Common single-nucleotide polymorphisms act in concert to affect plasma levels of high-density lipoprotein cholesterol

The identification of DNA sequence variants underlying human complex phenotypes remains a significant challenge for several reasons: individual variants can have small phenotypic effects or low population frequencies, and multiple allelic variants may act in concert to affect a trait. We evaluated the combined effect of allelic variants in seven genes involved in high-density lipoprotein (HDL) metabolism, using forward stepwise regression. Analysis of all known common single-nucleotide polymorphisms (SNPs) in the seven candidate genes revealed four variants that were associated with incremental changes in HDL cholesterol levels in three independent samples. Conversely, analysis of 660 polymorphisms in eight genes that do not appear to be involved in HDL metabolism did not identify any associations with plasma HDL-cholesterol levels. These data indicate that several common SNPs act in concert to influence plasma levels of HDL cholesterol.     

SNP2RFLP: a computational tool to facilitate genetic mapping using benchtop analysis of SNPs

Genome-wide analysis of single nucleotide polymorphism (SNP) markers is an extremely efficient means for genetic mapping of mutations or traits in mice. However, this approach often defines a relatively large recombinant interval. To facilitate the refinement of this interval, we developed the program SNP2RFLP. This program can be used to identify region-specific SNPs in which the polymorphic nucleotide creates a restriction fragment length polymorphism (RFLP) that can be readily assayed at the benchtop using restriction enzyme digestion of SNP-containing PCR products. The program permits user-defined queries that maximize the informative markers for a particular application. This facilitates fine-mapping in a region containing a mutation of interest, which should prove valuable to the mouse genetics community. SNP2RFLP and further details are publicly available at http://genetics.bwh.harvard.edu/snp2rflp/.

     

Assessing the evolutionary impact of amino acid mutations in the human genome

Quantifying the distribution of fitness effects among newly arising mutations in the human genome is key to resolving important debates in medical and evolutionary genetics. Here, we present a method for inferring this distribution using Single Nucleotide Polymorphism (SNP) data from a population with non-stationary demographic history (such as that of modern humans). Application of our method to 47,576 coding SNPs found by direct resequencing of 11,404 protein coding-genes in 35 individuals (20 European Americans and 15 African Americans) allows us to assess the relative contribution of demographic and selective effects to patterning amino acid variation in the human genome. We find evidence of an ancient population expansion in the sample with African ancestry and a relatively recent bottleneck in the sample with European ancestry. After accounting for these demographic effects, we find strong evidence for great variability in the selective effects of new amino acid replacing mutations. In both populations, the patterns of variation are consistent with a leptokurtic distribution of selection coefficients (e.g., gamma or log-normal) peaked near neutrality. Specifically, we predict 27–29% of amino acid changing (nonsynonymous) mutations are neutral or nearly neutral (|s|<0.01%), 30–42% are moderately deleterious (0.01%<|s|<1%), and nearly all the remainder are highly deleterious or lethal (|s|>1%). Our results are consistent with 10–20% of amino acid differences between humans and chimpanzees having been fixed by positive selection with the remainder of differences being neutral or nearly neutral. Our analysis also predicts that many of the alleles identified via whole-genome association mapping may be selectively neutral or (formerly) positively selected, implying that deleterious genetic variation affecting disease phenotype may be missed by this widely used approach for mapping genes underlying complex traits.     

Differential Relationship of DNA Replication Timing to Different Forms of Human Mutation and Variation

Human genetic variation is distributed nonrandomly across the genome, though the principles governing its distribution are only partially known. DNA replication creates opportunities for mutation, and the timing of DNA replication correlates with the density of SNPs across the human genome. To enable deeper investigation of how DNA replication timing relates to human mutation and variation, we generated a high-resolution map of the human genome’s replication timing program and analyzed its relationship to point mutations, copy number variations, and the meiotic recombination hotspots utilized by males and females. DNA replication timing associated with point mutations far more strongly than predicted from earlier analyses and showed a stronger relationship to transversion than transition mutations. Structural mutations arising from recombination-based mechanisms and recombination hotspots used more extensively by females were enriched in early-replicating parts of the genome, though these relationships appeared to relate more strongly to the genomic distribution of causative sequence features. These results indicate differential and sex-specific relationship of DNA replication timing to different forms of mutation and recombination.     

Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms

Currently, single-nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) of >5% are preferentially used in case-control association studies of common human diseases. Recent technological developments enable inexpensive and accurate genotyping of a large number of SNPs in thousands of cases and controls, which can provide adequate statistical power to analyze SNPs with MAF <5%. Our purpose was to determine whether evaluating rare SNPs in case-control association studies could help identify causal SNPs for common diseases. We suggest that slightly deleterious SNPs (sdSNPs) subjected to weak purifying selection are major players in genetic control of susceptibility to common diseases. We compared the distribution of MAFs of synonymous SNPs with that of nonsynonymous SNPs (1) predicted to be benign, (2) predicted to be possibly damaging, and (3) predicted to be probably damaging by PolyPhen. Our sources of data were the International HapMap Project, ENCODE, and the SeattleSNPs project. We found that the MAF distribution of possibly and probably damaging SNPs was shifted toward rare SNPs compared with the MAF distribution of benign and synonymous SNPs that are not likely to be functional. We also found an inverse relationship between MAF and the proportion of nsSNPs predicted to be protein disturbing. On the basis of this relationship, we estimated the joint probability that a SNP is functional and would be detected as significant in a case-control study. Our analysis suggests that including rare SNPs in genotyping platforms will advance identification of causal SNPs in case-control association studies, particularly as sample sizes increase.     

Large-scale identification and evolution indexing of tyrosine phosphorylation sites from murine brain

Metazoans employ reversible tyrosine phosphorylation to regulate innumerable biological processes. Thus, the large-scale identification of tyrosine phosphorylation sites from primary tissues is an essential step toward a molecular systems understanding of dynamic regulation in vivo. The relative paucity of phosphotyrosine has greatly limited its identification in large-scale phosphoproteomic experiments. However, using antiphosphotyrosine peptide immunoprecipitations, we report the largest study to date of tyrosine phosphorylation sites from primary tissue, identifying 414 unique tyrosine phosphorylation sites from murine brain. To measure the conservation of phosphorylated tyrosines and their surrounding residues, we constructed a computational pipeline and identified patterns of conservation within the signature of phosphotyrosine.     

SNP2RFLP: a computational tool to facilitate genetic mapping using benchtop analysis of SNPs

Genome-wide analysis of single nucleotide polymorphism (SNP) markers is an extremely efficient means for genetic mapping of mutations or traits in mice. However, this approach often defines a relatively large recombinant interval. To facilitate the refinement of this interval, we developed the program SNP2RFLP. This program can be used to identify region-specific SNPs in which the polymorphic nucleotide creates a restriction fragment length polymorphism (RFLP) that can be readily assayed at the benchtop using restriction enzyme digestion of SNP-containing PCR products. The program permits user-defined queries that maximize the informative markers for a particular application. This facilitates fine-mapping in a region containing a mutation of interest, which should prove valuable to the mouse genetics community. SNP2RFLP and further details are publicly available at http://genetics.bwh.harvard.edu/snp2rflp/.     

Increase of functional diversity by alternative splicing

A large-scale analysis of protein isoforms arising from alternative splicing shows that alternative splicing tends to insert or delete complete protein domains more frequently than expected by chance, whereas disruption of domains and other structural modules is less frequent. If domain regions are disrupted, the functional effect, as predicted from 3D structure, is frequently equivalent to removal of the entire domain. Also, short alternative splicing events within domains, which might preserve folded structure, target functional residues more frequently than expected. Thus, it seems that positive selection has had a major role in the evolution of alternative splicing.     

Homology-based functional proteomics by mass spectrometry: application to the Xenopus microtubule-associated proteome

The application of functional proteomics to important model organisms with unsequenced genomes is restricted because of the limited ability to identify proteins by conventional mass spectrometry (MS) methods. Here we applied MS and sequence-similarity database searching strategies to characterize the Xenopus laevis microtubule-associated proteome. We identified over 40 unique, and many novel, microtubule-bound proteins, as well as two macromolecular protein complexes involved in protein translation. This finding was corroborated by electron microscopy showing the presence of ribosomes on spindles assembled from frog egg extracts. Taken together, these results suggest that protein translation occurs on the spindle during meiosis in the Xenopus oocyte. These findings were made possible due to the application of sequence-similarity methods, which extended mass spectrometric protein identification capabilities by 2-fold compared to conventional methods.