Margaret G. Ehm

Testing association of statistically inferred haplotypes with discrete and continuous traits in samples of unrelated individuals.

There have been increasing efforts to relate drug efficacy and disease predisposition with genetic polymorphisms. We present statistical tests for association of haplotype frequencies with discrete and continuous traits in samples of unrelated individuals. Haplotype frequencies are estimated through the expectation-maximization algorithm, and each individual in the sample is expanded into all possible haplotype configurations with corresponding probabilities, conditional on their genotype. A regression-based approach is then used to relate inferred haplotype probabilities to the response. The relationship of this technique to commonly used approaches developed for case-control data is discussed. We confirm the proper size of the test under H₀ and find an increase in power under the alternative by comparing test results using inferred haplotypes with single-marker tests using simulated data. More importantly, analysis of real data comprised of a dense map of single nucleotide polymorphisms spaced along a 12-cM chromosomal region allows us to confirm the utility of the haplotype approach as well as the validity and usefulness of the proposed statistical technique. The method appears to be successful in relating data from multiple, correlated markers to response.

An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People

A Deep Look Into Our Genes Recent debates have focused on the degree of genetic variation and its impact upon health at the genomic level in humans (see the Perspective by Casals and Bertranpetit). Tennessen et al. (p. 64, published online 17 May), looking at all of the protein-coding genes in the human genome, and Nelson et al. (p. 100, published online 17 May), looking at genes that encode drug targets, address this question through deep sequencing efforts on samples from multiple individuals. The findings suggest that most human variation is rare, not shared between populations, and that rare variants are likely to play a role in human health. A pharmacogenomics analysis shows how challenging it will be to associate rare variants with phenotypes. Rare genetic variants contribute to complex disease risk; however, the abundance of rare variants in human populations remains unknown. We explored this spectrum of variation by sequencing 202 genes encoding drug targets in 14,002 individuals. We find rare variants are abundant (1 every 17 bases) and geographically localized, so that even with large sample sizes, rare variant catalogs will be largely incomplete. We used the observed patterns of variation to estimate population growth parameters, the proportion of variants in a given frequency class that are putatively deleterious, and mutation rates for each gene. We conclude that because of rapid population growth and weak purifying selection, human populations harbor an abundance of rare variants, many of which are deleterious and have relevance to understanding disease risk.

An autosomal genomic scan for loci linked to type II diabetes mellitus and body-mass index in Pima Indians

Genetic factors influence the development of type II diabetes mellitus, but genetic loci for the most common forms of diabetes have not been identified. A genomic scan was conducted to identify loci linked to diabetes and body-mass index (BMI) in Pima Indians, a Native American population with a high prevalence of type II diabetes. Among 264 nuclear families containing 966 siblings, 516 autosomal markers with a median distance between adjacent markers of 6.4 cM were genotyped. Variance-components methods were used to test for linkage with an age-adjusted diabetes score and with BMI. In multipoint analyses, the strongest evidence for linkage with age-adjusted diabetes (LOD = 1.7) was on chromosome 11q, in the region that was also linked most strongly with BMI (LOD = 3.6). Bivariate linkage analyses strongly rejected both the null hypothesis of no linkage with either trait and the null hypothesis of no contribution of the locus to the covariation among the two traits. Sib-pair analyses suggest additional potential diabetes-susceptibility loci on chromosomes 1q and 7q.

Detecting Marker-Disease Association by Testing for Hardy-Weinberg Disequilibrium at a Marker Locus

We review and extend a recent suggestion that fine-scale localization of a disease-susceptibility locus for a complex disease be done on the basis of deviations from Hardy-Weinberg equilibrium among affected individuals. This deviation is driven by linkage disequilibrium between disease and marker loci in the whole population and requires a heterogeneous genetic basis for the disease. A finding of marker-locus Hardy-Weinberg disequilibrium therefore implies disease heterogeneity and marker-disease linkage disequilibrium. Although a lack of departure of Hardy-Weinberg disequilibrium at marker loci implies that disease susceptibilityweighted linkage disequilibria are zero, given disease heterogeneity, it does not follow that the usual measures of linkage disequilibrium are zero. For disease-susceptibility loci with more than two alleles, therefore, care is needed in the drawing of inferences from marker Hardy-Weinberg disequilibria.

An autosomal genomic scan for loci linked to prediabetic phenotypes in Pima Indians.

