A coalescent approach to study linkage disequilibrium between single-nucleotide polymorphisms

Association between miR-146a rs2910164, miR-196a2 rs11614913, and miR-499 rs3746444 polymorphisms and the risk of esophageal carcinoma: A case-control study

MicroRNAs (miRNAs) are a group of small, non‐coding, and endogenous RNAs that regulate gene expression and over 50% of them are located at cancer‐related genomic regions or fragile sites. According to previous studies there is significant association of miRNA single nucleotide polymorphisms (SNPs) with tumorigenesis (e.g., esophageal cancer, hepatocellular cancer, gastric cancer, bladder cancer, breast cancer, lung cancer, and colon cancer), however, the conclusions have been inconsistent. To investigate the relationship between miR‐146a rs2910164 C > G, miR‐196a2 rs11614913 T > C, and miR‐499 rs3746444 A > G polymorphisms and the susceptibility to esophageal squamous cell cancer (ESCC) in the Chinese Han nationality, we recruited 829 cases and 1522 controls in our study. In this case–control study, our results suggest that the rs3746444 GG genotype increased ESCC risk [homozygote model: adjusted odds ratio (OR), 2.26; 95% CI, 1.33–3.83; p = 0.003, recessive model: adjusted OR, 2.34; 95% CI, 1.38–3.96; p = 0.002], which remained consistent after Bonferroni correction. There was no association of rs11614913 and rs2910164 polymorphisms with ESCC. After adjusting by age, sex, smoking, and drinking status and body mass index (BMI), the multiple logistic analysis suggested that rs11614913 T → C variation reduced ESCC susceptibility in females and in the ≥63 years old subgroups, while rs2910164 C → G variation increased ESCC risk in both two BMI subgroups.

Tissue Expression and Variation of the DGAT2 Gene and Its Effect on Carcass and Meat Quality Traits in Yak

Simple Summary

Yaks (Bos grunniens) inhabit the Qinghai-Tibetan Plateau and adjacent highlands at elevations between 2000 and 5000 m, where they are important domestic animals, as they provide meat, milk, fuel, and other necessities for Tibetans and nomads in China. Yak meat is fine in texture and high in protein, yet poor in muscular marbling and tenderness. Diacylglycerol acyltransferase-2 (DGAT2), which regulates fat deposition in animals, is a candidate gene for meat quality and quantity traits. However, there have been few reports on the effects of the DGAT2 gene on the meat quality of yak. Our study elucidated tissue expression of the yak DGAT2 gene and association of variation in the gene with Warner–Bratzler shear force of longissimus muscle. The results provide guidance for the molecular-assisted selection of meat tenderness in yak.

Abstract

Diacylglycerol acyltransferase-2 (DGAT2) plays a key role in the synthesis of animal triglycerides (TGs). This study investigated the relative expression of the DGAT2 gene in tissues, variation in the gene, and its association with carcass and meat quality traits in yaks (Bos grunniens). DGAT2 was found to be expressed in twelve tissues investigated, but the highest expression was detected in subcutaneous fat, and moderate levels were observed in the liver, heart, longissimus dorsi muscle, and abomasum. Three variants (A₁ to C₁) were found in intron 5 and another three variants (A₂ to C₂) were found in intron 6, with two single-nucleotide polymorphisms (SNPs) being identified in each region in 694 Gannan yaks. Variants B₁ and C₂ were associated with a decrease in Warner–Bratzler shear force (WBSF) (p = 0.0020 and p = 0.0441, respectively), and variant C₁ was associated with an increase in WBSF (p = 0.0434) and a decrease in drip loss rate (p = 0.0271), whereas variant B₂ was associated with a decrease in cooking loss rate (p = 0.0142). Haplotypes A₁-A₂ and B₁-A₂ were found to be, respectively, associated with an increase and a decrease in WBSF (p = 0.0191 and p = 0.0010, respectively). These results indicate that DGAT2 could be a useful gene marker for improving meat tenderness in yaks.

A non-threshold region-specific method for detecting rare variants in complex diseases

A region-specific method, NTR (non-threshold rare) variant detection method, was developed—it does not use the threshold for defining rare variants and accounts for directions of effects. NTR also considers linkage disequilibrium within the region and accommodates common and rare variants simultaneously. NTR weighs variants according to minor allele frequency and odds ratio to combine the effects of common and rare variants on disease occurrence into a single score and provides a test statistic to assess the significance of the score. In the simulations, under different effect sizes, the power of NTR increased as the effect size increased, and the type I error of our method was controlled well. Moreover, NTR was compared with several other existing methods, including the combined multivariate and collapsing method (CMC), weighted sum statistic method (WSS), sequence kernel association test (SKAT), and its modification, SKAT-O. NTR yields comparable or better power in simulations, especially when the effects of linkage disequilibrium between variants were at least moderate. In an analysis of diabetic nephropathy data, NTR detected more confirmed disease-related genes than the other aforementioned methods. NTR can thus be used as a complementary tool to help in dissecting the etiology of complex diseases.

