Using the plurality of codon positions to identify deleterious variants in human exomes

Population size influences the type of nucleotide variations in humans

Background

It is well known that the effective size of a population (N_e) is one of the major determinants of the amount of genetic variation within the population. However, it is unclear whether the types of genetic variations are also dictated by the effective population size. To examine this, we obtained whole genome data from over 100 populations of the world and investigated the patterns of mutational changes.

Results

Our results revealed that for low frequency variants, the ratio of AT→GC to GC→AT variants (β) was similar across populations, suggesting the similarity of the pattern of mutation in various populations. However, for high frequency variants, β showed a positive correlation with the effective population size of the populations. This suggests a much higher proportion of high frequency AT→GC variants in large populations (e.g. Africans) compared to those with small population sizes (e.g. Asians). These results imply that the substitution patterns vary significantly between populations. These findings could be explained by the effect of GC-biased gene conversion (gBGC), which favors the fixation of G/C over A/T variants in populations. In large population, gBGC causes high β. However, in small populations, genetic drift reduces the effect of gBGC resulting in reduced β. This was further confirmed by a positive relationship between N_e and β for homozygous variants.

Conclusions

Our results highlight the huge variation in the types of homozygous and high frequency polymorphisms between world populations. We observed the same pattern for deleterious variants, implying that the homozygous polymorphisms associated with recessive genetic diseases will be more enriched with G or C in populations with large N_e (e.g. Africans) than in populations with small N_e (e.g. Europeans).

OVA: integrating molecular and physical phenotype data from multiple biomedical domain ontologies with variant filtering for enhanced variant prioritization

MOTIVATION

Exome sequencing has become a de facto standard method for Mendelian disease gene discovery in recent years, yet identifying disease-causing mutations among thousands of candidate variants remains a non-trivial task.

RESULTS

Here we describe a new variant prioritization tool, OVA (ontology variant analysis), in which user-provided phenotypic information is exploited to infer deeper biological context. OVA combines a knowledge-based approach with a variant-filtering framework. It reduces the number of candidate variants by considering genotype and predicted effect on protein sequence, and scores the remainder on biological relevance to the query phenotype.We take advantage of several ontologies in order to bridge knowledge across multiple biomedical domains and facilitate computational analysis of annotations pertaining to genes, diseases, phenotypes, tissues and pathways. In this way, OVA combines information regarding molecular and physical phenotypes and integrates both human and model organism data to effectively prioritize variants. By assessing performance on both known and novel disease mutations, we show that OVA performs biologically meaningful candidate variant prioritization and can be more accurate than another recently published candidate variant prioritization tool.

AVAILABILITY AND IMPLEMENTATION

OVA is freely accessible at http://dna2.leeds.ac.uk:8080/OVA/index.jsp.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CONTACT

umaan@leeds.ac.uk.