Tag SNP selection for Finnish individuals based on the CEPH Utah HapMap database

Proband Whole-Exome Sequencing Identified Genes Responsible for Autosomal Recessive Non-Syndromic Hearing Loss in 33 Chinese Nuclear Families

Autosomal recessive non-syndromic hearing loss (ARNSHL) is a highly heterogeneous disease involving more than 70 pathogenic genes. However, most ARNSHL families have small-sized pedigrees with limited genetic information, rendering challenges for the molecular diagnosis of these patients. Therefore, we attempted to establish a strategy for identifying deleterious variants associated with ARNSHL by applying proband whole-exome sequencing (proband-WES). Aside from desiring to improve molecular diagnostic rates, we also aimed to search for novel deafness genes shared by patients with similar phenotype, making up for the deficiency of small ARNSHL families. In this study, 48.5% (16/33) families were detected the pathogenic variants in eight known deafness genes, including 10 novel variants identified in TMPRSS3 (MIM 605551), MYO15A (MIM 602666), TMC1 (MIM 606706), ADGRV1 (MIM 602851), and PTPRQ (MIM 603317). Apart from six novel variants with a truncating effect (nonsense, deletion, insertion, and splice-site), four novel missense variants were not found in 200 unrelated control population by using Sanger sequencing. It is important to note that none of novel genes were shared across different pedigrees, indicating that a larger sample size might be needed. Proband-WES is a cost-effective and precise way of identifying causative variants in nuclear families with ARNSHL. This economical strategy may be appropriated as a clinical application to provide molecular diagnostics, genetic counseling, and individualized health maintenance measures for patients with ARNSHL at hearing clinics.

A Rare Variant (rs933717) at FBXO31-MAP1LC3B in Chinese Is Associated With Systemic Lupus Erythematosus

OBJECTIVE

Recent evidence from genetic, cell biology, and animal model studies has suggested a pivotal role of autophagy in mediating systemic lupus erythematosus (SLE). However, the genetic basis has not yet been thoroughly examined. Therefore, the aim of the present study was to identify additional susceptibility variants in autophagy-related genes along with their functional significance.

METHODS

First, we performed a gene family-based genetic association analysis in SLE patients with the use of ImmunoChip arrays, and then we selected the most strongly associated polymorphisms for replication in additional cohorts. To identify regulatory clues, we analyzed publicly available blood expression quantitative trait locus data and Encyclopedia of DNA Elements data on transcription factor binding sites and cell type-specific differential expression. Functional effects were tested by luciferase reporter assays, electrophoretic mobility shift assays, and differential gene expression assays.

RESULTS

In 14,474 samples, we observed that the rare Chinese variant rs933717T was associated with susceptibility to SLE (0.11% in cases versus 0.87% in controls; P = 2.36 × 10^-10 , odds ratio 0.13). The rs933717 risk allele C correlated with increased MAP1LC3B expression; increased MAP1LC3B messenger RNA was observed in SLE patients and in lupus-prone mice. In reporter gene constructs, the risk allele increased luciferase activity up to 2.7-3.8-fold in both HEK 293T and Jurkat cell lines, and the binding of HEK 293T and Jurkat cell nuclear extracts to the risk allele was also increased.

CONCLUSION

We observed a likely genetic association between light chain 3B, a widely used marker for autophagy, and susceptibility to SLE.

A Theory for the Origin of Human Menopause

A complete and compelling evolutionary explanation for the origin of human menopause is wanting. Menopause onset is defined clinically as the final menses, confirmed after 1 year without menstruation. The theory proposed herein explains at multiple levels - ultimately genetic but involving (1) behavioral, (2) life history, and (3) social changes - the origin and evolution of menopause in women. Individuals in Lower Paleolithic human populations were characterized by short lifespans with diminished late-age survival and fertility, similar to contemporary chimpanzees, and thence were subject to three changes. (1) A mating behavior change was established in which only young women reproduced, thereby rendering as effectively neutral female-specific late-onset fertility-diminishing mutations, which accumulated subsequently. (2) A lifespan increase was manifested adaptively, revealing the reproductive senescence phenotype encoded in late-onset fertility-diminishing mutation genotypes, which, heretofore, had been unexpressed in the shorter lifespan. (3) A social interaction change emerged exaptively, when older non-reproductive women exclusively started assisting in rearing grandchildren rather than giving birth to and caring for their own children, ultimately leading to menstrual cycle cessation. The changes associate in a one-to-one manner with existing, non-mutually exclusive hypotheses for the origin of human menopause. Evidence for each hypothesis and its associated change having occurred are reviewed, and the hypotheses are combined in a synthetic theory for the origin of human menopause. The new theory simultaneously addresses the main theoretical problem with each hypothesis and yields predictions for future testing.

Ovarian aging and premature ovarian failure

Physiological reproductive aging occurs as a result of a decrease in the number and quality of oocytes in ovarian cortex follicles. Although the reason for the decrease in the quality of the pool and follicular oocytes is not fully understood, endocrine, paracrine, genetic, and metabolic factors are thought to be effective. Nowadays, in order to understand the mechanisms of ovarian aging, genomic research has gained importance. The effect of co-factors, such as telomerase and ceramide, in the ovarian aging process is only getting ascertained with new research studies. The most important tests in the assessment of ovarian aging are antral follicle count and anti-Mullerian hormone.

Exploring genomic structure differences and similarities between the Greek and European HapMap populations: implications for association studies

Studies of the genomic structure of the Greek population and Southeastern Europe are limited, despite the central position of the area as a gateway for human migrations into Europe. HapMap has provided a unique tool for the analysis of human genetic variation. Europe is represented by the CEU (Northwestern Europe) and the TSI populations (Tuscan Italians from Southern Europe), which serve as reference for the design of genetic association studies. Furthermore, genetic association findings are often transferred to unstudied populations. Although initial studies support the fact that the CEU can, in general, be used as reference for the selection of tagging SNPs in European populations, this has not been extensively studied across Europe. We set out to explore the genomic structure of the Greek population (56 individuals) and compare it to the HapMap TSI and CEU populations. We studied 1112 SNPs (27 regions, 13 chromosomes). Although the HapMap European populations are, in general, a good reference for the Greek population, regions of population differentiation do exist and results should not be light-heartedly generalized. We conclude that, perhaps due to the individual evolutionary history of each genomic region, geographic proximity is not always a perfect guide for selecting a reference population for an unstudied population.

