Fine mapping functional sites or regions from case-control data using haplotypes of multiple linked SNPs

Sister haplotypes and recombination disequilibrium: a new approach to identify associations of haplotypes with complex diseases

Haplotype-based association analysis has several advantages over single-SNP association analysis. However, to date all haplotype-disease associations have not excluded recombination interference among multiple loci and hence some results might be confounded by recombination interference. Association of sister haplotypes with a complex disease, based on recombination disequilibrium (RD) was presented. Sister haplotypes can be determined by translating notation of DNA base haplotypes to notation of genetic genotypes. Sister haplotypes provide haplotype pairs available for haplotype-disease association analysis. After performing RD tests in control and case cohorts, a two-by-two contingency table can be constructed using sister haplotype pair and case-control pair. With this standard two-by-two table, one can perform classical Chi-square test to find statistical haplotype-disease association. Applying this method to a haplotype dataset of Alzheimer disease (AD), association of sister haplotypes containing ApoE3/4 with risk for AD was identified under no RD. Haplotypes within gene IL-13 were not associated with risk for breast cancer in the case of no RD and no association of haplotypes in gene IL-17A with risk for coronary artery disease were detected without RD. The previously reported associations of haplotypes within these genes with risk for these diseases might be due to strong RD and/or inappropriate haplotype pairs.

Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle

Background

Traditional single nucleotide polymorphism (SNP) genome-wide association analysis (GWAA) can be inefficient because single SNPs provide limited genetic information about genomic regions. On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium (LD) between haplotypes and causal variants and may also potentially capture epistastic interactions between variants within a haplotyped locus, providing an increase in the power and robustness of the association studies. We performed GWAA (413,355 SNP markers) using haplotypes based on variable-sized sliding windows and compared the results to a single-SNP GWAA using Warner-Bratzler shear force measured in the longissimus thorasis muscle of 3161 Nelore bulls to ascertain the optimal window size for identifying the genomic regions that influence meat tenderness.

Results

The GWAA using single SNPs identified eight variants influencing meat tenderness on BTA 3, 4, 9, 10 and 11. However, thirty-three putative meat tenderness QTL were detected on BTA 1, 3, 4, 5, 8, 9, 10, 11, 15, 17, 18, 24, 25, 26 and 29 using variable-sized sliding haplotype windows. Analyses using sliding window haplotypes of 3, 5, 7, 9 and 11 SNPs identified 57, 61, 42, 39, and 21% of all thirty-three putative QTL regions, respectively; however, the analyses using the 3 and 5 SNP haplotypes, cumulatively detected 88% of the putative QTL. The genes associated with variation in meat tenderness participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes.

Conclusions

GWAA using haplotypes based on variable-sized sliding windows allowed the detection of more QTL than traditional single-SNP GWAA. Analyses using smaller haplotypes (3 and 5 SNPs) detected a higher proportion of the putative QTL.

Electronic supplementary material

The online version of this article (10.1186/s12863-019-0713-4) contains supplementary material, which is available to authorized users.

Low-Frequency IL23R Coding Variant Associated with Crohn's Disease Susceptibility in Japanese Subjects Identified by Personal Genomics Analysis

BACKGROUND

The common disease-common variant hypothesis is insufficient to explain the complexities of Crohn's disease (CD) genetics; therefore, rare variants are expected to be important in the disease. We explored rare variants associated with susceptibility to CD in Japanese individuals by personal genomic analysis.

METHODS

Two-step analyses were performed. The first step was a trio analysis with whole-exome sequence (WES) analysis and the second was a follow-up case-control association study. The WES analysis pipeline comprised Burrows-Wheeler Aligner, Picard, Genome Analysis Toolkit, and SAMTOOLS. Single nucleotide variants (SNVs)/indels were annotated and filtered by using programs implemented in ANNOVAR in combination with identity-by-descent (IBD), subsequently were subjected to the linkage based, and de novo based strategies. Finally, we conducted an association study that included 176 unrelated subjects with CD and 358 healthy control subjects.

