Daniel Shriner

Improved coreceptor usage prediction and genotypic monitoring of R5-to-X4 transition by motif analysis of human immunodeficiency virus type 1 env V3 loop sequences.

ABSTRACT Early in infection, human immunodeficiency virus type 1 (HIV-1) generally uses the CCR5 chemokine receptor (along with CD4) for cellular entry. In many HIV-1-infected individuals, viral genotypic changes arise that allow the virus to use CXCR4 (either in addition to CCR5 or alone) as an entry coreceptor. This switch has been associated with an acceleration of both CD3+ T-cell decline and progression to AIDS. While it is well known that the V3 loop of gp120 largely determines coreceptor usage and that positively charged residues in V3 play an important role, the process of genetic change in V3 leading to altered coreceptor usage is not well understood. Further, the methods for biological phenotyping of virus for research or clinical purposes are laborious, depend on sample availability, and present biosafety concerns, so reliable methods for sequence-based“ virtual phenotyping” are desirable. We introduce a simple bioinformatic method of scoring V3 amino acid sequences that reliably predicts CXCR4 usage (sensitivity, 84%; specificity, 96%). This score (as determined on the basis of position-specific scoring matrices [PSSM]) can be interpreted as revealing a propensity to use CXCR4 as follows: known R5 viruses had low scores, R5X4 viruses had intermediate scores, and X4 viruses had high scores. Application of the PSSM scoring method to reconstructed virus phylogenies of 11 longitudinally sampled individuals revealed that the development of X4 viruses was generally gradual and involved the accumulation of multiple amino acid changes in V3. We found that X4 viruses were lost in two ways: by the dying off of an established X4 lineage or by mutation back to low-scoring V3 loops.

Detection of Gene × Gene Interactions in Genome-Wide Association Studies of Human Population Data

Empirical evidence supporting the commonality of gene × gene interactions, coupled with frequent failure to replicate results from previous association studies, has prompted statisticians to develop methods to handle this important subject. Nonparametric methods have generated intense interest because of their capacity to handle high-dimensional data. Genome-wide association analysis of large-scale SNP data is challenging mathematically and computationally. In this paper, we describe major issues and questions arising from this challenge, along with methodological implications. Data reduction and pattern recognition methods seem to be the new frontiers in efforts to detect gene × gene interactions comprehensively. Currently, there is no single method that is recognized as the ‘best’ for detecting, characterizing, and interpreting gene × gene interactions. Instead, a combination of approaches with the aim of balancing their specific strengths may be the optimal approach to investigate gene × gene interactions in human data.

Selection on the human immunodeficiency virus type 1 proteome following primary infection.

ABSTRACT Typically during human immunodeficiency virus type 1 (HIV-1) infection, a nearly homogeneous viral population first emerges and then diversifies over time due to selective forces that are poorly understood. To identify these forces, we conducted an intensive longitudinal study of viral genetic changes and T-cell immunity in one subject at ≤17 time points during his first 3 years of infection, and in his infecting partner near the time of transmission. Autologous peptides covering amino acid sites inferred to be under positive selection were powerful for identifying HIV-1-specific cytotoxic-T-lymphocyte (CTL) epitopes. Positive selection and mutations resulting in escape from CTLs occurred across the viral proteome. We detected 25 CTL epitopes, including 14 previously unreported. Seven new epitopes mapped to the viral Env protein, emphasizing Env as a major target of CTLs. One-third of the selected sites were associated with epitopic mutational escapes from CTLs. Most of these resulted from replacement with amino acids found at low database frequency. Another one-third represented acquisition of amino acids found at high database frequency, suggesting potential reversions of CTL epitopic sites recognized by the immune system of the transmitting partner and mutation toward improved viral fitness in the absence of immune targeting within the recipient. A majority of the remaining selected sites occurred in the envelope protein and may have been subjected to humoral immune selection. Hence, a majority of the amino acids undergoing selection in this subject appeared to result from fitness-balanced CTL selection, confirming CTLs as a dominant selective force in HIV-1 infection.

Potential impact of recombination on sitewise approaches for detecting positive natural selection.