Current understanding of ovarian aging

The reproductive system of human female exhibits a much faster rate of aging than other body systems. Ovarian aging is thought to be dominated by a gradual decreasing numbers of follicles, coinciding with diminished quality of oocytes. Menopause is the final step in the process of ovarian aging. This review focuses on the mechanisms underlying the ovarian aging involving a poor complement of follicles at birth and a high rate of attrition each month, as well as the alternated endocrine factors. We also discuss the possible causative factors that contribute to ovarian aging, e.g., genetic factors, accumulation of irreparable damage of microenvironment, pathological effect and other factors. The appropriate and reliable methods to assess ovarian aging, such as quantification of follicles, endocrine measurement and genetic testing have also been discussed. Increased knowledge of the ovarian aging mechanisms may improve the prevention of premature ovarian failure.

MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes

Genome-wide association studies (GWAS) can identify common alleles that contribute to complex disease susceptibility. Despite the large number of SNPs assessed in each study, the effects of most common SNPs must be evaluated indirectly using either genotyped markers or haplotypes thereof as proxies. We have previously implemented a computationally efficient Markov Chain framework for genotype imputation and haplotyping in the freely available MaCH software package. The approach describes sampled chromosomes as mosaics of each other and uses available genotype and shotgun sequence data to estimate unobserved genotypes and haplotypes, together with useful measures of the quality of these estimates. Our approach is already widely used to facilitate comparison of results across studies as well as meta-analyses of GWAS. Here, we use simulations and experimental genotypes to evaluate its accuracy and utility, considering choices of genotyping panels, reference panel configurations, and designs where genotyping is replaced with shotgun sequencing. Importantly, we show that genotype imputation not only facilitates cross study analyses but also increases power of genetic association studies. We show that genotype imputation of common variants using HapMap haplotypes as a reference is very accurate using either genome-wide SNP data or smaller amounts of data typical in fine-mapping studies. Furthermore, we show the approach is applicable in a variety of populations. Finally, we illustrate how association analyses of unobserved variants will benefit from ongoing advances such as larger HapMap reference panels and whole genome shotgun sequencing technologies.

ERAP1 Is Associated with Ankylosing Spondylitis in Han Chinese

Objective.

Genetic components play important roles in the incidence and development of ankylosing spondylitis (AS). Aminopeptidase regulator of tumor necrosis factor receptor shedding 1 (ERAP1) was recently found to be associated with AS in North American and British cohorts. We evaluated whether ERAP1 is associated with AS in a Chinese Han population.

Methods.

A sample of 50 patients and 50 healthy controls was recruited for preliminary screening for informative single-nucleotide polymorphisms (SNP). Then 6 SNP of suggestive significance in the initial screening were followed up in a large sample of 471 patients with AS and 456 ethnically matched controls. Diagnosis of AS followed the 1984 modified New York criteria. Linkage disequilibrium coefficient (D’ and r2) and haplotypes were estimated by Haploview.

Result.

Two SNP (rs27434, p = 0.00039, and rs27529, p = 0.0083) in ERAP1 other than that reported previously were found to be significantly associated with AS. Haplotype analysis using 5 SNP within 1 linkage disequilibrium block identified 2 risk haplotypes (GATGT and GACGT) and 1 protective haplotype (GGTGT) for AS.

Conclusion.

Our study demonstrated that 2 novel SNP in ERAP1 were associated with AS in the Han Chinese population, suggesting that ERAP1 might confer genetic risk for AS in Han Chinese through the common mechanism shared by different populations, although the AS-associated SNP in ERAP1 might be population-specific.

Comparison of genome-wide variation between Malawians and African ancestry HapMap populations

Understanding genetic variation between populations is important because it affects the portability of human genome-wide analytical methods. We compared genetic variation and substructure between Malawians and other African and non-African HapMap populations. Allele frequencies and adjacent linkage disequilibrium (LD) were measured for 617 715 single nucleotide polymorphisms (SNPs) across subject genomes. Allele frequencies in the Malawian population (N=226) were highly correlated with allele frequencies in HapMap populations of African ancestry (AFA, N=376), namely Yoruban in Ibadan, Nigeria (Spearman's r(2)=0.97), Luhya in Webuye, Kenya (r(2)=0.97), African Americans in the southwest United States (r(2)=0.94) and Maasai in Kinyawa, Kenya (r(2)=0.91). This correlation was much lower between Malawians and other ancestry populations (r(2)<0.52). LD correlations between Malawians and HapMap populations were strongest for the populations of AFA (AFA r(2)>0.82, other ancestries r(2)<0.57). Principal components analyses revealed little population substructure within our Malawi sample but provided clear distinction between Malawians, AFA populations and two European populations. Five SNPs within the lactase gene (LCT) had substantially different allele frequencies between the Malawi population and Maasai in Kenyawa, Kenya (rs3769013, rs730005, rs3769012, rs2304370; P-values <1 x 10(-33)).

Extent and distribution of linkage disequilibrium in the Old Order Amish

Knowledge of the extent and distribution of linkage disequilibrium (LD) is critical to the design and interpretation of gene mapping studies. Because the demographic history of each population varies and is often not accurately known, it is necessary to empirically evaluate LD on a population-specific basis. Here we present the first genome-wide survey of LD in the Old Order Amish (OOA) of Lancaster County Pennsylvania, a closed population derived from a modest number of founders. Specifically, we present a comparison of LD between OOA individuals and US Utah participants in the International HapMap project (abbreviated CEU) using a high-density single nucleotide polymorphism (SNP) map. Overall, the allele (and haplotype) frequency distributions and LD profiles were remarkably similar between these two populations. For example, the median absolute allele frequency difference for autosomal SNPs was 0.05, with an inter-quartile range of 0.02–0.09, and for autosomal SNPs 10–20 kb apart with common alleles (minor allele frequency≥0.05), the LD measure r² was at least 0.8 for 15 and 14% of SNP pairs in the OOA and CEU, respectively. Moreover, tag SNPs selected from the HapMap CEU sample captured a substantial portion of the common variation in the OOA (∼88%) at r²≥0.8. These results suggest that the OOA and CEU may share similar LD profiles for other common but untyped SNPs. Thus, in the context of the common variant-common disease hypothesis, genetic variants discovered in gene mapping studies in the OOA may generalize to other populations.