RESULTS

In family members, 234,067-297,523 SNVs/indels were detected and they were educed to 106-146 by annotation based filtering. Fifty-four CD variants common to both individuals of the affected sib pair were identified. The linkage based strategy detected five candidate variants whereas the de novo based strategy identified no variants. Consequently, five candidates were analyzed in the case-control association study. CD showed a significant association with one variant in exon 4 of IL23R, G149R [rs76418789, P = 3.9E-5, odds ratio (OR) 0.21, 95% confidence interval (CI) 0.09-0.47 for the dominant model (AA + AG versus GG), and P = 7.3E-5, OR 0.21, 95% CI 0.10-0.48 for AG versus GG, and P = 7.2E-5, OR 0.23, 95% CI 0.10-0.50 for the allele model].

CONCLUSIONS

The present study, using personal genomics analysis of a small CD pedigree, is the first to show that the low-frequency non-synonymous variant of IL23R, rs76418789, protects against CD development in Japanese subjects.

Genetic variation in a4GnT in relation to Helicobacter pylori serology and gastric cancer risk

BACKGROUND

Helicobacter pylori, a known risk factor of gastric cancer, rarely colonize the deeper portion of normal gastric glands, where the mucus is rich in alpha-1,4-linked N-acetylglucosamine capped O-glycans, that strongly inhibit H. pylori growth in vitro.

MATERIALS AND METHODS

We investigated the association between genetic variation in the O-glycan transferase encoding gene (a4GnT) and H. pylori infection and gastric cancer risk using a Polish population-based case-control study (273 gastric cancer patients and 377 controls).

RESULTS

A haplotype at the rs2622694-rs397266 locus was associated with H. pylori infection, with the A-A haplotype associated with a higher risk compared with the most frequent G-G haplotype (odds ratio 2.30; 95% confidence interval 1.35-3.92). The association remained significant after correction for multiple tests (global p value: nominal 0.002, empirical 0.045). Neither this haplotype nor the tagSNPs were associated with overall gastric cancer risk.

CONCLUSION

a4GnT genetic variation may be relevant to H. pylori infection, but not to gastric cancer risk.

Genetic variants in surfactant, pulmonary-associated protein D (SFTPD) and Japanese susceptibility to ulcerative colitis

BACKGROUND

Identifying culprit genes for a complex trait in a homogeneous population such as the Japanese remains challenging. We aimed to use previous achievements of genome-wide association studies to identify precise susceptibility loci for inflammatory bowel disease in Japanese people and to simultaneously investigate the replication of recently identified susceptibility loci.

METHODS

An unrelated Japanese population of 174 Crohn's disease patients, 296 ulcerative colitis (UC) patients, and 394 healthy controls was consecutively enrolled in this study. Genotype and haplotype analyses focusing on susceptibility to inflammatory bowel disease were performed using 7 single nucleotide polymorphisms (SNPs) including 5 HapMap tag SNPs within surfactant, pulmonary-associated protein D (SFTPD) along with the 2 Caucasian susceptibility loci. We performed fine-scale mapping of trait-associated loci with the extension of a shattered coalescent process in a Bayesian framework. Epistasis on disease phenotypes was statistically explored with the interaction dendrogram.

RESULTS

A minor allele G of rs911887 reached statistical significance for susceptibility to UC. The 2-allele haplotype GG comprising rs911887 and rs2243639 (Ala160Thr) within SFTPD was significantly associated with susceptibility to UC. A posterior density plot shows that trait-associated variants in the vicinity of rs911887 are likely to exist. An association between NKX2-3 and UC susceptibility was replicated and diverse evidence of epistasis in Japan was suggested.

CONCLUSIONS

This preliminary study suggests that SFTPD is both a susceptibility gene and a disease-modifying gene for UC in Japanese. Replication of the causality and functional analyses of SFTPD is urgently warranted.

A systematic search for SNPs/haplotypes associated with disease phenotypes using a haplotype-based stepwise procedure

Background

Genotyping technologies enable us to genotype multiple Single Nucleotide Polymorphisms (SNPs) within selected genes/regions, providing data for haplotype association analysis. While haplotype-based association analysis is powerful for detecting untyped causal alleles in linkage-disequilibrium (LD) with neighboring SNPs/haplotypes, the inclusion of extraneous SNPs could reduce its power by increasing the number of haplotypes with each additional SNP.

