attracted considerable attention, and, during the validation of a set of single-nucleotide polymorphisms (SNPs) for linkage analysis, a set of data for 4,833 SNPs in 538 clusters was produced that provides a rich picture of local attributes of LD across the genome. LD estimates may be biased depending on the means by which SNPs are first identified, and a particular problem of ascertainment bias arises when SNPs identified in small heterogeneous panels are subsequently typed in larger population samples. Understanding and correcting ascertainment bias is essential for a useful quantitative assessment of the landscape of LD across the human genome. Heterogeneity in the population recombination rate, rho=4Nr, along the genome reflects how variable the density of markers will have to be for optimal coverage. We find that ascertainment-corrected rho varies along the genome by more than two orders of magnitude, implying great differences in the recombinational history of different portions of our genome. The distribution of rho is unimodal, and we show that this is compatible with a wide range of mixtures of hotspots in a background of variable recombination rate. Although rho is significantly correlated across the three population samples, some regions of the genome exhibit population-specific spikes or troughs in rho that are too large to be explained by sampling. This result is consistent with differences in the genealogical depth of local genomic regions, a finding that has direct bearing on the design and utility of LD mapping and on the National Institutes of Health HapMap project.

Haplotype analysis in population genetics and association studies

Several studies of haplotype structures in the human genome in various populations have been published recently. Such knowledge may provide valuable information on human evolutionary history and lead to the development of more efficient strategies to identify genetic variants that increase susceptibility to human diseases. In this review, we summarize the current understanding of haplotype structure, diversity, and distribution in the human genome, with a focus on statistical issues in using haplotypes for studies of population genetics and evolutionary history, as well as to identify genetic variants underlying complex human traits.