Evaluating the transferability of Hapmap SNPs to a Singapore Chinese population

BACKGROUND

The International Hapmap project serves as a valuable resource for human genome variation data, however its applicability to other populations has yet to be exhaustively investigated. In this paper, we use high density genotyping chips and resequencing strategies to compare the Singapore Chinese population with the Hapmap populations. First we compared 1028 and 114 unrelated Singapore Chinese samples genotyped using the Illumina Human Hapmap 550 k chip and Affymetrix 500 k array respectively against the 270 samples from Hapmap. Secondly, data from 20 candidate genes on 5q31-33 resequenced for an asthma candidate gene based study was also used for the analysis.

RESULTS

A total of 237 SNPs were identified through resequencing of which only 95 SNPs (40%) were in Hapmap; however an additional 56 SNPs (24%) were not genotyped directly but had a proxy SNP in the Hapmap. At the genome-wide level, Singapore Chinese were highly correlated with Hapmap Han Chinese with correlation of 0.954 and 0.947 for the Illumina and Affymetrix platforms respectively with deviant SNPs randomly distributed within and across all chromosomes.

CONCLUSIONS

The high correlation between our population and Hapmap Han Chinese reaffirms the applicability of Hapmap based genome-wide chips for GWA studies. There is a clear population signature for the Singapore Chinese samples and they predominantly resemble the southern Han Chinese population; however when new migrants particularly those with northern Han Chinese background were included, population stratification issues may arise. Future studies needs to address population stratification within the sample collection while designing and interpreting GWAS in the Chinese population.

Additive effects of genetic variation in GCK and G6PC2 on insulin secretion and fasting glucose

OBJECTIVE

Glucokinase (GCK) and glucose-6-phosphatase catalytic subunit 2 (G6PC2) regulate the glucose-cycling step in pancreatic beta-cells and may regulate insulin secretion. GCK rs1799884 and G6PC2 rs560887 have been independently associated with fasting glucose, but their interaction on glucose-insulin relationships is not well characterized.

RESEARCH DESIGN AND METHODS

We tested whether these variants are associated with diabetes-related quantitative traits in Mexican Americans from the BetaGene Study and attempted to replicate our findings in Finnish men from the METabolic Syndrome in Men (METSIM) Study.

RESULTS

rs1799884 was not associated with any quantitative trait (corrected P > 0.1), whereas rs560887 was significantly associated with the oral glucose tolerance test 30-min incremental insulin response (30' Deltainsulin, corrected P = 0.021). We found no association between quantitative traits and the multiplicative interaction between rs1799884 and rs560887 (P > 0.26). However, the additive effect of these single nucleotide polymorphisms was associated with fasting glucose (corrected P = 0.03) and 30' Deltainsulin (corrected P = 0.027). This additive association was replicated in METSIM (fasting glucose, P = 3.5 x 10(-10) 30' Deltainsulin, P = 0.028). When we examined the relationship between fasting glucose and 30' Deltainsulin stratified by GCK and G6PC2, we noted divergent changes in these quantitative traits for GCK but parallel changes for G6PC2. We observed a similar pattern in METSIM.

CONCLUSIONS

Our data suggest that variation in GCK and G6PC2 have additive effects on both fasting glucose and insulin secretion.

TagSNP transferability and relative loss of variability prediction from HapMap to an admixed population

Background

The application of a subset of single nucleotide polymorphisms, the tagSNPs, can be useful in capturing untyped SNPs information in a genomic region. TagSNP transferability from the HapMap dataset to admixed populations is of uncertain value due population structure, admixture, drift and recombination effects. In this work an empirical dataset from a Brazilian admixed sample was evaluated against the HapMap population to measure tagSNP transferability and the relative loss of variability prediction.

Methods

The transferability study was carried out using SNPs dispersed over four genomic regions: the PTPN22, HMGCR, VDR and CETP genes. Variability coverage and the prediction accuracy for tagSNPs in the selected genomic regions of HapMap phase II were computed using a prediction accuracy algorithm. Transferability of tagSNPs and relative loss of prediction were evaluated according to the difference between the Brazilian sample and the pooled and single HapMap population estimates.

Results

Each population presented different levels of prediction per gene. On average, the Brazilian (BRA) sample displayed a lower power of prediction when compared to HapMap and the pooled sample. There was a relative loss of prediction for BRA when using single HapMap populations, but a pooled HapMap dataset generated minor loss of variability prediction and lower standard deviations, except at the VDR locus at which loss was minor using CEU tagSNPs.

Conclusion

Studies that involve tagSNP selection for an admixed population should not be generally correlated with any specific HapMap population and can be better represented with a pooled dataset in most cases.

Ovarian aging: mechanisms and clinical consequences

Menopause is the final step in the process referred to as ovarian ageing. The age related decrease in follicle numbers dictates the onset of cycle irregularity and the final cessation of menses. The parallel decay in oocyte quality contributes to the gradual decline in fertility and the final occurrence of natural sterility. Endocrine changes mainly relate to the decline in the negative feedback from ovarian factors at the hypothalamo-pituitary unit. The declining cohort of antral follicles with age first results in gradually elevated FSH levels, followed by subsequent stages of overt cycle irregularity. The gradual decline in the size of the antral follicle cohort is best represented by decreasing levels of anti-Mullerian hormone. The variability of ovarian ageing among women is evident from the large variation in age at menopause. The identification of women who have severely decreased ovarian reserve for their age is clinically relevant. Ovarian reserve tests have appeared to be fairly accurate in predicting response to ovarian stimulation in the assisted reproductive technology (ART) setting. The capacity to predict the chances for spontaneous pregnancy or pregnancy after ART appears very limited. As menopause and the preceding decline in oocyte quality seem to have a fixed time interval, tests that predict the age at menopause may be useful to assess individual reproductive lifespan. Especially genetic studies, both addressing candidate gene and genome wide association, have identified several interesting loci of small genetic variation that may determine fetal follicle pool development and subsequent wastage of his pool over time. Improved knowledge of the ovarian ageing mechanisms may ultimately provide tools for prediction of menopause and manipulation of the early steps of folliculogenesis for the purpose of contraception and fertility lifespan extension.

ATOM: a powerful gene-based association test by combining optimally weighted markers

BACKGROUND

Large-scale candidate-gene and genome-wide association studies genotype multiple SNPs within or surrounding a gene, including both tag and functional SNPs. The immense amount of data generated in these studies poses new challenges to analysis. One particularly challenging yet important question is how to best use all genetic information to test whether a gene or a region is associated with the trait of interest.

