Sequential haplotype scan methods for association analysis

Cold Conditioned: Discovery of Novel Alleles for Low-Temperature Tolerance in the Vavilov Barley Collection

Climate changes leading to higher summer temperatures can adversely affect cool season crops like spring barley. In the Upper Midwest region of the United States, one option for escaping this stress factor is to plant winter or facultative type cultivars in the autumn and then harvest in early summer before the onset of high-temperature stress. However, the major challenge in breeding such cultivars is incorporating sufficient winter hardiness to survive the extremely low temperatures that commonly occur in this production region. To broaden the genetic base for winter hardiness in the University of Minnesota breeding program, 2,214 accessions from the N. I. Vavilov Institute of Plant Industry (VIR) were evaluated for winter survival (WS) in St. Paul, Minnesota. From this field trial, 267 (>12%) accessions survived [designated as the VIR-low-temperature tolerant (LTT) panel] and were subsequently evaluated for WS across six northern and central Great Plains states. The VIR-LTT panel was genotyped with the Illumina 9K SNP chip, and then a genome-wide association study was performed on seven WS datasets. Twelve significant associations for WS were identified, including the previously reported frost resistance gene FR-H2 as well as several novel ones. Multi-allelic haplotype analysis revealed the most favorable alleles for WS in the VIR-LTT panel as well as another recently studied panel (CAP-LTT). Seventy-eight accessions from the VIR-LTT panel exhibited a high and consistent level of WS and select ones are being used in winter barley breeding programs in the United States and in a multiparent population.

Selecting Closely-Linked SNPs Based on Local Epistatic Effects for Haplotype Construction Improves Power of Association Mapping

Genome-wide association studies (GWAS) have gained central importance for the identification of candidate loci underlying complex traits. Single nucleotide polymorphism (SNP) markers are mostly used as genetic variants for the analysis of genotype-phenotype associations in populations, but closely linked SNPs that are grouped into haplotypes are also exploited. The benefit of haplotype-based GWAS approaches vs. SNP-based approaches is still under debate because SNPs in high linkage disequilibrium provide redundant information. To overcome some constraints of the commonly-used haplotype-based GWAS in which only consecutive SNPs are considered for haplotype construction, we propose a new method called functional haplotype-based GWAS (FH GWAS). FH GWAS is featured by combining SNPs into haplotypes based on the additive and epistatic effects among SNPs. Such haplotypes were termed functional haplotypes (FH). As shown by simulation studies, the FH GWAS approach clearly outperformed the SNP-based approach unless the minor allele frequency of the SNPs making up the haplotypes is low and the linkage disequilibrium between them is high. Applying FH GWAS for the trait flowering time in a large Arabidopsis thaliana population with whole-genome sequencing data revealed its potential empirically. FH GWAS identified all candidate regions which were detected in SNP-based and two other haplotype-based GWAS approaches. In addition, a novel region on chromosome 4 was solely detected by FH GWAS. Thus both the results of our simulation and empirical studies demonstrate that FH GWAS is a promising method and superior to the SNP-based approach even if almost complete genotype information is available.

Genetic endophenotypes for insomnia of major depressive disorder and treatment-induced insomnia

Major depressive disorder (MDD) is primarily hinged on the presence of either low mood and/or anhedonia to previously pleasurable events for a minimum of 2 weeks. Other clinical features that characterize MDD include disturbances in sleep, appetite, concentration and thoughts. The combination of any/both of the primary MDD symptoms as well as any four of the other clinical features has been referred to as MDD. The challenge for replicating gene association findings with phenotypes of MDD as well as its treatment outcome is putatively due to stratification of MDD patients. Likelihood for replication of gene association findings is hypothesized with specificity in symptoms profile (homogenous clusters of symptom/individual symptoms) evaluated. The current review elucidates the genetic factors that have been associated with insomnia symptom of MDD phenotype, insomnia symptom as a constellation of neuro-vegetative cluster of MDD symptom, insomnia symptom of MDD as an individual entity and insomnia feature of treatment outcome. Homozygous CC genotype of 3111T/C, GSK3B-AT/TT genotype of rs33458 and haplotype of TPH1 218A/C were associated with insomnia symptom of MDD. Insomnia symptom of MDD was not resolved in patients with the A/A genotype of HTR2A-rs6311 when treated with SSRI. Homozygous short (SS) genotype-HTTLPR, GG genotype of HTR2A-rs6311 and CC genotype of HTR2A-rs6313 were associated with AD treatment-induced insomnia, while val/met genotype of BDNF-rs6265 and the TT genotype of GSK-3beta-rs5443 reduced it. Dearth of association studies may remain the bane for the identification of robust genetic endophenotypes in line with findings for genotypes of HTR2A-rs6311.

