Final Report - DE-FG03-97ER62385

OAK B188 Single nucleotide polymorphism (SNPs) are the most common form of sequence variation in the human genome, and based on their natural frequency, they are also likely to be the underlying cause of most phenotypic differences in humans. Since SNPs are found in both coding and non-coding regions of the genome, randomly distributed markers as well as markers clustered in genes can be discovered. The majority of SNPs found in coding regions (cSNPs) are single base substitutions that may or may not lead to amino acid substitutions. Some cSNPs can result in changes in the activity of protein by altering a functionally important amino acid residue(s), and these are of interest for their potential links with phenotype, e.g. disease susceptibility or resistance. Other cSNPs (synonymous and non-synonymous) may prove useful for their potential links to functional cSNPs via linkage disequilibrium mapping. With funding from the Department of Energy, we have explored approaches: (1) to improve technology to find SNPs in the human genome using fluorescence-based sequencing, and (2) to mine SNPs from genome resources such as expressed-sequence tag (EST) sequences

Comparison of exon 5 sequences from 35 class I genes of the BALB/c mouse

DNA sequences of the fifth exon, which encodes the transmembrane domain, were determined for the BALB/c mouse class I MHC genes and used to study the relationships between them. Based on nucleotide sequence similarity, the exon 5 sequences can be divided into seven groups. Although most members within each group are at least 80% similar to each other, comparison between groups reveals that the groups share little similarity. However, in spite of the extensive variation of the fifth exon sequences, analysis of their predicted amino acid translations reveals that only four class I gene fifth exons have frameshifts or stop codons that terminate their translation and prevent them from encoding a domain that is both hydrophobic and long enough to span a lipid bilayer. Exactly 27 of the remaining fifth exons could encode a domain that is similar to those of the transplantation antigens in that it consists of a proline-rich connecting peptide, a transmembrane segment, and a cytoplasmic portion with membrane-anchoring basic residues. The conservation of this motif in the majority of the fifth exon translations in spite of extensive variation suggests that selective pressure exists for these exons to maintain their ability to encode a functional transmembrane domain, raising the possibility that many of the nonclassical class I genes encode functionally important products

The utility of single nucleotide polymorphisms in inferences of population history

Single nucleotide polymorphisms (SNPs) represent the most widespread type of sequence variation in genomes, yet they have only emerged recently as valuable genetic markers for revealing the evolutionary history of populations. Their occurrence throughout the genome also makes them ideal for analyses of speciation and historical demography, especially in light of recent theory suggesting that many unlinked nuclear loci are needed to estimate population genetic parameters with statistical confidence. In spite of having lower variation compared with microsatellites, SNPs should make the comparison of genomic diversities and histories of different species (the core goal of comparative biogeography) more straightforward than has been possible with microsatellites. The most pervasive, but correctable, complication to SNP analysis is a bias towards analyzing only the most variable loci, an artifact that is usually introduced by the limited number of individuals used to screen initially for polymorphisms. Although the use of SNPs as markers in population studies is still new, innovative methods for SNP identification, automated screening, haplotype inference and statistical analysis might quickly make SNPs the marker of choice

Allele Frequency Matching Between SNPs Reveals an Excess of Linkage Disequilibrium in Genic Regions of the Human Genome

Significant interest has emerged in mapping genetic susceptibility for complex traits through whole-genome association studies. These studies rely on the extent of association, i.e., linkage disequilibrium (LD), between single nucleotide polymorphisms (SNPs) across the human genome. LD describes the nonrandom association between SNP pairs and can be used as a metric when designing maximally informative panels of SNPs for association studies in human populations. Using data from the 1.58 million SNPs genotyped by Perlegen, we explored the allele frequency dependence of the LD statistic r (2) both empirically and theoretically. We show that average r (2) values between SNPs unmatched for allele frequency are always limited to much less than 1 (theoretical [Image: see text] approximately 0.46 to 0.57 for this dataset). Frequency matching of SNP pairs provides a more sensitive measure for assessing the average decay of LD and generates average r (2) values across nearly the entire informative range (from 0 to 0.89 through 0.95). Additionally, we analyzed the extent of perfect LD (r (2) = 1.0) using frequency-matched SNPs and found significant differences in the extent of LD in genic regions versus intergenic regions. The SNP pairs exhibiting perfect LD showed a significant bias for derived, nonancestral alleles, providing evidence for positive natural selection in the human genome

Tracing Sub-Structure in the European American Population with PCA-Informative Markers