METHODS

Here we propose a powerful gene-based Association Test by combining Optimally Weighted Markers (ATOM) within a genomic region. Due to variation in linkage disequilibrium, different markers often associate with the trait of interest at different levels. To appropriately apportion their contributions, we assign a weight to each marker that is proportional to the amount of information it captures about the trait locus. We analytically derive the optimal weights for both quantitative and binary traits, and describe a procedure for estimating the weights from a reference database such as the HapMap. Compared with existing approaches, our method has several distinct advantages, including (i) the ability to borrow information from an external database to increase power, (ii) the theoretical derivation of optimal marker weights and (iii) the scalability to simultaneous analysis of all SNPs in candidate genes and pathways.

RESULTS

Through extensive simulations and analysis of the FTO gene in our ongoing genome-wide association study on childhood obesity, we demonstrate that ATOM increases the power to detect genetic association as compared with several commonly used multi-marker association tests.

Calibrating the performance of SNP arrays for whole-genome association studies

To facilitate whole-genome association studies (WGAS), several high-density SNP genotyping arrays have been developed. Genetic coverage and statistical power are the primary benchmark metrics in evaluating the performance of SNP arrays. Ideally, such evaluations would be done on a SNP set and a cohort of individuals that are both independently sampled from the original SNPs and individuals used in developing the arrays. Without utilization of an independent test set, previous estimates of genetic coverage and statistical power may be subject to an overfitting bias. Additionally, the SNP arrays' statistical power in WGAS has not been systematically assessed on real traits. One robust setting for doing so is to evaluate statistical power on thousands of traits measured from a single set of individuals. In this study, 359 newly sampled Americans of European descent were genotyped using both Affymetrix 500K (Affx500K) and Illumina 650Y (Ilmn650K) SNP arrays. From these data, we were able to obtain estimates of genetic coverage, which are robust to overfitting, by constructing an independent test set from among these genotypes and individuals. Furthermore, we collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Our genetic coverage estimates are lower than previous reports, providing evidence that previous estimates may be inflated due to overfitting. The Ilmn650K platform showed reasonable power (50% or greater) to detect SNPs associated with quantitative traits when the signal-to-noise ratio (SNR) is greater than or equal to 0.5 and the causal SNP's minor allele frequency (MAF) is greater than or equal to 20% (N = 359). In testing each of the more than 40,000 gene expression traits for association to each of the SNPs on the Ilmn650K and Affx500K arrays, we found that the Ilmn650K yielded 15% times more discoveries than the Affx500K at the same false discovery rate (FDR) level.

Advances in SNP genotyping array technologies have made whole-genome association studies (WGAS) a readily available approach. Genetic coverage and the statistical power are two key properties to evaluate on the arrays. In this study, 359 newly sampled individuals were genotyped using Affymetrix 500K and Illumina 650Y SNP arrays. From these data, we obtained new estimates of genetic coverage by constructing a test set from among these genotypes and individuals that is independent from the SNPs and individuals used to construct the arrays. These estimates are notably smaller than previous ones, which we argue is due to an overfitting bias in previous studies. We also collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Through this dataset and simulations, we find that the SNP arrays provide adequate power to detect quantitative trait loci when the causal SNP's minor allele frequency is greater than 20%, but low power is less than 10%. Importantly, we provide evidence that sample size has a greater impact on the power of WGAS than SNP density or genetic coverage.

HapMap tagSNP transferability in multiple populations: general guidelines

Linkage disequilibrium (LD) has received much attention recently because of its value in localizing disease-causing genes. Due to the extensive LD between neighboring loci in the human genome, it is believed that a subset of the single nucleotide polymorphisms in a region (tagSNPs) can be selected to capture most of the remaining SNP variants. In this study, we examined LD patterns and HapMap tagSNP transferability in more than 300 individuals. A South Indian sample and an African Mbuti Pygmy population sample were included to evaluate the performance of HapMap tagSNPs in geographically distinct and genetically isolated populations. Our results show that HapMap tagSNPs selected with r(2) >= 0.8 can capture more than 85% of the SNPs in populations that are from the same continental group. Combined tagSNPs from HapMap CEU and CHB+JPT serve as the best reference for the Indian sample. The HapMap YRI are a sufficient reference for tagSNP selection in the Pygmy sample. In addition to our findings, we reviewed over 25 recent studies of tagSNP transferability and propose a general guideline for selecting tagSNPs from HapMap populations.

On transferability of genome-wide tagSNPs

The question of tagging single nucleotide polymorphism (tagSNP) transferability is an important one because many ongoing and upcoming Genome-Wide Association studies rely critically upon the validity, and practical feasibility of using a universal core set of tagSNPs. A series of recent studies analyzed performance of tagSNPs selected based on the HapMap. While these studies showed largely satisfactory transferability of the tagSNPs, they also reported that the level of transferability varies, substantively sometimes, especially when tagSNPs selected in one population were used in another distant population. We present a review of the literature about where and why tagSNP transferability may become a problem and suggest research directions that may help the resolution.

Evaluation of HapMap data in six populations of European descent

We studied how well the European CEU samples used in the Haplotype Mapping Project (HapMap) represent five European populations by analyzing nuclear family samples from the Swedish, Finnish, Dutch, British and Australian (European ancestry) populations. The number of samples from each population (about 30 parent-offspring trios) was similar to that in the HapMap sample sets. A panel of 186 single nucleotide polymorphisms (SNPs) distributed over the 1.5 Mb region of the GRID2 gene on chromosome 4 was genotyped. The genotype data were compared pair-wise between the HapMap sample and the other population samples. Principal component analysis (PCA) was used to cluster the data from different populations with respect to allele frequencies and to define the markers responsible for observed variance. The only sample with detectable differences in allele frequencies was that from Kuusamo, Finland. This sample also separated from the others, including the other Finnish sample, in the PCA analysis. A set of tagSNPs was defined based on the HapMap data and applied to the samples. The tagSNPs were found to capture the genetic variation in the analyzed region at r(2)>0.8 at levels ranging from 95% in the Kuusamo sample to 87% in the Australian sample. To capture the maximal genetic variation in the region, the Kuusamo, HapMap and Australian samples required 58, 63 and 73 native tagSNPs, respectively. The HapMap CEU sample represents the European samples well for tagSNP selection, with some caution regarding estimation of allele frequencies in the Finnish Kuusamo sample, and a slight reduction in tagging efficiency in the Australian sample.