Single Marker and Haplotype-Based Association Analysis of Semolina and Pasta Colour in Elite Durum Wheat Breeding Lines Using a High-Density Consensus Map

Association mapping is usually performed by testing the correlation between a single marker and phenotypes. However, because patterns of variation within genomes are inherited as blocks, clustering markers into haplotypes for genome-wide scans could be a worthwhile approach to improve statistical power to detect associations. The availability of high-density molecular data allows the possibility to assess the potential of both approaches to identify marker-trait associations in durum wheat. In the present study, we used single marker- and haplotype-based approaches to identify loci associated with semolina and pasta colour in durum wheat, the main objective being to evaluate the potential benefits of haplotype-based analysis for identifying quantitative trait loci. One hundred sixty-nine durum lines were genotyped using the Illumina 90K Infinium iSelect assay, and 12,234 polymorphic single nucleotide polymorphism (SNP) markers were generated and used to assess the population structure and the linkage disequilibrium (LD) patterns. A total of 8,581 SNPs previously localized to a high-density consensus map were clustered into 406 haplotype blocks based on the average LD distance of 5.3 cM. Combining multiple SNPs into haplotype blocks increased the average polymorphism information content (PIC) from 0.27 per SNP to 0.50 per haplotype. The haplotype-based analysis identified 12 loci associated with grain pigment colour traits, including the five loci identified by the single marker-based analysis. Furthermore, the haplotype-based analysis resulted in an increase of the phenotypic variance explained (50.4% on average) and the allelic effect (33.7% on average) when compared to single marker analysis. The presence of multiple allelic combinations within each haplotype locus offers potential for screening the most favorable haplotype series and may facilitate marker-assisted selection of grain pigment colour in durum wheat. These results suggest a benefit of haplotype-based analysis over single marker analysis to detect loci associated with colour traits in durum wheat.

Simultaneous estimation of the locations and effects of multiple disease loci in case-control studies

The genetic basis of complex diseases often involves multiple causative loci. Under such a disease etiology, assuming one disease locus in linkage disequilibrium mapping is likely to induce bias and lead to efficiency loss in disease locus estimation. An approach is needed for simultaneously localizing multiple functional loci within the same region. However, due to the increasing number of parameters accompanying disease loci, these estimates can be computationally infeasible. To circumvent this problem, we propose to estimate the main and two-adjacent-locus joint effects and a nuisance parameter at the disease loci separately through a linear approximation. Estimates of the genetic effects are entered into a generalized estimating equation to estimate disease loci, and the procedure is conducted iteratively until convergence. The proposed method provides estimates and confidence intervals (CIs) for the disease loci, the genetic main effects, and the joint effects of two adjacent disease loci, with the CIs for the disease loci providing useful regions for further fine-mapping. We apply the proposed approach to a data example of case-control studies. Results of the simulations and data example suggest that the developed method performs well in terms of bias, variance, and coverage probability under scenarios with up to three disease loci.

