Christopher S. Carlson

High-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels are influenced by both genes and the environment. Genome-wide association studies (GWAS) have identified ~100 common genetic variants associated with HDL-C, LDL-C, and/or TG levels, mostly in populations of European descent, but little is known about the modifiers of these associations. Here, we investigated whether GWAS-identified SNPs for lipid traits exhibited heterogeneity by sex in the Population Architecture using Genomics and Epidemiology (PAGE) study. A sex-stratified meta-analysis was performed for 49 GWAS-identified SNPs for fasting HDL-C, LDL-C, and ln(TG) levels among adults self-identified as European American (25,013). Heterogeneity by sex was established when phet < 0.001. There was evidence for heterogeneity by sex for two SNPs for ln(TG) in the APOA1/C3/A4/A5/BUD13 gene cluster: rs28927680 (phet = 7.4x10-7) and rs3135506 (phet = 4.3x10-4), one SNP in PLTP for HDL levels (rs7679; phet = 9.9x10-4), and one in HMGCR for LDL levels (rs12654264; phet = 3.1x10-5). We replicated heterogeneity by sex in five of seventeen loci previously reported by genome-wide studies (binomial p = 0.0009). We also present results for other racial/ethnic groups in the supplementary materials, to provide a resource for future meta-analyses. We provide further evidence for sex-specific effects of SNPs in the APOA1/C3/A4/A5/BUD13 gene cluster, PLTP, and HMGCR on fasting triglyceride levels in European Americans from the PAGE study. Our findings emphasize the need for considering context-specific effects when interpreting genetic associations emerging from GWAS, and also highlight the difficulties in replicating interaction effects across studies and across racial/ethnic groups.

The allele frequency spectrum of polymorphisms in DNA sequences can be used to test for signatures of natural selection that depart from the expected frequency spectrum under the neutral theory. We observed a significant (P = 0.001) correlation between the Tajima's D test statistic in full resequencing data and Tajima's D in a dense, genome-wide data set of genotyped polymorphisms for a set of 179 genes. Based on this, we used a sliding window analysis of Tajima's D across the human genome to identify regions putatively subject to strong, recent, selective sweeps. This survey identified seven Contiguous Regions of Tajima's D Reduction (CRTRs) in an African-descent population (AD), 23 in a European-descent population (ED), and 29 in a Chinese-descent population (XD). Only four CRTRs overlapped between populations: three between ED and XD and one between AD and ED. Full resequencing of eight genes within six CRTRs demonstrated frequency spectra inconsistent with neutral expectations for at least one gene within each CRTR. Identification of the functional polymorphism (and/or haplotype) responsible for the selective sweeps within each CRTR may provide interesting insights into the strongest selective pressures experienced by the human genome over recent evolutionary history.

In their paper in Bioinformatics, Mitchell et al. (2004) constructed a model of single nucleotide polymorphism (SNP) genotyping errors in different studies, and based on this model concluded that our study (Carlson et al., 2003) missed 99% of heterozygous genotypes when the minor allele was present only in the heterozygous state. We show here that this oversimplified model strikingly fails to fit many descriptors of our data, such as nucleotide diversity and allele frequency distribution, and that the results of Mitchell et al. are based on an incorrect value of one of the two statistics that they used to describe our data.

Identification of the genetic polymorphisms that contribute to susceptibility for common diseases such as type 2 diabetes and schizophrenia will aid in the development of diagnostics and therapeutics. Previous studies have focused on the technique of genetic linkage, but new technologies and experimental resources make whole-genome association studies more feasible. Association studies of this type have good prospects for dissecting the genetics of common disease, but they currently face a number of challenges, including problems with multiple testing and study design, definition of intermediate phenotypes and interaction between polymorphisms.

More than 5 million single-nucleotide polymorphisms (SNPs) with minor-allele frequency greater than 10% are expected to exist in the human genome. Some of these SNPs may be associated with risk of developing common diseases. To assess the power of currently available SNPs to detect such associations, we resequenced 50 genes in two ethnic samples and measured patterns of linkage disequilibrium between the subset of SNPs reported in dbSNP and the complete set of common SNPs. Our results suggest that using all 2.7 million SNPs currently in the database would detect nearly 80% of all common SNPs in European populations but only 50% of those common in the African American population and that efficient selection of a minimal subset of SNPs for use in association studies requires measurement of allele frequency and linkage disequilibrium relationships for all SNPs in dbSNP.