An evaluation of the performance of HapMap SNP data in a Shanghai Chinese population: analyses of allele frequency, linkage disequilibrium pattern and tagging SNPs transferability on chromosome 1q21-q25

BACKGROUND

The HapMap project aimed to catalog millions of common single nucleotide polymorphisms (SNPs) in the human genome in four major populations, in order to facilitate association studies of complex diseases. To examine the transferability of Han Chinese in Beijing HapMap data to the Southern Han Chinese in Shanghai, we performed comparative analyses between genotypes from over 4,500 SNPs in a 21 Mb region on chromosome 1q21-q25 in 80 unrelated Shanghai Chinese and 45 HapMap Chinese data.

RESULTS

Three thousand and forty-two SNPs were analyzed after removal of SNPs that failed quality control and those not in the HapMap panel. We compared the allele frequency distributions, linkage disequilibrium patterns, haplotype frequency distributions and tagging SNP sets transferability between the HapMap population and Shanghai Chinese population. Among the four HapMap populations, Beijing Chinese showed the best correlation with Shanghai population on allele frequencies, linkage disequilibrium and haplotype frequencies. Tagging SNP sets selected from four HapMap populations at different thresholds were evaluated in the Shanghai sample. Under the threshold of r2 equal to 0.8 or 0.5, both HapMap Chinese and Japanese data showed better coverage and tagging efficiency than Caucasian and African data.

CONCLUSION

Our study supported the applicability of HapMap Beijing Chinese SNP data to the study of complex diseases among southern Chinese population.

An overview of the genetic dissection of complex traits

Thanks to the recent revolutionary genomic advances such as the International HapMap consortium, resolution of the genetic architecture of common complex traits is beginning to look hopeful. While demonstrating the feasibility of genome-wide association (GWA) studies, the pathbreaking Wellcome Trust Case Control Consortium (WTCCC) study also serves to underscore the critical importance of very large sample sizes and draws attention to potential problems, which need to be addressed as part of the study design. Even the large WTCCC study had vastly inadequate power for several of the associations reported (and confirmed) and, therefore, most of the regions harboring relevant associations may not be identified anytime soon. This chapter provides an overview of some of the key developments in the methodological approaches to genetic dissection of common complex traits. Constrained Bayesian networks are suggested as especially useful for analysis of pathway-based SNPs. Likewise, composite likelihood is suggested as a promising method for modeling complex systems. It discusses the key steps in a study design, with an emphasis on GWA studies. Potential limitations highlighted by the WTCCC GWA study are discussed, including problems associated with massive genotype imputation, analysis of pooled national samples, shared controls, and the critical role of interactions. GWA studies clearly need massive sample sizes that are only possible through genuine collaborations. After all, for common complex traits, the question is not whether we can find some pieces of the puzzle, but how large and what kind of a sample we need to (nearly) solve the genetic puzzle.

Gene-centric characteristics of genome-wide association studies

BACKGROUND

The high-throughput genotyping chips have contributed greatly to genome-wide association (GWA) studies to identify novel disease susceptibility single nucleotide polymorphisms (SNPs). The high-density chips are designed using two different SNP selection approaches, the direct gene-centric approach, and the indirect quasi-random SNPs or linkage disequilibrium (LD)-based tagSNPs approaches. Although all these approaches can provide high genome coverage and ascertain variants in genes, it is not clear to which extent these approaches could capture the common genic variants. It is also important to characterize and compare the differences between these approaches.

METHODOLOGY/PRINCIPAL FINDINGS

In our study, by using both the Phase II HapMap data and the disease variants extracted from OMIM, a gene-centric evaluation was first performed to evaluate the ability of the approaches in capturing the disease variants in Caucasian population. Then the distribution patterns of SNPs were also characterized in genic regions, evolutionarily conserved introns and nongenic regions, ontologies and pathways. The results show that, no mater which SNP selection approach is used, the current high-density SNP chips provide very high coverage in genic regions and can capture most of known common disease variants under HapMap frame. The results also show that the differences between the direct and the indirect approaches are relatively small. Both have similar SNP distribution patterns in these gene-centric characteristics.

CONCLUSIONS/SIGNIFICANCE

This study suggests that the indirect approaches not only have the advantage of high coverage but also are useful for studies focusing on various functional SNPs either in genes or in the conserved regions that the direct approach supports. The study and the annotation of characteristics will be helpful for designing and analyzing GWA studies that aim to identify genetic risk factors involved in common diseases, especially variants in genes and conserved regions.

GWAsimulator: a rapid whole-genome simulation program

SUMMARY

GWAsimulator implements a rapid moving-window algorithm to simulate genotype data for case-control or population samples from genomic SNP chips. For case-control data, the program generates cases and controls according to a user-specified multi-locus disease model, and can simulate specific regions if desired. The program uses phased genotype data as input and has the flexibility of simulating genotypes for different populations and different genomic SNP chips. When the HapMap phased data are used, the simulated data have similar local LD patterns as the HapMap data. As genome-wide association (GWA) studies become increasingly popular and new GWA data analysis methods are being developed, we anticipate that GWAsimulator will be an important tool for evaluating performance of new GWA analysis methods.

AVAILABILITY

The C++ source code, executables for Linux, Windows and MacOS, manual, example data sets and analysis program are available at http://biostat.mc.vanderbilt.edu/GWAsimulator

Assembly of inflammation-related genes for pathway-focused genetic analysis

Recent identifications of associations between novel variants in inflammation-related genes and several common diseases emphasize the need for systematic evaluations of these genes in disease susceptibility. Considering that many genes are involved in the complex inflammation responses and many genetic variants in these genes have the potential to alter the functions and expression of these genes, we assembled a list of key inflammation-related genes to facilitate the identification of genetic associations of diseases with an inflammation-related etiology. We first reviewed various phases of inflammation responses, including the development of immune cells, sensing of danger, influx of cells to sites of insult, activation and functional responses of immune and non-immune cells, and resolution of the immune response. Assisted by the Ingenuity Pathway Analysis, we then identified 17 functional sub-pathways that are involved in one or multiple phases. This organization would greatly increase the chance of detecting gene-gene interactions by hierarchical clustering of genes with their functional closeness in a pathway. Finally, as an example application, we have developed tagging single nucleotide polymorphism (tSNP) arrays for populations of European and African descent to capture all the common variants of these key inflammation-related genes. Assays of these tSNPs have been designed and assembled into two Affymetrix ParAllele customized chips, one each for European (12,011 SNPs) and African (21,542 SNPs) populations. These tSNPs have greater coverage for these inflammation-related genes compared to the existing genome-wide arrays, particularly in the African population. These tSNP arrays can facilitate systematic evaluation of inflammation pathways in disease susceptibility. For additional applications, other genotyping platforms could also be employed. For existing genome-wide association data, this list of key inflammation-related genes and associated subpathways can facilitate comprehensive inflammation pathway- focused association analyses.