Powerful haplotype-based Hardy-Weinberg equilibrium tests for tightly linked loci

Recently, there have been many case-control studies proposed to test for association between haplotypes and disease, which require the Hardy-Weinberg equilibrium (HWE) assumption of haplotype frequencies. As such, haplotype inference of unphased genotypes and development of haplotype-based HWE tests are crucial prior to fine mapping. The goodness-of-fit test is a frequently-used method to test for HWE for multiple tightly-linked loci. However, its degrees of freedom dramatically increase with the increase of the number of loci, which may lack the test power. Therefore, in this paper, to improve the test power for haplotype-based HWE, we first write out two likelihood functions of the observed data based on the Niu's model (NM) and inbreeding model (IM), respectively, which can cause the departure from HWE. Then, we use two expectation-maximization algorithms and one expectation-conditional-maximization algorithm to estimate the model parameters under the HWE, IM and NM models, respectively. Finally, we propose the likelihood ratio tests LRT and LRT for haplotype-based HWE under the NM and IM models, respectively. We simulate the HWE, Niu's, inbreeding and population stratification models to assess the validity and compare the performance of these two LRT tests. The simulation results show that both of the tests control the type I error rates well in testing for haplotype-based HWE. If the NM model is true, then LRT is more powerful. While, if the true model is the IM model, then LRT has better performance in power. Under the population stratification model, LRT is still more powerful. To this end, LRT is generally recommended. Application of the proposed methods to a rheumatoid arthritis data set further illustrates their utility for real data analysis.

Genome-wide haplotype association study identifies the FRMD4A gene as a risk locus for Alzheimer's disease

Recently, several genome-wide association studies (GWASs) have led to the discovery of nine new loci of genetic susceptibility in Alzheimer's disease (AD). However, the landscape of the AD genetic susceptibility is far away to be complete and in addition to single-SNP (single-nucleotide polymorphism) analyses as performed in conventional GWAS, complementary strategies need to be applied to overcome limitations inherent to this type of approaches. We performed a genome-wide haplotype association (GWHA) study in the EADI1 study (n=2025 AD cases and 5328 controls) by applying a sliding-windows approach. After exclusion of loci already known to be involved in AD (APOE, BIN1 and CR1), 91 regions with suggestive haplotype effects were identified. In a second step, we attempted to replicate the best suggestive haplotype associations in the GERAD1 consortium (2820 AD cases and 6356 controls) and observed that 9 of them showed nominal association. In a third step, we tested relevant haplotype associations in a combined analysis of five additional case-control studies (5093 AD cases and 4061 controls). We consistently replicated the association of a haplotype within FRMD4A on Chr.10p13 in all the data set analyzed (OR: 1.68; 95% CI: (1.43-1.96); P=1.1 × 10(-10)). We finally searched for association between SNPs within the FRMD4A locus and Aβ plasma concentrations in three independent non-demented populations (n=2579). We reported that polymorphisms were associated with plasma Aβ42/Aβ40 ratio (best signal, P=5.4 × 10(-7)). In conclusion, combining both GWHA study and a conservative three-stage replication approach, we characterised FRMD4A as a new genetic risk factor of AD.

Contrasting linkage disequilibrium as a multilocus family-based association test

Linkage disequilibrium (LD) of genetic loci is routinely estimated and graphically illustrated in genetic association studies. It has been suggested that the information in LD is also useful for association mapping and genetic association can be detected by comparing LD patterns between cases and controls. Here, we extend this idea to analyze case-parents data by comparing LD patterns between transmitted and nontransmitted genotypes. We provide the condition when contrasting LD is valid for testing gene-gene interactions. A permutation procedure is given to assess statistical significance. One advantage of our proposed methods is that haplotype information is not required. Thus, the implementation of our methods is straightforward and the resulted tests are free from potential bias caused by assumptions made to estimate haplotypes in silico. Since our test statistics use pairwise LD measurements, they are less affected by missing data than many other multilocus methods. With simulated data, we demonstrate that examining LD patterns of case-parents data is a useful multilocus association mapping strategy and it complements existing association mapping methods. The application of our methods to a Crohn's disease data set shows that our methods can detect multilocus association that might be missed by other association methods. Our permutation procedure can also be modified to allow multiple offspring from a family to be analyzed.

Genetic variants in the folate pathway and risk of childhood acute lymphoblastic leukemia

OBJECTIVE

Folate is involved in the one-carbon metabolism that plays an essential role in the synthesis, repair, and methylation of DNA. We examined whether child's germline genetic variation in the folate pathway is associated with childhood acute lymphoblastic leukemia (ALL), and whether periconception maternal folate and alcohol intake modify the risk.

