The prevalence of folate-remedial MTHFR enzyme variants in humans.

Studies of rare, inborn metabolic diseases establish that the phenotypes of some mutations in vitamin-dependent enzymes can be suppressed by supplementation of the cognate vitamin, which restores function of the defective enzyme. To determine whether polymorphisms exist that more subtly affect enzymes yet are augmentable in the same way, we sequenced the coding region of a prototypical vitamin-dependent enzyme, methylenetetrahydrofolate reductase (MTHFR), from 564 individuals of diverse ethnicities. All nonsynonymous changes were evaluated in functional in vivo assays in Saccharomyces cerevisiae to identify enzymatic defects and folate remediability of impaired alleles. We identified 14 nonsynonymous changes: 11 alleles with minor allele frequencies <1% and 3 common alleles (A222V, E429A, and R594Q). Four of 11 low-frequency alleles affected enzyme function, as did A222V. Of the five impaired alleles, four could be restored to normal functionality by elevating intracellular folate levels. All five impaired alleles mapped to the N-terminal catalytic domain of the enzyme, whereas changes in the C-terminal regulatory domain had little effect on activity. Impaired activity correlated with the phosphorylation state of MTHFR, with more severe mutations resulting in lower abundance of the phosphorylated protein. Significantly, diploid yeast heterozygous for mutant alleles were impaired for growth, particularly with lower folate supplementation. These results suggested that multiple less-frequent alleles, in aggregate, might significantly contribute to metabolic dysfunction. Furthermore, vitamin remediation of mutant enzymes may be a common phenomenon in certain domains of proteins.

New generation pharmacogenomic tools: a SNP linkage disequilibrium Map, validated SNP assay resource, and high-throughput instrumentation system for large-scale genetic studies.

Since public and private efforts announced the first draft of the human genome last year, researchers have reported great numbers of single nucleotide polymorphisms (SNPs). We believe that the availability of well-mapped, quality SNP markers constitutes the gateway to a revolution in genetics and personalized medicine that will lead to better diagnosis and treatment of common complex disorders. A new generation of tools and public SNP resources for pharmacogenomic and genetic studies--specifically for candidate-gene, candidate-region, and whole-genome association studies--will form part of the new scientific landscape. This will only be possible through the greater accessibility of SNP resources and superior high-throughput instrumentation-assay systems that enable affordable, highly productive large-scale genetic studies. We are contributing to this effort by developing a high-quality linkage disequilibrium SNP marker map and an accompanying set of ready-to-use, validated SNP assays across every gene in the human genome. This effort incorporates both the public sequence and SNP data sources, and Celera Genomics' human genome assembly and enormous resource ofphysically mapped SNPs (approximately 4,000,000 unique records). This article discusses our approach and methodology for designing the map, choosing quality SNPs, designing and validating these assays, and obtaining population frequency ofthe polymorphisms. We also discuss an advanced, high-performance SNP assay chemisty--a new generation of the TaqMan probe-based, 5' nuclease assay-and high-throughput instrumentation-software system for large-scale genotyping. We provide the new SNP map and validation information, validated SNP assays and reagents, and instrumentation systems as a novel resource for genetic discoveries.

Assessing gene expression variation in normal human tissues using GeneTag, a novel, global, sensitive profiling method.

GeneTag is a novel expression profiling method that allows the visualization, quantification and identification of expressed genes-whether known or novel-in any species, tissue or cell type, independent of knowledge of the underlying sequence. Here we describe the application of this method to determine variation of gene expression in individual human liver samples and the identification of tissue-specific genes by comparing expression patterns across several human organs. Expression data are stored in a database for future reference and data analysis relies on proprietary software, which allows complex comparisons to be performed. Differentially expressed genes are quickly identified through a link to a sequence database. The results from our study underscore the importance of knowledge of individual variation of gene expression for the design and interpretation of transcript profiling experiments in the context of any biological question.

High school students' attitudes about firearms policies.

PURPOSE: To examine high school students' attitudes about firearm policies and to compare their attitudes with those of adults. METHODS: The Hamilton Youth and Guns Poll is the first national survey of high school students about their attitudes concerning firearm policies. Questions were asked of 1005 sophomores, juniors, and seniors about their actual (i.e., direct) exposure (e.g., presence of a gun in the home) and about their social (i.e., indirect) exposure (e.g., whether the student could get a gun) to firearms and related violence. Population weights were applied, and multivariate logistic regression was used to examine the relationship between demographic and exposure variables and opinions about firearm policies. RESULTS: Most high school students supported more restrictive firearm policies. Opinions varied little by demographic variables with the exception of gender. Females were significantly more supportive of most firearm policies. Actual exposure was a more consistent predictor than social exposure. Students living in a home with a gun, particularly a handgun, were less likely to support most restrictive gun policies. CONCLUSIONS: Most high school students in the United States favor stringent policies governing firearms. Adolescents' attitudes about firearm policies parallel those of adults.