Simulating association studies: a data-based resampling method for candidate regions or whole genome scans

MOTIVATION

Reductions in genotyping costs have heightened interest in performing whole genome association scans and in the fine mapping of candidate regions. Improvements in study design and analytic techniques will require the simulation of datasets with realistic patterns of linkage disequilibrium and allele frequencies for typed SNPs.

METHODS

We describe a general approach to simulate genotyped datasets for standard case-control or affected child trio data, by resampling from existing phased datasets. The approach allows for considerable flexibility in disease models, potentially involving a large number of interacting loci. The method is most applicable for diseases caused by common variants that have not been under strong selection, a class specifically targeted by the International HapMap project.

RESULTS

Using the three population Phase I/II HapMap data as a testbed for our approach, we have implemented the approach in HAP-SAMPLE, a web-based simulation tool.

Foundations, promises and uncertainties of personalized medicine

Personalized medicine introduces the promise to use molecular markers that signal the risk of disease or its presence before clinical signs and symptoms appear. This information underlies a new healthcare strategy focused on prevention and early intervention, rather than reaction to advanced stages of disease. Such a strategy can delay disease onset or minimize symptom severity. The molecular foundations that enable personalized medicine include detection of variation in nucleotide sequence of genes and in characteristic patterns of gene expression, proteins and metabolites. Genetic and molecular patterns are correlated with disease manifestations, drug responses, treatment prognosis, or prediction of predisposition to future disease states. However, the uncertainties for personalized medicine are considerable, including economic, ethical, legal, and societal questions. Although much of its promise remains unproven to date, the foundations of personalized medicine appear solid and evidence is accumulating rapidly pointing to its growing importance in healthcare (Fig. 1).

The complexity of age-related hearing impairment: contributing environmental and genetic factors

Age-related hearing impairment (ARHI) is the most common sensory impairment seen in the elderly. It is a complex disorder, with both environmental as well as genetic factors contributing to the impairment. The involvement of several environmental factors has been partially elucidated. A first step towards the identification of the genetic factors has been made, which will result in the identification of susceptibility genes, and will provide possible targets for the future treatment and/or prevention of ARHI. This paper aims to give a broad overview of the scientific findings related to ARHI, focusing mainly on environmental and genetic data in humans and in animal models. In addition, methods for the identification of contributing genetic factors as well as possible future therapeutic strategies are discussed.

Comparison of ENCODE region SNPs between Cebu Filipino and Asian HapMap samples

Patterns of linkage disequilibrium (LD) act as the framework for designing efficient association studies; these patterns are being studied and catalogued by The International HapMap Project. The current study assessed the transferability of tag SNPs chosen from HapMap panels to a cohort of 80 individuals from metro Cebu, Philippines, who participated in the Cebu Longitudinal Health and Nutrition Survey (CLHNS). The analyses focused on 627 single nucleotide polymorphisms (SNPs) in the central 40 kb within each of the 10 HapMap ENCODE regions. The similarity between the genetic variants in Cebu Filipino samples and HapMap panels was examined using allele frequency estimates, measures of pairwise linkage disequilibrium (LD), and haplotype frequency estimates. For these measures, strong correlations were observed between the Cebu Filipino samples and the Asian panels from HapMap, with the strongest correlations observed with the Han Chinese from Beijing (CHB) panel. Tag SNPs selected using the HapMap CHB panel were particularly effective at representing the genetic variation in Cebu Filipino samples. These results suggest that the HapMap data will be an effective resource for future studies in Cebu Filipino samples.

[The Haplotype Map of the human genome: a revolution in the genetics of complex diseases]

More than 99.9 % of the sequence is identical when comparing the DNA from two individuals. The remaining 0.1 % is responsible, along with other factors such as the environment, for the risk level of developing complex diseases (such as asthma, diabetes or cancer) or for the different pharmacological response to drugs. Despite the incredible advances in genomics in the past few years, identifying the variants involved remains difficult because of the prodigious amount of information to process. The recent completion of the Haplotype Map of the human genome has raised great hopes in the field as it is expected to help reduce the complexity of association studies and thus accelerate the discovery of genes associated with complex diseases. This review details the rationale behind the HapMap project, gives a summary of the results and also describes potential applications of the Haplotype Map.

Evaluating the performance of commercial whole-genome marker sets for capturing common genetic variation

Background

New technologies have enabled genome-wide association studies to be conducted with hundreds of thousands of genotyped SNPs. Several different first-generation genome-wide panels of SNPs have been commercialized. The total amount of common genetic variation is still unknown; however, the coverage of commercial panels can be evaluated against reference population samples genotyped by the International HapMap project. Less information is available about coverage in samples from other populations.

Results

In this study we compare four commercial panels: the HumanHap 300 and HumanHap 550 Array Sets from the Illumina Infinium series and the Mapping 100 K and Mapping 500 K Array Sets from the Affymetrix GeneChip series. Tagging performance is compared among HapMap CEPH (CEU), Asian (JPT, CHB) and Yoruba (YRI) population samples. It is also evaluated in an Estonian population sample with more than 1000 individuals genotyped in two 500-kbp ENCODE regions of chromosome 2: ENr112 on 2p16.3 and ENr131 on 2p37.1.

Conclusion

We found that in a non-reference Caucasian population, commercial SNP panels provide levels of coverage similar to those in the HapMap CEPH population sample. We present the proportions of universal and population-specific SNPs in all the commercial platforms studied.