Methods

Here, we propose a haplotype-based stepwise procedure (HBSP) to eliminate extraneous SNPs. To evaluate its properties, we applied HBSP to both simulated and real data, generated from a study of genetic associations of the bactericidal/permeability-increasing (BPI) gene with pulmonary function in a cohort of patients following bone marrow transplantation.

Results

Under the null hypothesis, use of the HBSP gave results that retained the desired false positive error rates when multiple comparisons were considered. Under various alternative hypotheses, HBSP had adequate power to detect modest genetic associations in case-control studies with 500, 1,000 or 2,000 subjects. In the current application, HBSP led to the identification of two specific SNPs with a positive validation.

Conclusion

These results demonstrate that HBSP retains the essence of haplotype-based association analysis while improving analytic power by excluding extraneous SNPs. Minimizing the number of SNPs also enables simpler interpretation and more cost-effective applications.

Gains in power for exhaustive analyses of haplotypes using variable-sized sliding window strategy: a comparison of association-mapping strategies

Linkage disequilibrium (LD)-based association mapping is often performed by analyzing either individual SNPs or block-based multi-SNP haplotypes. Sliding windows of several fixed sizes (in terms of SNP numbers) were also applied to a few simulated or real data sets. In comparison, exhaustively testing based on variable-sized sliding windows (VSW) of all possible sizes of SNPs over a genomic region has the best chance to capture the optimum markers (single SNPs or haplotypes) that are most significantly associated with the traits under study. However, the cost is the increased number of multiple tests and computation. Here, a strategy of VSW of all possible sizes is proposed and its power is examined, in comparison with those using only haplotype blocks (BLK) or single SNP loci (SGL) tests. Critical values for statistical significance testing that account for multiple testing are simulated. We demonstrated that, over a wide range of parameters simulated, VSW increased power for the detection of disease variants by approximately 1-15% over the BLK and SGL approaches. The improved performance was more significant in regions with high recombination rates. In an empirical data set, VSW obtained the most significant signal and identified the LRP5 gene as strongly associated with osteoporosis. With the use of computational techniques such as parallel algorithms and clustering computing, it is feasible to apply VSW to large genomic regions or those regions preliminarily identified by traditional SGL/BLK methods.

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.

Xeroderma pigmentosum complementation group C genotypes/diplotypes play no independent or interaction role with polycyclic aromatic hydrocarbons-DNA adducts for breast cancer risk

Xeroderma pigmentosum complementation group C (XPC) is an important DNA nuclear excision repair (NER) gene that recognises the damage caused by a variety of bulky DNA adducts. We evaluated the association of two common non-synonymous polymorphisms in XPC (Ala499Val and Lys939Gln) with breast cancer risk in the Long Island Breast Cancer Study Project (LIBCSP), a population-based case-control study. Genotyping of 1067 cases and 1110 controls was performed by a high throughput assay with fluorescence polarisation. There were no overall associations between XPC polymorphisms and breast cancer risk. A diplotype CC-CC was significantly associated with increased breast cancer risk compared with diplotype CA-CA (OR=1.4, 95%CI: 1.0-1.9), but was not significant when compared with all other diplotypes combined (OR=1.22, 95%CI: 0.97-1.53). No modification effects were observed for XPC genotypes by cigarette smoking status, smoking pack-years or polycyclic aromatic hydrocarbons (PAH)-DNA adducts. The increase in breast cancer risk was slightly more pronounced among women with detectable PAH-DNA adducts and carrying the diplotype CC-CC (OR=1.6, 95%CI: 1.1-2.2) compared to women with non-detectable PAH-DNA adducts carrying other diplotypes combined, but no statistically significant interaction was observed (P(interaction)=0.69). These data suggest that XPCs have neither independent effects nor interactions with cigarette smoking and PAH-DNA adducts for breast cancer risk. Further studies with multiple genetic polymorphisms in NER pathway are warranted.