METHODS

Seventy-six single nucleotide polymorphisms (SNPs), including 66 haplotype-tagging SNPs in 10 genes (CBS, DHFR, FOLH1, MTHFD1, MTHFR, MTR, MTRR, SHMT1, SLC19A1, and TYMS), were genotyped in 377 ALL cases and 448 controls. Log-additive associations between genotypes and ALL risk were adjusted for age, sex, Hispanic ethnicity (when appropriate), and maternal race.

RESULTS

Single and haplotype SNPs analyses showed statistically significant associations between SNPs located in (or adjacent to) CBS, MTRR, TYMS/ENOFS, and childhood ALL. Many regions of CBS were associated with childhood ALL in Hispanics and non-Hispanics (p < 0.01). Levels of maternal folate intake modified associations with SNPs in CBS, MTRR, and TYMS.

CONCLUSION

Our data suggest the importance of genetic variability in the folate pathway and childhood ALL risk.

Endometrial cancer and genetic variation in PTEN, PIK3CA, AKT1, MLH1, and MSH2 within a population-based case-control study

OBJECTIVE

We assessed whether common genetic variation in PTEN, PIK3CA, AKT1, MLH1, and MSH2-genes that reportedly are frequently altered in endometrial cancer-was associated with risk of endometrial cancer.

METHODS

Using data from a population-based case-control study in Poland (PECS) of 417 cases and 407 matched controls, we genotyped 76 tagging single nucleotide polymorphisms (tagSNPs; located in or within 10 kb upstream or 5 kb downstream of the gene of interest, minor allele frequency >=5% among various ethnic groups, and not already represented by another tagSNP at a LD of r(2) >=0.80) on an Illumina Custom Infinium iSelect assay that included over 29,000 SNPs in 1316 genes. For individual SNPs, we used unconditional logistic regression models, adjusted for age and site, to generate odds ratios (ORs) and 95% confidence intervals (CIs). To replicate the one statistically significant association in PECS, we independently genotyped that tagSNP among 1141 endometrial cancer cases and 2275 controls from the SEARCH study in the UK. We assessed haplotypes via extended haplotype blocks and the sequential haplotype scan method.

RESULTS

The rs2677764 tagSNP in PIK3CA was statistically significantly associated with endometrial cancer in PECS (OR=1.42, 95% CI, 1.03-1.95; P=0.03) but not SEARCH (OR=0.98, 95% CI=0.82-1.17). Of the 25 haplotypes observed in at least 5% of cases and controls in PECS, only 1, in PIK3CA, was statistically significantly associated with endometrial cancer (OR=1.39, 95% CI, 1.00-1.93). All haplotype global p-values were null.

CONCLUSION

Common genetic variation in PTEN, PIK3CA, AKT1, MLH1, or MSH2 was not statistically significantly associated with endometrial cancer.

Conditional meta-analysis stratifying on detailed HLA genotypes identifies a novel type 1 diabetes locus around TCF19 in the MHC

The human leukocyte antigen (HLA) class II genes HLA-DRB1, -DQA1 and -DQB1 are the strongest genetic factors for type 1 diabetes (T1D). Additional loci in the major histocompatibility complex (MHC) are difficult to identify due to the region's high gene density and complex linkage disequilibrium (LD). To facilitate the association analysis, two novel algorithms were implemented in this study: one for phasing the multi-allelic HLA genotypes in trio families, and one for partitioning the HLA strata in conditional testing. Screening and replication were performed on two large and independent datasets: the Wellcome Trust Case-Control Consortium (WTCCC) dataset of 2,000 cases and 1,504 controls, and the T1D Genetics Consortium (T1DGC) dataset of 2,300 nuclear families. After imputation, the two datasets have 1,941 common SNPs in the MHC, of which 22 were successfully tested and replicated based on the statistical testing stratifying on the detailed DRB1 and DQB1 genotypes. Further conditional tests using the combined dataset confirmed eight novel SNP associations around 31.3 Mb on chromosome 6 (rs3094663, p = 1.66 × 10(-11) and rs2523619, p = 2.77 × 10(-10) conditional on the DR/DQ genotypes). A subsequent LD analysis established TCF19, POU5F1, CCHCR1 and PSORS1C1 as potential causal genes for the observed association.