Current sitewise methods for detecting positive selection on gene sequences (the de facto standard being the CODEML method (Yang et al., 2000)) assume no recombination. This paper presents simulation results indicating that violation of this assumption can lead to false positive detection of sites undergoing positive selection. Through the use of population-scaled mutation and recombination rates, simulations can be performed that permit the generation of appropriate null distributions corresponding to neutral expectations in the presence of recombination, thereby allowing for a more accurate estimation of positive selection.

Genome-Wide Association of Body Fat Distribution in African Ancestry Populations Suggests New Loci

Central obesity, measured by waist circumference (WC) or waist-hip ratio (WHR), is a marker of body fat distribution. Although obesity disproportionately affects minority populations, few studies have conducted genome-wide association study (GWAS) of fat distribution among those of predominantly African ancestry (AA). We performed GWAS of WC and WHR, adjusted and unadjusted for BMI, in up to 33,591 and 27,350 AA individuals, respectively. We identified loci associated with fat distribution in AA individuals using meta-analyses of GWA results for WC and WHR (stage 1). Overall, 25 SNPs with single genomic control (GC)-corrected p-values<5.0×10−6 were followed-up (stage 2) in AA with WC and with WHR. Additionally, we interrogated genomic regions of previously identified European ancestry (EA) WHR loci among AA. In joint analysis of association results including both Stage 1 and 2 cohorts, 2 SNPs demonstrated association, rs2075064 at LHX2, p = 2.24×10−8 for WC-adjusted-for-BMI, and rs6931262 at RREB1, p = 2.48×10−8 for WHR-adjusted-for-BMI. However, neither signal was genome-wide significant after double GC-correction (LHX2: p = 6.5×10−8; RREB1: p = 5.7×10−8). Six of fourteen previously reported loci for waist in EA populations were significant (p<0.05 divided by the number of independent SNPs within the region) in AA studied here (TBX15-WARS2, GRB14, ADAMTS9, LY86, RSPO3, ITPR2-SSPN). Further, we observed associations with metabolic traits: rs13389219 at GRB14 associated with HDL-cholesterol, triglycerides, and fasting insulin, and rs13060013 at ADAMTS9 with HDL-cholesterol and fasting insulin. Finally, we observed nominal evidence for sexual dimorphism, with stronger results in AA women at the GRB14 locus (p for interaction = 0.02). In conclusion, we identified two suggestive loci associated with fat distribution in AA populations in addition to confirming 6 loci previously identified in populations of EA. These findings reinforce the concept that there are fat distribution loci that are independent of generalized adiposity.

Evolutionary Indicators of Human Immunodeficiency Virus Type 1 Reservoirs and Compartments

ABSTRACT In vivo virologic compartments are cell types or tissues between which there is a restriction of virus flow, while virologic reservoirs are cell types or tissues in which there is a relative restriction of replication. The distinction between reservoirs and compartments is important because therapies that would be effective against a reservoir may not be effective against viruses produced by a given compartment, and vice versa. For example, the use of cytokines to “flush out” long-lived infected cells in patients on highly active antiretroviral therapy (T. W. Chun, D. Engel, M. M. Berrey, T. Shea, L. Corey, and A. S. Fauci, Proc. Natl. Acad. Sci. USA 95:8869-8873, 1998) may be successful for a latent reservoir but may not impact a compartment in which virus continues to replicate because of poor drug penetration. Here, we suggest phylogenetic criteria to illustrate, define, and differentiate between reservoirs and compartments. We then apply these criteria to the analysis of simulated and actual human immunodeficiency virus type 1 sequence data sets. We report that existing statistical methods work quite well at detecting viral compartments, and we learn from simulations that viral divergence from a calculated most recent common ancestor is a strong predictor of viral reservoirs.

R/qtlbim: QTL with Bayesian Interval Mapping in experimental crosses

UNLABELLED R/qtlbim is an extensible, interactive environment for the Bayesian Interval Mapping of QTL, built on top of R/qtl (Broman et al., 2003), providing Bayesian analysis of multiple interacting quantitative trait loci (QTL) models for continuous, binary and ordinal traits in experimental crosses. It includes several efficient Markov chain Monte Carlo (MCMC) algorithms for evaluating the posterior of genetic architectures, i.e. the number and locations of QTL, their main and epistatic effects and gene-environment interactions. R/qtlbim provides extensive informative graphical and numerical summaries, and model selection and convergence diagnostics of the MCMC output, illustrated through the vignette, example and demo capabilities of R (R Development Core Team 2006). AVAILABILITY The package is freely available from cran.r-project.org.