A genetic signature of spina bifida risk from pathway-informed comprehensive gene-variant analysis.

Despite compelling epidemiological evidence that folic acid supplements reduce the frequency of neural tube defects (NTDs) in newborns, common variant association studies with folate metabolism genes have failed to explain the majority of NTD risk. The contribution of rare alleles as well as genetic interactions within the folate pathway have not been extensively studied in the context of NTDs. Thus, we sequenced the exons in 31 folate-related genes in a 480-member NTD case-control population to identify the full spectrum of allelic variation and determine whether rare alleles or obvious genetic interactions within this pathway affect NTD risk. We constructed a pathway model, predetermined independent of the data, which grouped genes into coherent sets reflecting the distinct metabolic compartments in the folate/one-carbon pathway (purine synthesis, pyrimidine synthesis, and homocysteine recycling to methionine). By integrating multiple variants based on these groupings, we uncovered two provocative, complex genetic risk signatures. Interestingly, these signatures differed by race/ethnicity: a Hispanic risk profile pointed to alterations in purine biosynthesis, whereas that in non-Hispanic whites implicated homocysteine metabolism. In contrast, parallel analyses that focused on individual alleles, or individual genes, as the units by which to assign risk revealed no compelling associations. These results suggest that the ability to layer pathway relationships onto clinical variant data can be uniquely informative for identifying genetic risk as well as for generating mechanistic hypotheses. Furthermore, the identification of ethnic-specific risk signatures for spina bifida resonated with epidemiological data suggesting that the underlying pathogenesis may differ between Hispanic and non-Hispanic groups.

Identifying selected regions from heterozygosity and divergence using a light-coverage genomic dataset from two human populations.

When a selective sweep occurs in the chromosomal region around a target gene in two populations that have recently separated, it produces three dramatic genomic consequences: 1) decreased multi-locus heterozygosity in the region; 2) elevated or diminished genetic divergence (F(ST)) of multiple polymorphic variants adjacent to the selected locus between the divergent populations, due to the alternative fixation of alleles; and 3) a consequent regional increase in the variance of F(ST) (S(2)F(ST)) for the same clustered variants, due to the increased alternative fixation of alleles in the loci surrounding the selection target. In the first part of our study, to search for potential targets of directional selection, we developed and validated a resampling-based computational approach; we then scanned an array of 31 different-sized moving windows of SNP variants (5-65 SNPs) across the human genome in a set of European and African American population samples with 183,997 SNP loci after correcting for the recombination rate variation. The analysis revealed 180 regions of recent selection with very strong evidence in either population or both. In the second part of our study, we compared the newly discovered putative regions to those sites previously postulated in the literature, using methods based on inspecting patterns of linkage disequilibrium, population divergence and other methodologies. The newly found regions were cross-validated with those found in nine other studies that have searched for selection signals. Our study was replicated especially well in those regions confirmed by three or more studies. These validated regions were independently verified, using a combination of different methods and different databases in other studies, and should include fewer false positives. The main strength of our analysis method compared to others is that it does not require dense genotyping and therefore can be used with data from population-based genome SNP scans from smaller studies of humans or other species.

An efficient and high-throughput approach for experimental validation of novel human gene predictions.

A highly automated RT-PCR-based approach has been established to validate novel human gene predictions with no prior experimental evidence of mRNA splicing (ab initio predictions). Ab initio gene predictions were selected for high-throughput validation using predicted protein classification, sequence similarity to other genomes, colocalization with an MPSS tag, or microarray expression. Initial microarray prioritization followed by RT-PCR validation was the most efficient combination, resulting in approximately 35% of the ab initio predictions being validated by RT-PCR. Of the 7252 novel genes that were prioritized and processed, 796 constituted real transcripts. In addition, high-throughput RACE successfully extended the 5' and/or 3' ends of >60% of RT-PCR-validated genes. Reevaluation of these transcripts produced 574 novel transcripts using RefSeq as a reference. RT-PCR sequencing in combination with RACE on ab initio gene predictions could be used to define the transcriptome across all species.

Power and sample size calculations for genetic case/control studies using gene-centric SNP maps: application to human chromosomes 6, 21, and 22 in three populations.