The application of the HapMap to diabetic nephropathy and other causes of chronic renal failure

The human nuclear genome consists of approximately 3 billion nucleotides. Human beings are 99% similar in DNA sequence to each other, but natural genetic variation in approximately 1% of the DNA sequence is responsible for interindividual differences, including determining who will develop disease and who will remain healthy. The pace and timing of disease initiation also is regulated by exposure to individual-level environmental factors and other random causes. Therefore, an examination of the DNA sequences of individuals with and without diabetic nephropathy, or, more broadly, chronic renal failure, can predict which sequence differences vary with disease (or health). The technology is not yet economical enough to analyze large numbers of individuals down to each nucleotide, but standardized dense genotyping sets for interrogating 1 marker for every 5,000, 10,000, or 15,000 nucleotides now are affordable even in large samples. The swiftness with which disease-gene associations can be mined has improved radically as a result of the availability of discovery human genetic variation data from large-scale public and private initiatives, such as those provided by the International Haplotype Map Consortium and Perlegen Sciences, Inc. (Mountain View, CA). These projects have captured many of the common genetic variants (>1%) in the genome. This information has been buttressed with improvements in large-scale genotyping technologies and statistical methods for data analysis. In summary, the renal community is now poised for discovery of genes for chronic renal failure using these resources.

Using population mixtures to optimize the utility of genomic databases: linkage disequilibrium and association study design in India

When performing association studies in populations that have not been the focus of large-scale investigations of haplotype variation, it is often helpful to rely on genomic databases in other populations for study design and analysis - such as in the selection of tag SNPs and in the imputation of missing genotypes. One way of improving the use of these databases is to rely on a mixture of database samples that is similar to the population of interest, rather than using the single most similar database sample. We demonstrate the effectiveness of the mixture approach in the application of African, European, and East Asian HapMap samples for tag SNP selection in populations from India, a genetically intermediate region underrepresented in genomic studies of haplotype variation.

Tag SNPs chosen from HapMap perform well in several population isolates

Population isolates may be particularly useful for association studies of complex traits. This utility, however, largely depends on the transferability of tag SNPs chosen from reference samples, such as HapMap, to samples from such populations. Factors that characterize population isolates, such as widespread genetic drift, could impede such transferability. In this report, we show that tag SNPs chosen from HapMap perform well in several population isolates; this is true even for populations that differ substantially from the HapMap sample either in levels of linkage disequilibrium or in SNP allele frequency distributions.

Genome-wide association study of prostate cancer identifies a second risk locus at 8q24

Recently, common variants on human chromosome 8q24 were found to be associated with prostate cancer risk. While conducting a genome-wide association study in the Cancer Genetic Markers of Susceptibility project with 550,000 SNPs in a nested case-control study (1,172 cases and 1,157 controls of European origin), we identified a new association at 8q24 with an independent effect on prostate cancer susceptibility. The most significant signal is 70 kb centromeric to the previously reported SNP, rs1447295, but shows little evidence of linkage disequilibrium with it. A combined analysis with four additional studies (total: 4,296 cases and 4,299 controls) confirms association with prostate cancer for rs6983267 in the centromeric locus (P = 9.42 x 10(-13); heterozygote odds ratio (OR): 1.26, 95% confidence interval (c.i.): 1.13-1.41; homozygote OR: 1.58, 95% c.i.: 1.40-1.78). Each SNP remained significant in a joint analysis after adjusting for the other (rs1447295 P = 1.41 x 10(-11); rs6983267 P = 6.62 x 10(-10)). These observations, combined with compelling evidence for a recombination hotspot between the two markers, indicate the presence of at least two independent loci within 8q24 that contribute to prostate cancer in men of European ancestry. We estimate that the population attributable risk of the new locus, marked by rs6983267, is higher than the locus marked by rs1447295 (21% versus 9%).

Variation of estimates of SNP and haplotype diversity and linkage disequilibrium in samples from the same population due to experimental and evolutionary sample size

Studies of genetic polymorphisms and diversity between and within human populations are increasingly characterised by a very large number of genetic markers but using a relatively small number of individuals from which DNA samples were taken. In this report we examine the limitations of a small experimental sample size relative to a large genomic sample size, and quantify the sampling variance of a number of measures of diversity and linkage disequilibrium. The relationship between sample size and observed levels of polymorphism and haplotype diversity at the level of a gene is investigated under a neutral model of sequence evolution, using coalescent simulations. It is shown that the effect of evolutionary sampling, as manifested by differences between samples (genes) in measures of diversity estimated using very large sample sizes, is substantial, with a coefficient of variation of the number of detected polymorphic SNPs or haplotypes in the order of 15%. The effect of experimental design (sample size) is also very large, and a number of 'significant' results reported in the literature can be explained by sampling alone. The expected correlation coefficient of measures of linkage disequilibrium across samples from the same population has been quantified and found to be consistent with empirical estimates from the literature.

Female reproductive ageing: current knowledge and future trends

Over the past few decades, postponement of childbearing has led to a decrease in family size and increased rates of age-related female subfertility. Age-related decrease in ovarian follicle numbers and a decay in oocyte quality dictate the occurrence of natural loss of fecundity and, ultimately, menopause. The rate of this ovarian ageing process is highly variable among women. Identification of women who have severely decreased ovarian reserve for their age is, therefore, clinically relevant. Endocrine and imaging tests for ovarian reserve relate mainly to the quantitative aspect of ovarian reserve, but their capacity to predict the chances for pregnancy is limited. Genetic factors regulating the size of the follicle pool and the rate of its depletion might be identified in the near future and, possibly, assist the accurate prediction of a woman's reproductive lifespan.

Chasing genes for mood disorders and schizophrenia in genetically isolated populations

Major affective disorders and schizophrenia are among the most common brain diseases worldwide and their predisposition is influenced by a complex interaction of genetic and environmental factors. So far, traditional linkage mapping studies for these complex disorders have not achieved the same success as the positional cloning of genes for Mendelian diseases. The struggle to identify susceptibility genes for complex disorders has stimulated the development of alternative approaches, including studies in genetically isolated populations. Since isolated populations are likely to have both a reduced number of genetic vulnerability factors and environmental background and are therefore considered to be more homogeneous compared to outbred populations, the use of isolated populations in genetic studies is expected to improve the chance of finding susceptibility loci and genes. Here we review the role of isolated populations, based on linkage and association studies, in the identification of susceptibility genes for bipolar disorder and schizophrenia.

