A genome-wide study of lipid response to fenofibrate in Caucasians: a combined analysis of the GOLDN and ACCORD studies

Fibrates are commonly prescribed for hypertriglyceridemia but also lower low-density lipoprotein cholesterol (LDL-C) and raise high-density lipoprotein cholesterol (HDL-C). Large inter-individual variation in lipid response suggests that some persons may benefit more than others and genetic studies could help identify those persons

Differences in Candidate Gene Association between European Ancestry and African American Asthmatic Children

Candidate gene case-control studies have identified several single nucleotide polymorphisms (SNPs) that are associated with asthma susceptibility. Most of these studies have been restricted to evaluations of specific SNPs within a single gene and within populations from European ancestry. Recently, there is increasing interest in understanding racial differences in genetic risk associated with childhood asthma. Our aim was to compare association patterns of asthma candidate genes between children of European and African ancestry.Using a custom-designed Illumina SNP array, we genotyped 1,485 children within the Greater Cincinnati Pediatric Clinic Repository and Cincinnati Genomic Control Cohort for 259 SNPs in 28 genes and evaluated their associations with asthma. We identified 14 SNPs located in 6 genes that were significantly associated (p-values <0.05) with childhood asthma in African Americans. Among Caucasians, 13 SNPs in 5 genes were associated with childhood asthma. Two SNPs in IL4 were associated with asthma in both races (p-values <0.05). Gene-gene interaction studies identified race specific sets of genes that best discriminate between asthmatic children and non-allergic controls.We identified IL4 as having a role in asthma susceptibility in both African American and Caucasian children. However, while IL4 SNPs were associated with asthma in asthmatic children with European and African ancestry, the relative contributions of the most replicated asthma-associated SNPs varied by ancestry. These data provides valuable insights into the pathways that may predispose to asthma in individuals with European vs. African ancestry

Comparison of measures of marker informativeness for ancestry and admixture mapping

<p>Abstract</p> <p>Background</p> <p>Admixture mapping is a powerful gene mapping approach for an admixed population formed from ancestral populations with different allele frequencies. The power of this method relies on the ability of ancestry informative markers (AIMs) to infer ancestry along the chromosomes of admixed individuals. In this study, more than one million SNPs from HapMap databases and simulated data have been interrogated in admixed populations using various measures of ancestry informativeness: Fisher Information Content (FIC), Shannon Information Content (SIC), F statistics (F_ST), Informativeness for Assignment Measure (I_n), and the Absolute Allele Frequency Differences (delta, δ). The objectives are to compare these measures of informativeness to select SNP markers for ancestry inference, and to determine the accuracy of AIM panels selected by each measure in estimating the contributions of the ancestors to the admixed population.</p> <p>Results</p> <p>F_STand I_nhad the highest Spearman correlation and the best agreement as measured by Kappa statistics based on deciles. Although the different measures of marker informativeness performed comparably well, analyses based on the top 1 to 10% ranked informative markers of simulated data showed that I_nwas better in estimating ancestry for an admixed population.</p> <p>Conclusions</p> <p>Although millions of SNPs have been identified, only a small subset needs to be genotyped in order to accurately predict ancestry with a minimal error rate in a cost-effective manner. In this article, we compared various methods for selecting ancestry informative SNPs using simulations as well as SNP genotype data from samples of admixed populations and showed that the I_nmeasure estimates ancestry proportion (in an admixed population) with lower bias and mean square error.</p

Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities

This review explores the limitations of self-reported race, ethnicity, and genetic ancestry in biomedical research. Various terminologies are used to classify human differences in genomic research including race, ethnicity, and ancestry. Although race and ethnicity are related, race refers to a person’s physical appearance, such as skin color and eye color. Ethnicity, on the other hand, refers to communality in cultural heritage, language, social practice, traditions, and geopolitical factors. Genetic ancestry inferred using ancestry informative markers (AIMs) is based on genetic/genomic data. Phenotype-based race/ethnicity information and data computed using AIMs often disagree. For example, self-reporting African Americans can have drastically different levels of African or European ancestry. Genetic analysis of individual ancestry shows that some self-identified African Americans have up to 99% of European ancestry, whereas some self-identified European Americans have substantial admixture from African ancestry. Similarly, African ancestry in the Latino population varies between 3% in Mexican Americans to 16% in Puerto Ricans. The implication of this is that, in African American or Latino populations, self-reported ancestry may not be as accurate as direct assessment of individual genomic information in predicting treatment outcomes. To better understand human genetic variation in the context of health disparities, we suggest using “ancestry” (or biogeographical ancestry) to describe actual genetic variation, “race” to describe health disparity in societies characterized by racial categories, and “ethnicity” to describe traditions, lifestyle, diet, and values. We also suggest using ancestry informative markers for precise characterization of individuals’ biological ancestry. Understanding the sources of human genetic variation and the causes of health disparities could lead to interventions that would improve the health of all individuals

Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities

This review explores the limitations of self-reported race, ethnicity, and genetic ancestry in biomedical research. Various terminologies are used to classify human differences in genomic research including race, ethnicity, and ancestry. Although race and ethnicity are related, race refers to a person’s physical appearance, such as skin color and eye color. Ethnicity, on the other hand, refers to communality in cultural heritage, language, social practice, traditions, and geopolitical factors. Genetic ancestry inferred using ancestry informative markers (AIMs) is based on genetic/genomic data. Phenotype-based race/ethnicity information and data computed using AIMs often disagree. For example, self-reporting African Americans can have drastically different levels of African or European ancestry. Genetic analysis of individual ancestry shows that some self-identified African Americans have up to 99% of European ancestry, whereas some self-identified European Americans have substantial admixture from African ancestry. Similarly, African ancestry in the Latino population varies between 3% in Mexican Americans to 16% in Puerto Ricans. The implication of this is that, in African American or Latino populations, self-reported ancestry may not be as accurate as direct assessment of individual genomic information in predicting treatment outcomes. To better understand human genetic variation in the context of health disparities, we suggest using “ancestry” (or biogeographical ancestry) to describe actual genetic variation, “race” to describe health disparity in societies characterized by racial categories, and “ethnicity” to describe traditions, lifestyle, diet, and values. We also suggest using ancestry informative markers for precise characterization of individuals’ biological ancestry. Understanding the sources of human genetic variation and the causes of health disparities could lead to interventions that would improve the health of all individuals

Assessing the effects of subpopulations on the application of forensic DNA profiling.

Currently, UK forensic service providers (FSPs) tend to employ three geographically-broad databases when estimating profile frequencies based on a standard SGM Plus® DNA profile. These estimations will typically include correction factors to take into account issues such as substructuring of populations and sampling inefficiencies. It has been shown previously that regional genetic variation within the UK ‘Caucasian’ population is negligible but consideration has to be made for profiles which may originate from an individual of a more genetically isolated population. Samples were collected from Indian, Pakistani and UK (white British) donors; as well as Kalash individuals, a small population from the Khyber Pakhtunkhwa region in the North West of Pakistan. These were profiled using the SGM Plus® and Identifiler® kits and databases for each population were compiled. The greatest pairwise FST was seen between the Kalash and Indian population at 2.9 %. Allele frequency data were collected for each population and each sample’s profile frequency was estimated against all other databases to see whether samples reported a more conservative profile frequency (higher match probability) in their cognate database or in that of another population. A combined database comprising the Indian, Pakistani and previously published Bangladeshi data was also formed and used to calculate the level of correction required to make all samples of a population report a more conservative profile frequency in this combined database as opposed to their cognates. At the standard FST correction of 3 % – the minimum correction used by some FSPs, 94 % of the UK samples reported a more conservative profile frequency in the South Asian database; the lowest proportion that did so from all four populations. The Kalash dataset required the highest correction factor at FST = 12 % to make 100 % of samples report more conservative match probabilities when measured against the combined database. It was established that the current levels of correction applied to profile frequency calculations were more than sufficient; with random match probabilities remaining in the order of less than one in one billion for all samples in all databases with a correction of FST = 5 %. Although significant pairwise FST differences were observed as well as significant differentiation between populations across all SGM Plus® loci, no evidence of substructuring was detected using a program which employs a Bayesian probabilistic clustering approach, STRUCTURE, likely due to an insufficient number of samples and number of loci tested. Marked differences were seen in allele frequencies of the Kalash population, which also exhibited the highest affiliation to their cognate database, at least 80 %, with or without correction. AMOVA analysis also confirmed the greatest variance between groups was seen when the Kalash were kept as a separate entity from the other South Asian populations. Although current UK practice for applying FST correction prior to estimating STR match probabilities seems generous, there will be occasions when an estimation may appear less conservative when based on a broad database. Conversely, in this study, the one in one billion match probability ceiling threshold was not exceeded for any sample being compared to all databases. Therefore, although consideration should be given to a suspect’s reference population prior to frequency estimation, the current correction factors applied should be sufficient in the vast majority of cases. In instances where partial profiles are obtained, this caused little effect on the estimation of geographic origin, compared to full profiles, with the populations used in this study