MicroRNAs related polymorphisms and genetic susceptibility to esophageal squamous cell carcinoma

Esophageal cancer (EC) is the sixth leading cause of cancer-associated death worldwide and the incidence and mortality in China are the highest. The single nucleotide polymorphisms (SNPs) related to microRNAs could lead to alteration in microRNA expression and contribute to the susceptibility of cancer. To evaluate the association between microRNA-related SNPs and EC, a case-control study including 381 patients with esophageal squamous cell carcinoma (ESCC) and 426 gender, age-matched controls was carried out to investigate the genetic susceptibility of five microRNA-related SNPs (rs2910164 in microRNA-146a, rs11614913 in microRNA-196a-2, rs7813 in GEMIN4, rs1595066 and rs16845990 in ErbB4) as well as the interactions of gene-gene and gene-environment in the development of ESCC. Variant homozygote genotype of rs11614913 in microRNA-196a-2 and rs1595066 in ErbB4 were significantly associated with reduced ESCC risk (OR(adjusted): 0.62, 95 % CI: 0.39-0.99 and OR(adjusted): 0.38, 95 % CI: 0.24-0.61). The analysis of haplotypes in ErbB4 gene showed significant increased ESCC risk in G(rs1595066)C(rs16845990) and G(rs1595066)T(rs16845990) haplotypes (OR(adjusted): 1.46, 95 % CI: 1.08-1.99 and OR(adjusted): 1.33, 95 % CI: 1.10-1.62), and inversely reduced ESCC risk in A(rs1595066)C(rs16845990) and A(rs1595066)T(rs16845990) haplotypes with OR (95 % CI) of 0.75 (0.60-0.94) and 0.65 (0.49-0.86), respectively. These findings suggest that the polymorphisms in the microRNA-related genes may affect susceptibility of ESCC in Chinese Han population and the gene-gene interactions play vital roles in the progression on esophageal cancer. Future studies with larger sample and different ethnic populations are required to support and validate our findings.

Vascular endothelial growth factor 1498C/T, 936C/T polymorphisms associated with increased risk of colorectal adenoma: a Chinese case-control study

Single nucleotide polymorphisms in vascular endothelial growth factor gene VEGF, 1498C/T and 936 C/T are associated with colorectal cancer. We sought to determine whether such genetic variability in VEGF contributes to susceptibility of colorectal adenoma (CRA), a presumably precancerous state of colorectal cancer. In this research, two aforementioned polymorphisms were investigated for CRA susceptibility in a Chinese case-control study. The epidemiological risk factors were collected through questionnaire. The plasma VEGF levels were measured via enzyme-linked immunosorbent assay (ELISA). The Taqman-Probe assay was used to determine genotypes in 224 CRA patients and 200 CRA-free controls. The clinicopathological data of each sample were collected for further correlation analysis. According to data analysis males, cigarette smokers, patients who carry metabolic syndrome or familial antecedent of adenomas were significantly associated with CRA risk. Plasma VEGF levels of CRA patients were higher than those of controls (P = 0.003). This difference is independent of genotypes. The carriers with 936CT and CT+TT had higher risk of CRA in comparison with controls (CT vs. CC, OR 2.00, 95% CI 1.23-3.25, P = 0.006; CT+TT vs. CC, OR 2.04, 95% CI 1.28-3.26, P = 0.003). 936-T allele was associated with increased risk of CRA (OR 1.91, 95% CI 1.25-2.91, P = 0.003). Both CRA and control show no difference in the genotype of 1498C/T and the allele frequency of C-/T-. CRA patients with haplotype 1498T+936T presented significantly higher risk than those with wild-type 1498T+936C. Moreover, patients carrying 936CT+TT and 936-T allele demonstrated a tendency for villous adenoma. CRA patients have elevated plasma VEGF levels. The VEGF 936C/T polymorphism and 1498T+936T haplotype were found to be associated with increased CRA susceptibility.