Identifying disease polymorphisms from case-control genetic association data

In case-control association studies, it is typical to observe several associated polymorphisms in a gene region. Often the most significantly associated polymorphism is considered to be the disease polymorphism; however, it is not clear whether it is the disease polymorphism or there is more than one disease polymorphism in the gene region. Currently, there is no method that can handle these problems based on the linkage disequilibrium (LD) relationship between polymorphisms. To distinguish real disease polymorphisms from markers in LD, a method that can detect disease polymorphisms in a gene region has been developed. Relying on the LD between polymorphisms in controls, the proposed method utilizes model-based likelihood ratio tests to find disease polymorphisms. This method shows reliable Type I and Type II error rates when sample sizes are large enough, and works better with re-sequenced data. Applying this method to fine mapping using re-sequencing or dense genotyping data would provide important information regarding the genetic architecture of complex traits.

No association between FTO or HHEX and endometrial cancer risk

INTRODUCTION

Obesity and diabetes are known risk factors for endometrial cancer; thus, the genetic risk factors of these phenotypes might also be associated with endometrial cancer risk. To evaluate this hypothesis, we genotyped tag-single nucleotide polymorphisms (SNP) and candidate SNPs in FTO and HHEX in a primary set of 417 endometrial cancer cases and 406 population-based controls, and validated significant findings in a replication set of approximately 2,347 cases and 3,140 controls from three additional studies.

METHODS

We genotyped 189 tagSNPs in FTO (including rs8050136) and five tagSNPs in HHEX (including rs1111875) in the primary set and one SNP each in FTO (rs12927155) and HHEX (rs1111875) in the validation set. Per allele odds ratios (OR) and 95% confidence intervals (CI) were calculated to estimate the association between the genotypes of each SNPs (as an ordinal variable) and endometrial cancer risk using unconditional logistic regression models, controlling for age and site.

RESULTS

In the primary study, the most significant finding in FTO was rs12927155 (OR, 1.56; 95% CI, 1.21-2.01; P = 5.8 x 10(-4)), and in HHEX, it was rs1111875 (OR, 0.80; 95% CI, 0.66-0.97; P = 0.026). In the validation studies, the pooled per allele OR, adjusted for age and study for FTO, was rs12927155 (OR, 0.94; 95% CI, 0.83-1.06; P = 0.29), whereas for HHEX, it was rs1111875 (OR, 1.00; 95% CI, 0.92-1.10; P = 0.96).

CONCLUSION

Our data indicate that common genetic variants in two genes previously related to obesity (FTO) and diabetes (HHEX) by genome-wide association scans were not associated with endometrial cancer risk.

IMPACT

Polymorphisms in FTO and HHEX are unlikely to have large effects on endometrial cancer risk but may have weaker effects.

Cystic fibrosis transmembrane conductance regulator gene mutation and lung cancer risk

The cystic fibrosis transmembrane conductance regulator (CFTR) holds an important role in retaining lung function, but its association with lung cancer is unclear. A case-control study was conducted to determine the possible associations of the genetic variants in the CFTR gene with lung cancer risk. Genotypes of the most common deletion DeltaF508, one functional SNP, and eight tag SNPs in the CFTR gene were determined in 574 lung cancer patients and 679 controls. A logistic regression model, adjusting for known risk factors, was used to evaluate the association of each variant with lung cancer risk, as confirmation haplotype and sub-haplotype analyses were performed. DeltaF508 deletion and genotypes with minor alleles in one tag SNP, rs10487372, and one functional SNP, rs213950, were inversely associated with lung cancer risk. The results of haplotype and sub-haplotype analyses were consistent with single variant analysis, all pointing to deletion DeltaF508 being the key variant for significant haplotypes and sub-haplotypes. Individuals with 'deletion-T' (DeltaF508/rs10487372) haplotype had a 68% reduced risk for lung cancer compared to common haplotype 'no-deletion-C' (OR=0.32; 95% CI=0.15-0.68; p=0.01). Genetic variations in the CFTR gene might modulate the risk of lung cancer. This study, for the first time, provides evidence of a protective role of the CFTR deletion carrier in the etiology of lung cancer.

