Principal-component-based population structure adjustment in the North American Rheumatoid Arthritis Consortium data: impact of single-nucleotide polymorphism set and analysis method.

Population structure occurs when a sample is composed of individuals with different ancestries and can result in excess type I error in genome-wide association studies. Genome-wide principal-component analysis (PCA) has become a popular method for identifying and adjusting for subtle population structure in association studies. Using the Genetic Analysis Workshop 16 (GAW16) NARAC data, we explore two unresolved issues concerning the use of genome-wide PCA to account for population structure in genetic associations studies: the choice of single-nucleotide polymorphism (SNP) subset and the choice of adjustment model. We computed PCs for subsets of genome-wide SNPs with varying levels of LD. The first two PCs were similar for all subsets and the first three PCs were associated with case status for all subsets. When the PCs associated with case status were included as covariates in an association model, the reduction in genomic inflation factor was similar for all SNP sets. Several models have been proposed to account for structure using PCs, but it is not yet clear whether the different methods will result in substantively different results for association studies with individuals of European descent. We compared genome-wide association p-values and results for two positive-control SNPs previously associated with rheumatoid arthritis using four PC adjustment methods as well as no adjustment and genomic control. We found that in this sample, adjusting for the continuous PCs or adjusting for discrete clusters identified using the PCs adequately accounts for the case-control population structure, but that a recently proposed randomization test performs poorly.

Genome-wide association studies and the genetic dissection of complex traits.

The availability of affordable high throughput technology for parallel genotyping has opened the field of genetics to genome-wide association studies (GWAS), and in the last few years hundreds of articles reporting results of GWAS for a variety of heritable traits have been published. What do these results tell us? Although GWAS have discovered a few hundred reproducible associations, this number is underwhelming in relation to the huge amount of data produced, and challenges the conjecture that common variants may be the genetic causes of common diseases. We argue that the massive amount of genetic data that result from these studies remains largely unexplored and unexploited because of the challenge of mining and modeling enormous data sets, the difficulty of using nontraditional computational techniques and the focus of accepted statistical analyses on controlling the false positive rate rather than limiting the false negative rate. In this article, we will review the common approach to analysis of GWAS data and then discuss options to learn more from these data. We will use examples from our ongoing studies of sickle cell anemia and also GWAS in multigenic traits.

Imputation of missing genotypes: an empirical evaluation of IMPUTE.

BACKGROUND: Imputation of missing genotypes is becoming a very popular solution for synchronizing genotype data collected with different microarray platforms but the effect of ethnic background, subject ascertainment, and amount of missing data on the accuracy of imputation are not well understood. RESULTS: We evaluated the accuracy of the program IMPUTE to generate the genotype data of partially or fully untyped single nucleotide polymorphisms (SNPs). The program uses a model-based approach to imputation that reconstructs the genotype distribution given a set of referent haplotypes and the observed data, and uses this distribution to compute the marginal probability of each missing genotype for each individual subject that is used to impute the missing data. We assembled genome-wide data from five different studies and three different ethnic groups comprising Caucasians, African Americans and Asians. We randomly removed genotype data and then compared the observed genotypes with those generated by IMPUTE. Our analysis shows 97% median accuracy in Caucasian subjects when less than 10% of the SNPs are untyped and missing genotypes are accepted regardless of their posterior probability. The median accuracy increases to 99% when we require 0.95 minimum posterior probability for an imputed genotype to be acceptable. The accuracy decreases to 86% or 94% when subjects are African Americans or Asians. We propose a strategy to improve the accuracy by leveraging the level of admixture in African Americans. CONCLUSION: Our analysis suggests that IMPUTE is very accurate in samples of Caucasians origin, it is slightly less accurate in samples of Asians background, but substantially less accurate in samples of admixed background such as African Americans. Sample size and ascertainment do not seem to affect the accuracy of imputation.

BCL11A is a major HbF quantitative trait locus in three different populations with beta-hemoglobinopathies.

Increased HbF levels or F-cell (HbF containing erythrocyte) numbers can ameliorate the disease severity of beta-thalassemia major and sickle cell anemia. Recent genome-wide association studies reported that single nucleotide polymorphisms (SNPs) in BCL11A gene on chromosome 2p16.1 were correlated with F-cells among healthy northern Europeans, and HbF among Sardinians with beta-thalassemias. In this study, we showed that SNPs in BCL11A were associated with F-cell numbers in Chinese with beta-thalassemia trait, and with HbF levels in Thais with either beta-thalassemia or HbE trait and in African Americans with sickle cell anemia. Taken together, the data suggest that the functional motifs responsible for modulating F-cells and HbF levels reside within a 3 kb region in the second intron of BCL11A.