Sequential haplotype scan methods for association analysis

Multi-locus association analyses, including haplotype-based analyses, can sometimes provide greater power than single-locus analyses for detecting disease susceptibility loci. This potential gain, however, can be compromised by the large number of degrees of freedom caused by irrelevant markers. Exhaustive search for the optimal set of markers might be possible for a small number of markers, yet it is computationally inefficient. In this paper, we present a sequential haplotype scan method to search for combinations of adjacent markers that are jointly associated with disease status. When evaluating each marker, we add markers close to it in a sequential manner: a marker is added if its contribution to the haplotype association with disease is warranted, conditional on current haplotypes. This conditional evaluation is based on the well-known Mantel-Haenszel statistic. We propose two permutation based methods to evaluate the growing haplotypes: a haplotype method for the combined markers, and a summary method that sums conditional statistics. We compared our proposed methods, the single-locus method, and a sliding window method using simulated data. We also applied our sequential haplotype scan algorithm to experimental data for CYP2D6. The results indicate that the sequential scan procedure can identify a set of adjacent markers whose haplotypes might have strong genetic effects or be in linkage disequilibrium with disease predisposing variants. As a result, our methods can achieve greater power than the single-locus method, yet is much more computationally efficient than sliding window methods.

Incorporating single-locus tests into haplotype cladistic analysis in case-control studies

In case-control studies, genetic associations for complex diseases may be probed either with single-locus tests or with haplotype-based tests. Although there are different views on the relative merits and preferences of the two test strategies, haplotype-based analyses are generally believed to be more powerful to detect genes with modest effects. However, a main drawback of haplotype-based association tests is the large number of distinct haplotypes, which increases the degrees of freedom for corresponding test statistics and thus reduces the statistical power. To decrease the degrees of freedom and enhance the efficiency and power of haplotype analysis, we propose an improved haplotype clustering method that is based on the haplotype cladistic analysis developed by Durrant et al. In our method, we attempt to combine the strengths of single-locus analysis and haplotype-based analysis into one single test framework. Novel in our method is that we develop a more informative haplotype similarity measurement by using p-values obtained from single-locus association tests to construct a measure of weight, which to some extent incorporates the information of disease outcomes. The weights are then used in computation of similarity measures to construct distance metrics between haplotype pairs in haplotype cladistic analysis. To assess our proposed new method, we performed simulation analyses to compare the relative performances of (1) conventional haplotype-based analysis using original haplotype, (2) single-locus allele-based analysis, (3) original haplotype cladistic analysis (CLADHC) by Durrant et al., and (4) our weighted haplotype cladistic analysis method, under different scenarios. Our weighted cladistic analysis method shows an increased statistical power and robustness, compared with the methods of haplotype cladistic analysis, single-locus test, and the traditional haplotype-based analyses. The real data analyses also show that our proposed method has practical significance in the human genetics field.

Methods of haplotype-based analysis and single-locus analysis are widely used in genetic association studies. There is no consensus as to the best strategy for the performance of the two methods. Although haplotype-based analysis is a powerful tool, the large number of distinct haplotypes may reduce its efficiency. Haplotype clustering analysis is a promising way of decreasing haplotype dimensionality. A potential limitation of many existing clustering methods is that they do not allow the clustering to adapt to the position of the underlying trait locus. In this study, we proposed a weighted haplotype cladistic analysis method by incorporating a single-locus test into haplotype clustering. Under this framework, relationships between single loci and the disease outcomes can be considered when creating the hierarchical tree of haplotypes. The extensive simulations show that our method is robust against varied simulation conditions and is more powerful than either the original unweighted cladistic analysis method or single-locus analysis methods in case-control studies. Our hybrid method combining haplotype-based and single-locus analyses can be readily extended to whole genome association studies.