A genome-wide association study identifies pancreatic cancer susceptibility loci on chromosomes 13q22.1, 1q32.1 and 5p15.33

We conducted a genome-wide association study of pancreatic cancer in 3,851 affected individuals (cases) and 3,934 unaffected controls drawn from 12 prospective cohort studies and 8 case-control studies. Based on a logistic regression model for genotype trend effect that was adjusted for study, age, sex, self-described ancestry and five principal components, we identified eight SNPs that map to three loci on chromosomes 13q22.1, 1q32.1 and 5p15.33. Two correlated SNPs, rs9543325 (P = 3.27 x 10(-11), per-allele odds ratio (OR) 1.26, 95% CI 1.18-1.35) and rs9564966 (P = 5.86 x 10(-8), per-allele OR 1.21, 95% CI 1.13-1.30), map to a nongenic region on chromosome 13q22.1. Five SNPs on 1q32.1 map to NR5A2, and the strongest signal was at rs3790844 (P = 2.45 x 10(-10), per-allele OR 0.77, 95% CI 0.71-0.84). A single SNP, rs401681 (P = 3.66 x 10(-7), per-allele OR 1.19, 95% CI 1.11-1.27), maps to the CLPTM1L-TERT locus on 5p15.33, which is associated with multiple cancers. Our study has identified common susceptibility loci for pancreatic cancer that warrant follow-up studies.

Association of markers in the 3' region of the GluR5 kainate receptor subunit gene to alcohol dependence

BACKGROUND

Glutamate neurotransmission plays an important role in a variety of alcohol-related phenomena, including alcohol self-administration by both animals and humans. Because the risk for alcohol dependence (AD) is genetically influenced, genes encoding glutamate receptors are candidates to contribute to the risk for AD. We examined the role of variation in the 3' region of GRIK1, the gene that encodes the GluR5 receptor subunit of the kainic acid glutamate receptor, on risk for AD. We focused specifically on this gene because topiramate, a glutamate modulator that binds to the GluR5 subunit, has shown robust efficacy in the treatment of AD.

METHODS

We genotyped 7 single nucleotide polymorphisms (SNPs) in the 3'-half of GRIK1, which includes 3 differentially spliced exons, in a sample of EA control subjects (n = 507) and subjects with AD (n = 1,057).

RESULTS

We found nominally significant evidence of association to AD for 3 SNPs (rs2832407 in intron 9, rs2186305 in intron 17, and rs2832387 in the 3'UTR). Empirical p-value estimation revealed that only rs2832407 was significantly associated to phenotype (p = 0.043).

DISCUSSION

These findings provide support for the hypothesis that variation in the 3' portion of the gene encoding the GluR5 kainate receptor subunit contributes to the risk for AD. Further research is needed to ascertain whether this SNP is itself functional or whether the association reflects linkage disequilibrium with functional variation elsewhere in the gene and whether this SNP moderates topiramate's effects in the treatment of AD.

Common genetic variation in the sex hormone metabolic pathway and endometrial cancer risk: pathway-based evaluation of candidate genes

BACKGROUND

Estrogen plays a major role in endometrial carcinogenesis, suggesting that common variants of genes in the sex hormone metabolic pathway may be related to endometrial cancer risk. In support of this view, variants in CYP19A1 [cytochrome P450 (CYP), family 19, subfamily A, polypeptide 1] have been associated with both circulating estrogen levels and endometrial cancer risk. Associations with variants in other genes have been suggested, but findings have been inconsistent.

METHODS

We examined 36 sex hormone-related genes using a tagging approach in a population-based case-control study of 417 endometrial cancer cases and 407 controls conducted in Poland. We evaluated common variation in these genes in relation to endometrial cancer risk using sequential haplotype scan, variable-sized sliding window and adaptive rank-truncated product (ARTP) methods.