Type 2 diabetes mellitus is a common chronic disease that is thought to have a substantial genetic basis. Identification of the genes responsible has been hampered by the complex nature of the syndrome. Abnormalities in insulin secretion and insulin action predict the development of type 2 diabetes and are, themselves, highly heritable traits. Since fewer genes may contribute to these precursors of type 2 diabetes than to the overall syndrome, such genes may be easier to identify. We, therefore, undertook an autosomal genomic scan to identify loci linked to prediabetic traits in Pima Indians, a population with a high prevalence of type 2 diabetes. 363 nondiabetic Pima Indians were genotyped at 516 polymorphic microsatellite markers on all 22 autosomes. Linkage analyses were performed using three methods (single-marker, nonparametric multipoint [MAPMAKER/SIBS], and variance components multipoint). These analyses provided evidence for linkage at several chromosomal regions, including 3q21-24 linked to fasting plasma insulin concentration and in vivo insulin action, 4p15-q12 linked to fasting plasma insulin concentration, 9q21 linked to 2-h insulin concentration during oral glucose tolerance testing, and 22q12-13 linked to fasting plasma glucose concentration. These results suggest loci that may harbor genes contributing to type 2 diabetes in Pima Indians. None of the linkages exceeded a LOD score of 3.6 (a 5% probability of occurring in a genome-wide scan). These findings must, therefore, be considered tentative until extended in this population or replicated in others.

The Population Reference Sample, POPRES: A Resource for Population, Disease, and Pharmacological Genetics Research

Technological and scientific advances, stemming in large part from the Human Genome and HapMap projects, have made large-scale, genome-wide investigations feasible and cost effective. These advances have the potential to dramatically impact drug discovery and development by identifying genetic factors that contribute to variation in disease risk as well as drug pharmacokinetics, treatment efficacy, and adverse drug reactions. In spite of the technological advancements, successful application in biomedical research would be limited without access to suitable sample collections. To facilitate exploratory genetics research, we have assembled a DNA resource from a large number of subjects participating in multiple studies throughout the world. This growing resource was initially genotyped with a commercially available genome-wide 500,000 single-nucleotide polymorphism panel. This project includes nearly 6,000 subjects of African-American, East Asian, South Asian, Mexican, and European origin. Seven informative axes of variation identified via principal-component analysis (PCA) of these data confirm the overall integrity of the data and highlight important features of the genetic structure of diverse populations. The potential value of such extensively genotyped collections is illustrated by selection of genetically matched population controls in a genome-wide analysis of abacavir-associated hypersensitivity reaction. We find that matching based on country of origin, identity-by-state distance, and multidimensional PCA do similarly well to control the type I error rate. The genotype and demographic data from this reference sample are freely available through the NCBI database of Genotypes and Phenotypes (dbGaP).

Genomewide search for type 2 diabetes susceptibility genes in four American populations.

Type 2 diabetes is a serious, genetically influenced disease for which no fully effective treatments are available. Identification of biochemical or regulatory pathways involved in the disease syndrome could lead to innovative therapeutic interventions. One way to identify such pathways is the genetic analysis of families with multiple affected members where disease predisposing genes are likely to be segregating. We undertook a genomewide screen (389-395 microsatellite markers) in samples of 835 white, 591 Mexican American, 229 black, and 128 Japanese American individuals collected as part of the American Diabetes Associations GENNID study. Multipoint nonparametric linkage analyses were performed with diabetes, and diabetes or impaired glucose homeostasis (IH). Linkage to diabetes or IH was detected near markers D5S1404 (map position 77 cM, LOD = 2.80), D12S853 (map position 82 cM, LOD = 2.81) and GATA172D05 (X-chromosome map position 130 cM, LOD = 2.99) in whites, near marker D3S2432 (map position 51 cM, LOD = 3.91) in Mexican Americans, and near marker D10S1412 (map position 14 cM, LOD = 2.39) in African Americans mainly collected in phase 1 of the study. Further analyses showed evidence for interactions between the chromosome 5 locus and region on chromosome 12 containing the MODY 3 gene (map position 132 cM) and between the X-chromosome locus and region near D12S853 (map position 82 cM) in whites. Although these results were not replicated in samples collected in phase 2 of the GENNID study, the region on chromosome 12 was replicated in samples from whites described by Bektas et al. (1999).