Advances in statistical human genetics over the last 25 years

The past 25 years has seen an explosion in the number of genetic markers that can be measured on DNA samples at an ever decreasing cost. Although basic statistical methods for analysing such data gathered on samples of either independent individuals or family members, one or two markers at a time, were already well developed before this explosion occurred, there has been a corresponding burst in activity to develop multiple marker models to find disease-causing gene variants, capitalizing on the data that have become available, to increase the power of such methods. This has required the concomitant development of faster algorithms to speed up the computation of various likelihoods. For linkage analysis, to obtain the approximate locations for genes of interest, Mendelian segregation models have been extended to be more realistic and statistical models that do not assume specific modes of inheritance have been extended to allow for the analysis of larger pedigree structures. For association analysis, to obtain more precise locations for genes of interest, the recent completion of the first stage of the HapMap project has spurred the development, still underway, of novel experimental designs and analytical methods to combat the curse of dimensionality and the resulting multiple testing problem. Perhaps the greatest current challenge concerns how best to gather and synthesize the many lines of evidence possible in order to discover the genetic determinants underlying complex diseases.

Detecting genome wide haplotype sharing using SNP or microsatellite haplotype data

Genome wide association studies using high throughput technology are already being conducted despite the significant hurdles that need to be overcome (Nat Rev Genet 6:95-108, 2005; Nat Rev Genet 6:109-118, 2005). Methods for detecting haplotype association signals in genome wide haplotype datasets are as yet very limited. Much methodological research has already been devoted to linkage disequilibrium (LD) fine mapping where the focus is the identification of the disease locus rather than the detection of a disease signal. Applications of these approaches to genome wide scanning are limited by the strong model assumptions of the sharing process, which lead to computational complexity. We describe a new algorithm for the initial identification of disease susceptibility loci in genome wide haplotype association studies. Excess sharing of ancestral haplotypes, which indicates the presence of a disease locus, is detected with a simple, easy to interpret, chi2 based statistic. The method allows genome wide scanning for qualitative traits within reasonable computational timeframes and can serve as a first pass analysis prior to the usage of likelihood based methods, providing candidate regions and inferred susceptibility haplotypes. Our method makes no assumptions regarding the population history or the pattern of background LD. Statistical significance is evaluated with permutation tests. The method is illustrated on simulated and real data where it is applied to simple (cystic fibrosis) and complex disease (multiple sclerosis) examples. The statistic has low type I error and greater power to map disease loci over conventional single marker tests for low to moderate levels of LD.

Variants in the Wilms' tumor gene are associated with focal segmental glomerulosclerosis in the African American population

Wilms' tumor gene (WT1) is important for nephrogenesis and gonadal growth. WT1 mutations cause Denys-Drash and Frasier syndromes, which are characterized by glomerular scarring. To test whether genetic variations in WT1 and WIT1 (gene immediately 5' to WT1) associate with focal segmental glomerulosclerosis (FSGS), patients with biopsy-proven idiopathic and HIV-1-associated FSGS were enrolled in a multicenter study. We genotyped SNP rs6508 located in WIT1 exon 1, three SNPs (rs2301250, rs2301252, rs2301254) in the promoter shared by WT1 and WIT1, rs2234590 in exon 6, rs2234591 in intron 6, rs16754 in exon 7, and rs1799937 in intron 9 of WT1. Cases (n = 218) and controls (n = 281) were compared in the African American population. Stratification by HIV-1 infection status showed that SNPs rs6508, rs2301254, and rs1799937 were significantly associated with FSGS [rs6508 odds ratio (OR) 1.82, P = 0.006; rs2301254 OR 1.65, P = 0.049; rs1799937 OR 1.91, P = 0.005] in the non-HIV-1 group and rs2234591 (OR 0.234, P = 0.011) in the HIV-1 group. Haplotype analyses in the population revealed that seven SNPs were associated with FSGS; five SNPs had the highest contingency score [-log10(P value) = 13.57] in the HIV-1 group. This association could not be explained by population substructure. We conclude that SNPs in WT1 and WIT1 genes are significantly associated with FSGS, suggesting that variants in these genes may mediate pathogenesis by altering WT1 function. Furthermore, HIV-1 infection status interacts with genetic variations in both genes to influence this phenotype. We speculate that nephropathy liability alleles in WT1 pathway genes cause podocyte dysfunction and glomerular scarring.