RESULTS

In our case-control study, the strongest association with endometrial cancer risk was for AR (androgen receptor; ARTP P = 0.006). Multilocus analyses also identified boundaries for a region of interest in AR and in CYP19A1 around a previously identified susceptibility loci. We did not find evidence for consistent associations between previously reported candidate single-nucleotide polymorphisms in this pathway and endometrial cancer risk.

DISCUSSION

In summary, we identified regions in AR and CYP19A1 that are of interest for further evaluation in relation to endometrial cancer risk in future haplotype and subsequent fine mapping studies in larger study populations.

Modeling Informatively Missing Genotypes in Haplotype Analysis

It is common to have missing genotypes in practical genetic studies. The majority of the existing statistical methods, including those on haplotype analysis, assume that genotypes are missing at random-that is, at a given marker, different genotypes and different alleles are missing with the same probability. In our previous work, we have demonstrated that the violation of this assumption may lead to serious bias in haplotype frequency estimates and haplotype association analysis. We have proposed a general missing data model to simultaneously characterize missing data patterns across a set of two or more biallelic markers. We have proved that haplotype frequencies and missing data probabilities are identifiable if and only if there is linkage disequilibrium between these markers under the general missing data model. In this study, we extend our work to multi-allelic markers and observe a similar finding. Simulation studies on the analysis of haplotypes consisting of two markers illustrate that our proposed model can reduce the bias for haplotype frequency estimates due to incorrect assumptions on the missing data mechanism. Finally, we illustrate the utilities of our method through its application to a real data set from a study of scleroderma.

SHARE: an adaptive algorithm to select the most informative set of SNPs for candidate genetic association

Association studies have been widely used to identify genetic liability variants for complex diseases. While scanning the chromosomal region 1 single nucleotide polymorphism (SNP) at a time may not fully explore linkage disequilibrium, haplotype analyses tend to require a fairly large number of parameters, thus potentially losing power. Clustering algorithms, such as the cladistic approach, have been proposed to reduce the dimensionality, yet they have important limitations. We propose a SNP-Haplotype Adaptive REgression (SHARE) algorithm that seeks the most informative set of SNPs for genetic association in a targeted candidate region by growing and shrinking haplotypes with 1 more or less SNP in a stepwise fashion, and comparing prediction errors of different models via cross-validation. Depending on the evolutionary history of the disease mutations and the markers, this set may contain a single SNP or several SNPs that lay a foundation for haplotype analyses. Haplotype phase ambiguity is effectively accounted for by treating haplotype reconstruction as a part of the learning procedure. Simulations and a data application show that our method has improved power over existing methodologies and that the results are informative in the search for disease-causal loci.

Detection of parent-of-origin effects in complete and incomplete nuclear families with multiple affected children using multiple tightly linked markers

For a diallelic marker locus, the parental-asymmetry test (PAT) based on case-parents trios and its extensions to accommodate incomplete unclear families (1-PAT and C-PAT) are simple and powerful approaches to test for parent-of-origin effects. However, haplotype analysis is generally regarded as advantageous over single-marker analysis in genetic study of common complex diseases. This is mainly due to the fact that complex diseases are often associated with multiple markers. As such, HAP-PAT was constructed to test for parent-of-origin effects in the framework of haplotype analysis. However, its applicability is limited due to the need for complete parental information. In this paper, for nuclear families with only one parent and multiple affected children, we develop HAP-1-PAT to test for parent-of-origin effects using multiple tightly linked markers. We further propose HAP-C-PAT to combine data from families with both parents and those with only one parent. We carry out a simulation study to evaluate the validity and power of the test statistics in various settings, including incomplete family rates, marker/disease-locus linkage disequilibrium patterns, and population models. We perform analysis for all possible combinations of the markers being considered. A permutation-based Monte Carlo procedure is devised to determine the significance of the tests; the corrected global p values taking into account of multiple testing are used for inferences. The results show that HAP-1-PAT and HAP-C-PAT would work well even under the population stratification demographic model and assortative mating demographic model. Furthermore, for the disease models considered, there are significant gains in power from haplotype analysis compared to single-marker analysis, and from combined analysis using HAP-C-PAT compared to analysis using HAP-PAT for the complete family data only.