Autosomal genomic scan for loci linked to obesity and energy metabolism in Pima Indians

An autosomal genomic scan to search for linkage to obesity and energy metabolism was completed in Pima Indians, a population prone to obesity. Obesity was assessed by percent body fat (by hydrodensitometry) and fat distribution (the ratio of waist circumference to thigh circumference). Energy metabolism was measured in a respiratory chamber as 24-h metabolic rate, sleeping metabolic rate, and 24-h respiratory quotient (24RQ), an indicator of the ratio of carbohydrate oxidation to fat oxidation. Five hundred sixteen microsatellite markers with a median spacing of 6.4 cM were analyzed, in 362 siblings who had measurements of body composition and in 220 siblings who had measurements of energy metabolism. These comprised 451 sib pairs in 127 nuclear families, for linkage analysis to obesity, and 236 sib pairs in 82 nuclear families, for linkage analysis to energy metabolism. Pointwise and multipoint methods for regression of sib-pair differences in identity by descent, as well as a sibling-based variance-components method, were used to detect linkage. LOD scores >=2 were found at 11q21-q22, for percent body fat (LOD=2.1; P=.001), at 11q23-q24, for 24-h energy expenditure (LOD=2.0; P=.001), and at 1p31-p21 (LOD=2.0) and 20q11.2 (LOD=3.0; P=.0001), for 24RQ, by pointwise and multipoint analyses. With the variance-components method, the highest LOD score (LOD=2.3 P=.0006) was found at 18q21, for percent body fat, and at 1p31-p21 (LOD=2.8; P=.0003), for 24RQ. Possible candidate genes include LEPR (leptin receptor), at 1p31, and ASIP (agouti-signaling protein), at 20q11.2.

Rare Variants in APP, PSEN1 and PSEN2 Increase Risk for AD in Late-Onset Alzheimer's Disease Families

Pathogenic mutations in APP, PSEN1, PSEN2, MAPT and GRN have previously been linked to familial early onset forms of dementia. Mutation screening in these genes has been performed in either very small series or in single families with late onset AD (LOAD). Similarly, studies in single families have reported mutations in MAPT and GRN associated with clinical AD but no systematic screen of a large dataset has been performed to determine how frequently this occurs. We report sequence data for 439 probands from late-onset AD families with a history of four or more affected individuals. Sixty sequenced individuals (13.7%) carried a novel or pathogenic mutation. Eight pathogenic variants, (one each in APP and MAPT, two in PSEN1 and four in GRN) three of which are novel, were found in 14 samples. Thirteen additional variants, present in 23 families, did not segregate with disease, but the frequency of these variants is higher in AD cases than controls, indicating that these variants may also modify risk for disease. The frequency of rare variants in these genes in this series is significantly higher than in the 1,000 genome project (p = 5.09×10−5; OR = 2.21; 95%CI = 1.49–3.28) or an unselected population of 12,481 samples (p = 6.82×10−5; OR = 2.19; 95%CI = 1.347–3.26). Rare coding variants in APP, PSEN1 and PSEN2, increase risk for or cause late onset AD. The presence of variants in these genes in LOAD and early-onset AD demonstrates that factors other than the mutation can impact the age at onset and penetrance of at least some variants associated with AD. MAPT and GRN mutations can be found in clinical series of AD most likely due to misdiagnosis. This study clearly demonstrates that rare variants in these genes could explain an important proportion of genetic heritability of AD, which is not detected by GWAS.

HIBAG—HLA genotype imputation with attribute bagging

Genotyping of classical human leukocyte antigen (HLA) alleles is an essential tool in the analysis of diseases and adverse drug reactions with associations mapping to the major histocompatibility complex (MHC). However, deriving high-resolution HLA types subsequent to whole-genome single-nucleotide polymorphism (SNP) typing or sequencing is often cost prohibitive for large samples. An alternative approach takes advantage of the extended haplotype structure within the MHC to predict HLA alleles using dense SNP genotypes, such as those available from genome-wide SNP panels. Current methods for HLA imputation are difficult to apply or may require the user to have access to large training data sets with SNP and HLA types. We propose HIBAG, HLA Imputation using attribute BAGging, that makes predictions by averaging HLA-type posterior probabilities over an ensemble of classifiers built on bootstrap samples. We assess the performance of HIBAG using our study data (n=2668 subjects of European ancestry) as a training set and HLA data from the British 1958 birth cohort study (n≈1000 subjects) as independent validation samples. Prediction accuracies for HLA-A, B, C, DRB1 and DQB1 range from 92.2% to 98.1% using a set of SNP markers common to the Illumina 1M Duo, OmniQuad, OmniExpress, 660K and 550K platforms. HIBAG performed well compared with the other two leading methods, HLA*IMP and BEAGLE. This method is implemented in a freely available HIBAG R package that includes pre-fit classifiers for European, Asian, Hispanic and African ancestries, providing a readily available imputation approach without the need to have access to large training data sets.