Moving toward System Genetics through Multiple Trait Analysis in Genome-Wide Association Studies

Association studies are a staple of genotype–phenotype mapping studies, whether they are based on single markers, haplotypes, candidate genes, genome-wide genotypes, or whole genome sequences. Although genetic epidemiological studies typically contain data collected on multiple traits which themselves are often correlated, most analyses have been performed on single traits. Here, I review several methods that have been developed to perform multiple trait analysis. These methods range from traditional multivariate models for systems of equations to recently developed graphical approaches based on network theory. The application of network theory to genetics is termed systems genetics and has the potential to address long-standing questions in genetics about complex processes such as coordinate regulation, homeostasis, and pleiotropy.

Genome-wide association of anthropometric traits in African- and African-derived populations

Genome-wide association (GWA) studies have identified common variants that are associated with a variety of traits and diseases, but most studies have been performed in European-derived populations. Here, we describe the first genome-wide analyses of imputed genotype and copy number variants (CNVs) for anthropometric measures in African-derived populations: 1188 Nigerians from Igbo-Ora and Ibadan, Nigeria, and 743 African-Americans from Maywood, IL. To improve the reach of our study, we used imputation to estimate genotypes at approximately 2.1 million single-nucleotide polymorphisms (SNPs) and also tested CNVs for association. No SNPs or common CNVs reached a genome-wide significance level for association with height or body mass index (BMI), and the best signals from a meta-analysis of the two cohorts did not replicate in approximately 3700 African-Americans and Jamaicans. However, several loci previously confirmed in European populations showed evidence of replication in our GWA panel of African-derived populations, including variants near IHH and DLEU7 for height and MC4R for BMI. Analysis of global burden of rare CNVs suggested that lean individuals possess greater total burden of CNVs, but this finding was not supported in an independent European population. Our results suggest that there are not multiple loci with strong effects on anthropometric traits in African-derived populations and that sample sizes comparable to those needed in European GWA studies will be required to identify replicable associations. Meta-analysis of this data set with additional studies in African-ancestry populations will be helpful to improve power to detect novel associations.

Genetic association for renal traits among participants of African Ancestry reveals new loci for renal function

Chronic kidney disease (CKD) is an increasing global public health concern, particularly among populations of African ancestry. We performed an interrogation of known renal loci, genome-wide association (GWA), and IBC candidate-gene SNP association analyses in African Americans from the CARe Renal Consortium. In up to 8,110 participants, we performed meta-analyses of GWA and IBC array data for estimated glomerular filtration rate (eGFR), CKD (eGFR <60 mL/min/1.73 m2), urinary albumin-to-creatinine ratio (UACR), and microalbuminuria (UACR >30 mg/g) and interrogated the 250 kb flanking region around 24 SNPs previously identified in European Ancestry renal GWAS analyses. Findings were replicated in up to 4,358 African Americans. To assess function, individually identified genes were knocked down in zebrafish embryos by morpholino antisense oligonucleotides. Expression of kidney-specific genes was assessed by in situ hybridization, and glomerular filtration was evaluated by dextran clearance. Overall, 23 of 24 previously identified SNPs had direction-consistent associations with eGFR in African Americans, 2 of which achieved nominal significance (UMOD, PIP5K1B). Interrogation of the flanking regions uncovered 24 new index SNPs in African Americans, 12 of which were replicated (UMOD, ANXA9, GCKR, TFDP2, DAB2, VEGFA, ATXN2, GATM, SLC22A2, TMEM60, SLC6A13, and BCAS3). In addition, we identified 3 suggestive loci at DOK6 (p-value = 5.3×10−7) and FNDC1 (p-value = 3.0×10−7) for UACR, and KCNQ1 with eGFR (p = 3.6×10−6). Morpholino knockdown of kcnq1 in the zebrafish resulted in abnormal kidney development and filtration capacity. We identified several SNPs in association with eGFR in African Ancestry individuals, as well as 3 suggestive loci for UACR and eGFR. Functional genetic studies support a role for kcnq1 in glomerular development in zebrafish.