Intra- and interpopulation genotype reconstruction from tagging SNPs

The optimal method to be used for tSNP selection, the applicability of a reference LD map to unassayed populations, and the scalability of these methods to genome-wide analysis, all remain subjects of debate. We propose novel, scalable matrix algorithms that address these issues and we evaluate them on genotypic data from 38 populations and four genomic regions (248 SNPs typed for approximately 2000 individuals). We also evaluate these algorithms on a second data set consisting of genotypes available from the HapMap database (1336 SNPs for four populations) over the same genomic regions. Furthermore, we test these methods in the setting of a real association study using a publicly available family data set. The algorithms we use for tSNP selection and unassayed SNP reconstruction do not require haplotype inference and they are, in principle, scalable even to genome-wide analysis. Moreover, they are greedy variants of recently developed matrix algorithms with provable performance guarantees. Using a small set of carefully selected tSNPs, we achieve very good reconstruction accuracy of "untyped" genotypes for most of the populations studied. Additionally, we demonstrate in a quantitative manner that the chosen tSNPs exhibit substantial transferability, both within and across different geographic regions. Finally, we show that reconstruction can be applied to retrieve significant SNP associations with disease, with important genotyping savings.

LdCompare: rapid computation of single- and multiple-marker r2 and genetic coverage

The scale of genetic-variation datasets has increased enormously and the linkage equilibrium (LD) structure of these polymorphisms, particularly in whole-genome association studies, is of great interest. The significant computational complexity of calculating single- and multiple-marker correlations at a genome-wide scale remains challenging. We have developed a program that efficiently characterizes whole-genome LD structure on large number of SNPs in terms of single- and multiple-marker correlations.

AVAILABILITY

LdCompare is licensed under the GNU General Public License (GPL). Source code, documentation, testing datasets and precompiled executables are available for download at: http://www.affymetrix.com/support/developer/tools/devnettools.affx

A worldwide survey of haplotype variation and linkage disequilibrium in the human genome

Recent genomic surveys have produced high-resolution haplotype information, but only in a small number of human populations. We report haplotype structure across 12 Mb of DNA sequence in 927 individuals representing 52 populations. The geographic distribution of haplotypes reflects human history, with a loss of haplotype diversity as distance increases from Africa. Although the extent of linkage disequilibrium (LD) varies markedly across populations, considerable sharing of haplotype structure exists, and inferred recombination hotspot locations generally match across groups. The four samples in the International HapMap Project contain the majority of common haplotypes found in most populations: averaging across populations, 83% of common 20-kb haplotypes in a population are also common in the most similar HapMap sample. Consequently, although the portability of tag SNPs based on the HapMap is reduced in low-LD Africans, the HapMap will be helpful for the design of genome-wide association mapping studies in nearly all human populations.

How well do HapMap SNPs capture the untyped SNPs?

Background

The recent advancement in human genome sequencing and genotyping has revealed millions of single nucleotide polymorphisms (SNP) which determine the variation among human beings. One of the particular important projects is The International HapMap Project which provides the catalogue of human genetic variation for disease association studies. In this paper, we analyzed the genotype data in HapMap project by using National Institute of Environmental Health Sciences Environmental Genome Project (NIEHS EGP) SNPs. We first determine whether the HapMap data are transferable to the NIEHS data. Then, we study how well the HapMap SNPs capture the untyped SNPs in the region. Finally, we provide general guidelines for determining whether the SNPs chosen from HapMap may be able to capture most of the untyped SNPs.

Results

Our analysis shows that HapMap data are not robust enough to capture the untyped variants for most of the human genes. The performance of SNPs for European and Asian samples are marginal in capturing the untyped variants, i.e. approximately 55%. Expectedly, the SNPs from HapMap YRI panel can only capture approximately 30% of the variants. Although the overall performance is low, however, the SNPs for some genes perform very well and are able to capture most of the variants along the gene. This is observed in the European and Asian panel, but not in African panel. Through observation, we concluded that in order to have a well covered SNPs reference panel, the SNPs density and the association among reference SNPs are important to estimate the robustness of the chosen SNPs.

Conclusion

We have analyzed the coverage of HapMap SNPs using NIEHS EGP data. The results show that HapMap SNPs are transferable to the NIEHS SNPs. However, HapMap SNPs cannot capture some of the untyped SNPs and therefore resequencing may be needed to uncover more SNPs in the missing region.

Comparison of linkage disequilibrium patterns between the HapMap CEPH samples and a family-based cohort of Northern European descent

The International HapMap Consortium has determined the linkage disequilibrium (LD) patterns of four major human populations. The aim of our investigation was to compare the LD patterns of the HapMap CEPH (Centre d'Etude du Polymorphisme Humain) samples with a family-based cohort of similar ancestry to determine its usefulness as a reference population for disease association studies. We examined four genomic regions on chromosomes 7q, 12p, and 14q totaling 14.3 Mb, initially identified in our linkage study of obesity and the metabolic syndrome. Near identical patterns of LD were detected in both populations. Furthermore, tagSNPs selected based on the HapMap CEPH cohort data capture over 98% of the variants at an r2 > 0.8 in the disease cohort. This confirms the usefulness of the CEPH cohort of the HapMap as a reference sample for further investigations into the genomic variation of populations of Northern European descent.

Efficient selection of tagging single-nucleotide polymorphisms in multiple populations

Common genetic polymorphism may explain a portion of the heritable risk for common diseases, so considerable effort has been devoted to finding and typing common single-nucleotide polymorphisms (SNPs) in the human genome. Many SNPs show correlated genotypes, or linkage disequilibrium (LD), suggesting that only a subset of all SNPs (known as tagging SNPs, or tagSNPs) need to be genotyped for disease association studies. Based on the genetic differences that exist among human populations, most tagSNP sets are defined in a single population and applied only in populations that are closely related. To improve the efficiency of multi-population analyses, we have developed an algorithm called MultiPop-TagSelect that finds a near-minimal union of population-specific tagSNP sets across an arbitrary number of populations. We present this approach as an extension of LD-select, a tagSNP selection method that uses a greedy algorithm to group SNPs into bins based on their pairwise association patterns, although the MultiPop-TagSelect algorithm could be used with any SNP tagging approach that allows choices between nearly equivalent SNPs. We evaluate the algorithm by considering tagSNP selection in candidate-gene resequencing data and lower density whole-chromosome data. Our analysis reveals that an exhaustive search is often intractable, while the developed algorithm can quickly and reliably find near-optimal solutions even for difficult tagSNP selection problems. Using populations of African, Asian, and European ancestry, we also show that an optimal multi-population set of tagSNPs can be substantially smaller (up to 44%) than a typical set obtained through independent or sequential selection.