Large-scale evaluation of candidate genes identifies associations between DNA repair and genomic maintenance and development of benzene hematotoxicity

Benzene is an established human hematotoxicant and leukemogen but its mechanism of action is unclear. To investigate the role of single-nucleotide polymorphisms (SNPs) on benzene-induced hematotoxicity, we analyzed 1395 SNPs in 411 genes using an Illumina GoldenGate assay in 250 benzene-exposed workers and 140 unexposed controls. Highly significant findings clustered in five genes (BLM, TP53, RAD51, WDR79 and WRN) that play a critical role in DNA repair and genomic maintenance, and these regions were then further investigated with tagSNPs. One or more SNPs in each gene were associated with highly significant 10-20% reductions (P values ranged from 0.0011 to 0.0002) in the white blood cell (WBC) count among benzene-exposed workers but not controls, with evidence for gene-environment interactions for SNPs in BLM, WRN and RAD51. Further, among workers exposed to benzene, the genotype-associated risk of having a WBC count <4000 cells/microl increased when using individuals with progressively higher WBC counts as the comparison group, with some odds ratios >8-fold. In vitro functional studies revealed that deletion of SGS1 in yeast, equivalent to lacking BLM and WRN function in humans, caused reduced cellular growth in the presence of the toxic benzene metabolite hydroquinone, and knockdown of WRN using specific short hairpin RNA increased susceptibility of human TK6 cells to hydroquinone toxicity. Our findings suggest that SNPs involved in DNA repair and genomic maintenance, with particular clustering in the homologous DNA recombination pathway, play an important role in benzene-induced hematotoxicity.

PPARGC1A variation associated with DNA damage, diabetes, and cardiovascular diseases: the Boston Puerto Rican Health Study

OBJECTIVE

Individuals with type 2 diabetes exhibit higher DNA damage and increased risk of cardiovascular disease (CVD). However, mechanisms underlying the association between DNA damage and development of type 2 diabetes and CVD are not understood. We sought to link peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PPARGC1A), a master transcriptional regulator of mitochondrial oxidative phosphorylation and cellular energy metabolism, with DNA damage, type 2 diabetes, and CVD.

RESEARCH DESIGN AND METHODS

We measured DNA damage as urinary 8-hydroxydeoxyguanosine (8-OHdG) concentration and examined the relationship between nine PPARGC1A genetic variants, DNA damage, type 2 diabetes, and self-reported CVD in 959 participants of the Boston Puerto Rican Health Study.

RESULTS

With respect to urinary 8-OHdG, PPARGC1A variants showed significant association, and PPARGC1A haplotypes exhibited significant association after correction for multiple testing. Two independent PPARGC1A variants associated significantly with type 2 diabetes (odds ratios [ORs] 1.35 and 2.46; P = 0.045 and <0.001). Carriers of minor alleles of two other PPARGC1A variants, both in strong linkage disequilibrium and associated with lower DNA damage, showed lower prevalence of CVD (ORs 0.53 and 0.65; P = 0.030 and 0.175). Moreover, we found that physical activity correlated negatively with DNA damage.

CONCLUSIONS

It is plausible that low physical activity combined with risk haplotyes contribute to the high prevalence of type 2 diabetes in this population. We propose that PPARGC1A influences development of type 2 diabetes and CVD via DNA damage. Increasing physical activity, which induces PPARGC1A expression, is a potential strategy to slow DNA damage, thereby decreasing the risk of CVD for individuals with type 2 diabetes.

Application of sequential haplotype scan methods to case-control data

Haplotype association analysis based on arbitrarily chosen markers might lower statistical power because of the larger number of degrees of freedom caused by irrelevant makers.On the other hand, an exhaustive search for all possible combinations of markers for haplotype analysis is computationally expensive for genome-wide association analysis.To improve power, we applied our recently developed sequential haplotype scan method to case-control data for rheumatoid arthritis, including the PTPN22 candidate gene on chromosome 1p and the association mapping data on chromosome 18q, from the Genetic Analysis Workshop 15. The results showed that our new approach is at least as powerful as the traditional single-locus analysis and sometimes can be more powerful.