Utilizing Genotype Imputation for the Augmentation of Sequence Data

In recent years, capabilities for genotyping large sets of single nucleotide polymorphisms (SNPs) has increased considerably with the ability to genotype over 1 million SNP markers across the genome. This advancement in technology has led to an increase in the number of genome-wide association studies (GWAS) for various complex traits. These GWAS have resulted in the implication of over 1500 SNPs associated with disease traits. However, the SNPs identified from these GWAS are not necessarily the functional variants. Therefore, the next phase in GWAS will involve the refining of these putative loci.A next step for GWAS would be to catalog all variants, especially rarer variants, within the detected loci, followed by the association analysis of the detected variants with the disease trait. However, sequencing a locus in a large number of subjects is still relatively expensive. A more cost effective approach would be to sequence a portion of the individuals, followed by the application of genotype imputation methods for imputing markers in the remaining individuals. A potentially attractive alternative option would be to impute based on the 1000 Genomes Project; however, this has the drawbacks of using a reference population that does not necessarily match the disease status and LD pattern of the study population. We explored a variety of approaches for carrying out the imputation using a reference panel consisting of sequence data for a fraction of the study participants using data from both a candidate gene sequencing study and the 1000 Genomes Project.Imputation of genetic variation based on a proportion of sequenced samples is feasible. Our results indicate the following sequencing study design guidelines which take advantage of the recent advances in genotype imputation methodology: Select the largest and most diverse reference panel for sequencing and genotype as many "anchor" markers as possible

Jet-induced cratering of a granular surface with application to lunar spaceports

The erosion of lunar soil by rocket exhaust plumes is investigated experimentally. This has identified the diffusion-driven flow in the bulk of the sand as an important but previously unrecognized mechanism for erosion dynamics. It has also shown that slow regime cratering is governed by the recirculation of sand in the widening geometry of the crater. Scaling relationships and erosion mechanisms have been characterized in detail for the slow regime. The diffusion-driven flow occurs in both slow and fast regime cratering. Because diffusion-driven flow had been omitted from the lunar erosion theory and from the pressure cratering theory of the Apollo and Viking era, those theories cannot be entirely correct.Comment: 13 pages, link to published version: http://cedb.asce.org/cgi/WWWdisplay.cgi?090000

Utilizing Genotype Imputation for the Augmentation of Sequence Data

Background: In recent years, capabilities for genotyping large sets of single nucleotide polymorphisms (SNPs) has increased considerably with the ability to genotype over 1 million SNP markers across the genome. This advancement in technology has led to an increase in the number of genome-wide association studies (GWAS) for various complex traits. These GWAS have resulted in the implication of over 1500 SNPs associated with disease traits. However, the SNPs identified from these GWAS are not necessarily the functional variants. Therefore, the next phase in GWAS will involve the refining of these putative loci. Methodology: A next step for GWAS would be to catalog all variants, especially rarer variants, within the detected loci, followed by the association analysis of the detected variants with the disease trait. However, sequencing a locus in a large number of subjects is still relatively expensive. A more cost effective approach would be to sequence a portion of the individuals, followed by the application of genotype imputation methods for imputing markers in the remaining individuals. A potentially attractive alternative option would be to impute based on the 1000 Genomes Project; however, this has the drawbacks of using a reference population that does not necessarily match the disease status and LD pattern of the study population. We explored a variety of approaches for carrying out the imputation using a reference panel consisting of sequence data for a fraction of the study participants using data from both a candidate gene sequencing study and the 1000 Genomes Project

Concordance values between the “true” genotype and most likely imputed genotype for <i>GENE1</i> for various number of “anchor” markers.

<p>Table presents results for scenario with 50% of the samples sequenced.</p

Comparison of concordance rates between the various imputation scenarios for <i>COMT</i>.

<p>The proportion of the sample used as the reference panel is displayed on the X-axis and the percent concordant between the “true” genotype and the imputed most likely genotype is displayed on the Y-axis.</p

Comparison of mean SNP imputation quality score between the various imputation scenarios for <i>GENE1</i>.

<p>The proportion of the sample used as the reference panel is displayed on the X-axis and the mean SNP imputation quality score is displayed on the Y-axis.</p

Imputation Scenario Design Summary.

<p>Imputation Scenario Design Summary.</p

Summary of sequence data for <i>GENE1</i> for variants with MAF>1% or in HapMap.

<p>*SNP Marker in HapMap; used as typed genotypes in all samples (i.e., markers on a GWAS SNP array).</p><p>MAF = minor allele frequency based on imputed “dosage” or expected genotype, position = physical base-pair location of the SNP based on build 36, ObsHET = observed heterozygote rate.</p

Comparison of mean SNP imputation quality score versus MAF for <i>COMT</i> imputation scenario 1.

<p>The MAF (group 1: 0≤MAF≤0.05, group 2: 0.05</p

Comparison of minimum SNP imputation quality score between the various imputation scenarios for <i>COMT</i>.

<p>The proportion of the sample used as the reference panel is displayed on the X-axis and the minimum SNP imputation quality score is displayed on the Y-axis.</p