Forward-time simulation of realistic samples for genome-wide association studies

Background

Forward-time simulations have unique advantages in power and flexibility for the simulation of genetic samples of complex human diseases because they can closely mimic the evolution of human populations carrying these diseases. However, a number of methodological and computational constraints have prevented the power of this simulation method from being fully explored in existing forward-time simulation methods.

Results

Using a general-purpose forward-time population genetics simulation environment, we developed a forward-time simulation method that can be used to simulate realistic samples for genome-wide association studies. We examined the properties of this simulation method by comparing simulated samples with real data and demonstrated its wide applicability using four examples, including a simulation of case-control samples with a disease caused by multiple interacting genetic and environmental factors, a simulation of trio families affected by a disease-predisposing allele that had been subjected to either slow or rapid selective sweep, and a simulation of a structured population resulting from recent population admixture.

Conclusions

Our algorithm simulates populations that closely resemble the complex structure of the human genome, while allows the introduction of signals of natural selection. Because of its flexibility to generate different types of samples with arbitrary disease or quantitative trait models, this simulation method can simulate realistic samples to evaluate the performance of a wide variety of statistical gene mapping methods for genome-wide association studies.

SKM-SNP: SNP markers detection method

SKM-SNP, SNP markers detection program, is proposed to identify a set of relevant SNPs for the association between a disease and multiple marker genotypes. We employ a subspace categorical clustering algorithm to compute a weight for each SNP in the group of patient samples and the group of normal samples, and use the weights to identify the subsets of relevant SNPs that categorize these two groups. The experiments on both Schizophrenia and Parkinson Disease data sets containing genome-wide SNPs are reported to demonstrate the program. Results indicate that our method can find some relevant SNPs that categorize the disease samples. The online SKM-SNP program is available at http://www.math.hkbu.edu.hk/~mng/SKM-SNP/SKM-SNP.html.

Replication of genetic associations as pseudoreplication due to shared genealogy

The genotypes of individuals in replicate genetic association studies have some level of correlation due to shared descent in the complete pedigree of all living humans. As a result of this genealogical sharing, replicate studies that search for genotype-phenotype associations using linkage disequilibrium between marker loci and disease-susceptibility loci can be considered as "pseudoreplicates" rather than true replicates. We examine the size of the pseudoreplication effect in association studies simulated from evolutionary models of the history of a population, evaluating the excess probability that both of a pair of studies detect a disease association compared to the probability expected under the assumption that the two studies are independent. Each of nine combinations of a demographic model and a penetrance model leads to a detectable pseudoreplication effect, suggesting that the degree of support that can be attributed to a replicated genetic association result is less than that which can be attributed to a replicated result in a context of true independence.

A variable-sized sliding-window approach for genetic association studies via principal component analysis

Recently with the rapid improvements in high-throughout genotyping techniques, researchers are facing the very challenging task of analysing large-scale genetic associations, especially at the whole-genome level, without an optimal solution. In this study, we propose a new approach for genetic association analysis that is based on a variable-sized sliding-window framework and employs principal component analysis to find the optimum window size. With the help of the bisection algorithm in window-size searching, our method is more computationally efficient than available approaches. We evaluate the performance of the proposed method by comparing it with two other methods-a single-marker method and a variable-length Markov chain method. We demonstrate that, in most cases, the proposed method out-performs the other two methods. Furthermore, since the proposed method is based on genotype data, it does not require any computationally intensive phasing program to account for uncertain haplotype phase.

Genome-wide insights into the patterns and determinants of fine-scale population structure in humans

Studying genomic patterns of human population structure provides important insights into human evolutionary history and the relationship among populations, and it has significant practical implications for disease-gene mapping. Here we describe a principal component (PC)-based approach to studying intracontinental population structure in humans, identify the underlying markers mediating the observed patterns of fine-scale population structure, and infer the predominating evolutionary forces shaping local population structure. We applied this methodology to a data set of 650K SNPs genotyped in 944 unrelated individuals from 52 populations and demonstrate that, although typical PC analyses focus on the top axes of variation, substantial information about population structure is contained in lower-ranked PCs. We identified 18 significant PCs, some of which distinguish individual populations. In addition to visually representing sample clusters in PC biplots, we estimated the set of all SNPs significantly correlated with each of the most informative axes of variation. These polymorphisms, unlike ancestry-informative markers (AIMs), constitute a much larger set of loci that drive genomic signatures of population structure. The genome-wide distribution of these significantly correlated markers can largely be accounted for by the stochastic effects of genetic drift, although significant clustering does occur in genomic regions that have been previously implicated as targets of recent adaptive evolution.