Power and sample size calculations are critical parts of any research design for genetic association. We present a method that utilizes haplotype frequency information and average marker-marker linkage disequilibrium on SNPs typed in and around all genes on a chromosome. The test statistic used is the classic likelihood ratio test applied to haplotypes in case/control populations. Haplotype frequencies are computed through specification of genetic model parameters. Power is determined by computation of the test's non-centrality parameter. Power per gene is computed as a weighted average of the power assuming each haplotype is associated with the trait. We apply our method to genotype data from dense SNP maps across three entire chromosomes (6, 21, and 22) for three different human populations (African-American, Caucasian, Chinese), three different models of disease (additive, dominant, and multiplicative) and two trait allele frequencies (rare, common). We perform a regression analysis using these factors, average marker-marker disequilibrium, and the haplotype diversity across the gene region to determine which factors most significantly affect average power for a gene in our data. Also, as a 'proof of principle' calculation, we perform power and sample size calculations for all genes within 100 kb of the PSORS1 locus (chromosome 6) for a previously published association study of psoriasis. Results of our regression analysis indicate that four highly significant factors that determine average power to detect association are: disease model, average marker-marker disequilibrium, haplotype diversity, and the trait allele frequency. These findings may have important implications for the design of well-powered candidate gene association studies. Our power and sample size calculations for the PSORS1 gene appear consistent with published findings, namely that there is substantial power (>0.99) for most genes within 100 kb of the PSORS1 locus at the 0.01 significance level.

The linkage disequilibrium maps of three human chromosomes across four populations reflect their demographic history and a common underlying recombination pattern.

The extent and patterns of linkage disequilibrium (LD) determine the feasibility of association studies to map genes that underlie complex traits. Here we present a comparison of the patterns of LD across four major human populations (African-American, Caucasian, Chinese, and Japanese) with a high-resolution single-nucleotide polymorphism (SNP) map covering almost the entire length of chromosomes 6, 21, and 22. We constructed metric LD maps formulated such that the units measure the extent of useful LD for association mapping. LD reaches almost twice as far in chromosome 6 as in chromosomes 21 or 22, in agreement with their differences in recombination rates. By all measures used, out-of-Africa populations showed over a third more LD than African-Americans, highlighting the role of the population's demography in shaping the patterns of LD. Despite those differences, the long-range contour of the LD maps is remarkably similar across the four populations, presumably reflecting common localization of recombination hot spots. Our results have practical implications for the rational design and selection of SNPs for disease association studies.

A MORPHOLOGIC AND GENETIC STUDY OF THE ISLAND FOX, UROCYON LITTORALIS.

The Island Fox, Urocyon littoralis, is a dwarf form found on six of the Channel Islands located 30-98 km off the coast of southern California. The island populations differ in two variables that affect genetic variation: effective population size and duration of isolation. We estimate that the effective population size of foxes on the islands varies from approximately 150 to 1,000 individuals. Archeological and geological evidence suggests that foxes likely arrived on the three northern islands minimally 10,400-16,000 years ago and dispersed to the three southern islands 2,200-4,300 years ago. We use morphometrics, allozyme electrophoresis, mitochondrial DNA (mtDNA) restriction-site analysis, and analysis of hypervariable minisatellite DNA to measure variability within and distances among island fox populations. The amount of within-population variation is lowest for the smallest island populations and highest for the mainland population. However, the larger populations are sometimes less variable, with respect to some genetic measures, than expected. No distinct trends of variability with founding time are observed. Genetic distances among the island populations, as estimated by the four techniques, are not well correlated. The apparent lack of correspondence among techniques may reflect the effects of mutation rate and colonization history on the values of each genetic measure.

A high-density admixture map for disease gene discovery in african americans.

Admixture mapping (also known as "mapping by admixture linkage disequilibrium," or MALD) provides a way of localizing genes that cause disease, in admixed ethnic groups such as African Americans, with approximately 100 times fewer markers than are required for whole-genome haplotype scans. However, it has not been possible to perform powerful scans with admixture mapping because the method requires a dense map of validated markers known to have large frequency differences between Europeans and Africans. To create such a map, we screened through databases containing approximately 450000 single-nucleotide polymorphisms (SNPs) for which frequencies had been estimated in African and European population samples. We experimentally confirmed the frequencies of the most promising SNPs in a multiethnic panel of unrelated samples and identified 3011 as a MALD map (1.2 cM average spacing). We estimate that this map is approximately 70% informative in differentiating African versus European origins of chromosomal segments. This map provides a practical and powerful tool, which is freely available without restriction, for screening for disease genes in African American patient cohorts. The map is especially appropriate for those diseases that differ in incidence between the parental African and European populations.