Genetic structure in the European American population reflects waves of migration and recent gene flow among different populations. This complex structure can introduce bias in genetic association studies. Using Principal Components Analysis (PCA), we analyze the structure of two independent European American datasets (1,521 individuals–307,315 autosomal SNPs). Individual variation lies across a continuum with some individuals showing high degrees of admixture with non-European populations, as demonstrated through joint analysis with HapMap data. The CEPH Europeans only represent a small fraction of the variation encountered in the larger European American datasets we studied. We interpret the first eigenvector of this data as correlated with ancestry, and we apply an algorithm that we have previously described to select PCA-informative markers (PCAIMs) that can reproduce this structure. Importantly, we develop a novel method that can remove redundancy from the selected SNP panels and show that we can effectively remove correlated markers, thus increasing genotyping savings. Only 150–200 PCAIMs suffice to accurately predict fine structure in European American datasets, as identified by PCA. Simulating association studies, we couple our method with a PCA-based stratification correction tool and demonstrate that a small number of PCAIMs can efficiently remove false correlations with almost no loss in power. The structure informative SNPs that we propose are an important resource for genetic association studies of European Americans. Furthermore, our redundancy removal algorithm can be applied on sets of ancestry informative markers selected with any method in order to select the most uncorrelated SNPs, and significantly decreases genotyping costs

Analysis of 6,515 exomes reveals a recent origin of most human protein-coding variants

Establishing the age of each mutation segregating in contemporary human populations is important to fully understand our evolutionary history1,2 and will help facilitate the development of new approaches for disease gene discovery3. Large-scale surveys of human genetic variation have reported signatures of recent explosive population growth4-6, notable for an excess of rare genetic variants, qualitatively suggesting that many mutations arose recently. To more quantitatively assess the distribution of mutation ages, we resequenced 15,336 genes in 6,515 individuals of European (n=4,298) and African (n=2,217) American ancestry and inferred the age of 1,146,401 autosomal single nucleotide variants (SNVs). We estimate that ~73% of all protein-coding SNVs and ~86% of SNVs predicted to be deleterious arose in the past 5,000-10,000 years. The average age of deleterious SNVs varied significantly across molecular pathways, and disease genes contained a significantly higher proportion of recently arisen deleterious SNVs compared to other genes. Furthermore, European Americans had an excess of deleterious variants in essential and Mendelian disease genes compared to African Americans, consistent with weaker purifying selection due to the out-of-Africa dispersal. Our results better delimit the historical details of human protein-coding variation, illustrate the profound effect recent human history has had on the burden of deleterious SNVs segregating in contemporary populations, and provides important practical information that can be used to prioritize variants in disease gene discovery

Genetic and seasonal determinants of vitamin D status in Confederated Salish and Kootenai Tribes (CSKT) participants

Background: Vitamin D is a hormone produced in the skin upon ultraviolet B (UVB) radiation. Vitamin D is a crucial regulator of calcium and phosphate levels for bone mineralization and other physiological roles. Vitamin D levels vary globally in human populations due to genetics, geography, and other demographic factors. It is estimated that 20-85 % of the variability in vitamin D levels is driven by genetic variation. To improve our understanding of contributors to vitamin D levels, we conducted a candidate-gene study in partnership with the Confederated Salish and Kootenai Tribes (CSKT). Methods: We recruited 472 CSKT study participants on the Flathead Reservation in Montana. Demographic factors included age, BMI, and gender (185 male and 287 female; ≥ 18 years old). Genomic DNA and plasma were isolated from whole blood. We sequenced 14 vitamin D regulatory candidate genes: CASR, CUBN, CYP2R1, CYP3A4,CYP24A1, CYP27B1, DHCR7, GC, RXRA, RXRB, RXRG, SULT2A1, UGT1A4, and VDR. We also measured plasma levels of vitamin D and vitamin D metabolites by liquid chromatography/mass-spectrometry (LC/MS), including the clinical marker of vitamin D status, 25-hydroxyvitamin D3 [25(OH)D3]. We tested demographic factors as well as common and rare genetic variants for statistical associations with vitamin D levels using bioinformatics software and R statistical programming language code. Results: We identified 7,370 total genetic variants with 8% (n = 585) of them being novel. We identified 60 genetic variants that may be of clinical significance (disease associated or predicted to influence medication response). Vitamin D levels were below sufficiency [25(OH)D3 + 25(OH)D2 levels \u3c 20 ng/mL] in 56 % of CSKT participants across the year. We observed seasonal vitamin D and metabolite level fluctuations in a seasonal, sinusoidal statistical model with peak concentrations in June – August and trough concentrations in December – February. In linear regression analysis, we found that age, BMI, season, and 5 variants in CUBN and CYP3A4 were significantly associated with 25(OH)D3 concentration (p-value\u3c 0.05). In logistic regression, we found that 4 variants in CUBN, CYP3A4, and UGT1A4 were associated with 25(OH)D sufficiency status [25(OH)D3 + 25(OH)D2 levels of 20 ng/mL] (p-value\u3c 0.05). Multivariate linear regression analysis revealed that genetic variation alone explained ~13% of the variability in 25(OH)D3 concentration in CSKT participants. Genetic variation and environmental factors together explained ~23 % of the variability in 25(OH)D3 concentration in CSKT participants. It is likely that genetic variation in additional genes and other environmental factors (e.g., dietary vitamin D intake) that were not included in this study explain the remaining variability in 25(OH)D3 concentration. Conclusion: This research addresses the need for increased inclusion of American Indian and Alaska Natives in precision medicine health research. We are the first to describe the contribution of season and genetics to vitamin D levels in an American Indian population. Our next steps will be to use these findings to perform mechanistic studies and develop interventional strategies for the CSKT people