Non-Synonymous and Synonymous Coding SNPs Show Similar Likelihood and Effect Size of Human Disease Association

Many DNA variants have been identified on more than 300 diseases and traits using Genome-Wide Association Studies (GWASs). Some have been validated using deep sequencing, but many fewer have been validated functionally, primarily focused on non-synonymous coding SNPs (nsSNPs). It is an open question whether synonymous coding SNPs (sSNPs) and other non-coding SNPs can lead to as high odds ratios as nsSNPs. We conducted a broad survey across 21,429 disease-SNP associations curated from 2,113 publications studying human genetic association, and found that nsSNPs and sSNPs shared similar likelihood and effect size for disease association. The enrichment of disease-associated SNPs around the 80th base in the first introns might provide an effective way to prioritize intronic SNPs for functional studies. We further found that the likelihood of disease association was positively associated with the effect size across different types of SNPs, and SNPs in the 3′untranslated regions, such as the microRNA binding sites, might be under-investigated. Our results suggest that sSNPs are just as likely to be involved in disease mechanisms, so we recommend that sSNPs discovered from GWAS should also be examined with functional studies

CRIS – service for input, storage and analysis of the biodiversity data of the cryptogams

Here we describe Cryptogamic Russian Information System (CRIS), a web service cataloguing the biodiversity of cryptogams: cyanobacteria, fungi (including lichens), and bryophytes. CRIS incorporates a wide spectrum of data types, allowing for greater ease of use. It is possible to print the labels for herbarium collections, to input literature references, media files, etc., using CRIS which has a flexible interface and specific technical abilities. Currently, CRIS contains ~ 90,000 herbarium records, including 67,861 records of bryophytes, 12,486 records of lichens and 3,800 records of cyanobacteria. Data analysis of the different taxonomic groups is provided below. Perspectives and directions for the future development of CRIS are discussed

Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++

Computational efforts to identify functional elements within genomes leverage comparative sequence information by looking for regions that exhibit evidence of selective constraint. One way of detecting constrained elements is to follow a bottom-up approach by computing constraint scores for individual positions of a multiple alignment and then defining constrained elements as segments of contiguous, highly scoring nucleotide positions. Here we present GERP++, a new tool that uses maximum likelihood evolutionary rate estimation for position-specific scoring and, in contrast to previous bottom-up methods, a novel dynamic programming approach to subsequently define constrained elements. GERP++ evaluates a richer set of candidate element breakpoints and ranks them based on statistical significance, eliminating the need for biased heuristic extension techniques. Using GERP++ we identify over 1.3 million constrained elements spanning over 7% of the human genome. We predict a higher fraction than earlier estimates largely due to the annotation of longer constrained elements, which improves one to one correspondence between predicted elements with known functional sequences. GERP++ is an efficient and effective tool to provide both nucleotide- and element-level constraint scores within deep multiple sequence alignments

Refining trait resilience: identifying engineering, ecological, and adaptive facets from extant measures of resilience

The current paper presents a new measure of trait resilience derived from three common mechanisms identified in ecological theory: Engineering, Ecological and Adaptive (EEA) resilience. Exploratory and confirmatory factor analyses of five existing resilience scales suggest that the three trait resilience facets emerge, and can be reduced to a 12-item scale. The conceptualization and value of EEA resilience within the wider trait and well-being psychology is illustrated in terms of differing relationships with adaptive expressions of the traits of the five-factor personality model and the contribution to well-being after controlling for personality and coping, or over time. The current findings suggest that EEA resilience is a useful and parsimonious model and measure of trait resilience that can readily be placed within wider trait psychology and that is found to contribute to individual well-bein

Non-synonymous and synonymous coding SNPs show similar likelihood and effect size of human disease association.

Many DNA variants have been identified on more than 300 diseases and traits using Genome-Wide Association Studies (GWASs). Some have been validated using deep sequencing, but many fewer have been validated functionally, primarily focused on non-synonymous coding SNPs (nsSNPs). It is an open question whether synonymous coding SNPs (sSNPs) and other non-coding SNPs can lead to as high odds ratios as nsSNPs. We conducted a broad survey across 21,429 disease-SNP associations curated from 2,113 publications studying human genetic association, and found that nsSNPs and sSNPs shared similar likelihood and effect size for disease association. The enrichment of disease-associated SNPs around the 80(th) base in the first introns might provide an effective way to prioritize intronic SNPs for functional studies. We further found that the likelihood of disease association was positively associated with the effect size across different types of SNPs, and SNPs in the 3' untranslated regions, such as the microRNA binding sites, might be under-investigated. Our results suggest that sSNPs are just as likely to be involved in disease mechanisms, so we recommend that sSNPs discovered from GWAS should also be examined with functional studies

Cumulative density of disease-associated SNPs in the first intron.

<p>The cumulative densities of disease-associated SNPs were plotted against the distance from the first intron start site. The density was peaked at the 80^th base from the first intron start site.</p

Impact of SNP types on the effect size of SNP-disease association.

<p>The median odds ratio±standard error was calculated for each of the nine SNP types using 9,574 curated SNP-disease associations. The number of distinct SNPs was specified in the parenthesis. The odds ratios of nonsense SNPs were significantly higher than those of other SNP types (p<0.05, Mann-whiney U test). The p-values between the odds ratios of nsSNPs and other type of SNPs were shown in the figure.</p

A curated quantitative disease-SNP association database.

<p>Starting from a list of all SNPs measured in the HapMap 3 project, we searched for their presence in all Medline abstracts, eliminating non-human studies. Significant SNP-disease associations were manually curated from the full text, and reviewed four rounds. SNP IDs were annotated using the UCSC genome browser for positions and function types and annotated using Entrez for associated genes. Disease mesh terms were compared with the Unified Medical Language System (UMLS) to select concept unique identifiers (CUIs).</p

Statistics of the curated quantitative human disease-SNP association database.

<p>Statistics of the curated quantitative human disease-SNP association database.</p

Likelihood of disease association for SNPs on current genotyping platforms.

<p>*A hypothetical platform combined from the above six platforms to report the likelihood of SNPs being associate with human disease for each SNP type.</p