A coalescent simulation of marker selection strategy for candidate gene association studies

Recent efforts have focused on the challenges of finding alleles that contribute to health-related phenotypes in genome-wide association studies. However, in candidate gene studies, where the genomic region of interest is small and recombination is limited, factors that affect the ability to detect disease-susceptibility alleles remain poorly understood. In particular, it is unclear how varying the number of markers on a haplotype, the type of marker (e.g., single nucleotide polymorphism (SNP), short tandem repeat (STR)), including the causative site (cs) as a genetic marker, or population demographics influences the power to detect a candidate gene. We evaluated the power of association tests using coalescent-modeled computer simulations. Results show that an effective number of markers on a haplotype is dependent on whether the cs is included as a marker. When the analyses include the cs, highest power is achieved with a single-marker association test. However, when the cs is excluded from analyses, the addition of more nonfunctional SNPs on the haplotype increases power to a certain point under most scenarios. We find a rapidly expanding population always has lower power compared to a population of constant size; although utilizing markers with a frequency of at least 5% improves the chance of detecting an association. Comparing the mutational properties of a nonfunctional SNP versus an STR, multi-allelic STRs provide more or comparable power than a bi-allelic SNP unless SNP frequencies are constrained to 10% or more. Similarly, including an STR with SNPs on a haplotype improves power unless SNP frequencies are 5% or more.

Combining association tests across multiple genetic markers in case-control studies

In the search to detect genetic associations between complex traits and DNA variants, a practice is to select a subset of Single Nucleotide Polymorphisms (tag SNPs) in a gene or chromosomal region of interest. This allows study of untyped polymorphisms in this region through the phenomenon of linkage disequilibrium (LD). However, it is crucial in the analysis to utilize such multiple SNP markers efficiently. In this study, we present a robust testing approach (T(C)) that combines single marker association test statistics or p values. This combination is based on the summation of single test statistics or p values, giving greater weight to those with lower p values. We compared the powers of T(C) in identifying common trait loci, using tag SNPs within the same haplotype block that the trait loci reside, with competing published tests, in case-control settings. These competing tests included the Bonferroni procedure (T(B)), the simple permutation procedure (T(P)), the permutation procedure proposed by Hoh et al. (T(P-H)) and its revised version using 'deflated' statistics (T(P-H_def)), the traditional chi(2) procedure (T(CHI)), the regression procedure (Hotelling T(2) test) (T(R)) and the haplotype-based test (T(H)). Results of these comparisons show that our proposed combining procedure (T(C)) is preferred in all scenarios examined. We also apply this new test to a data set from a previously reported association study on airway responsiveness to methacholine.

GENOME: a rapid coalescent-based whole genome simulator

SUMMARY

GENOME proposes a rapid coalescent-based approach to simulate whole genome data. In addition to features of standard coalescent simulators, the program allows for recombination rates to vary along the genome and for flexible population histories. Within small regions, we have evaluated samples simulated by GENOME to verify that GENOME provides the expected LD patterns and frequency spectra. The program can be used to study the sampling properties of any statistic for a whole genome study.

AVAILABILITY

The program and C++ source code are available online at http://www.sph.umich.edu/csg/liang/genome/

Association mapping via regularized regression analysis of single-nucleotide-polymorphism haplotypes in variable-sized sliding windows

Large-scale haplotype association analysis, especially at the whole-genome level, is still a very challenging task without an optimal solution. In this study, we propose a new approach for haplotype association analysis that is based on a variable-sized sliding-window framework and employs regularized regression analysis to tackle the problem of multiple degrees of freedom in the haplotype test. Our method can handle a large number of haplotypes in association analyses more efficiently and effectively than do currently available approaches. We implement a procedure in which the maximum size of a sliding window is determined by local haplotype diversity and sample size, an attractive feature for large-scale haplotype analyses, such as a whole-genome scan, in which linkage disequilibrium patterns are expected to vary widely. We compare the performance of our method with that of three other methods--a test based on a single-nucleotide polymorphism, a cladistic analysis of haplotypes, and variable-length Markov chains--with use of both simulated and experimental data. By analyzing data sets simulated under different disease models, we demonstrate that our method consistently outperforms the other three methods, especially when the region under study has high haplotype diversity. Built on the regression analysis framework, our method can incorporate other risk-factor information into haplotype-based association analysis, which is becoming an increasingly necessary step for studying common disorders to which both genetic and environmental risk factors contribute.

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.

Backward simulation of ancestors of sampled individuals

If the population is large and the sampling mechanism is random, the coalescent is commonly used to model the haplotypes in the sample. Ordered genotypes can then be formed by random matching of the derived haplotypes. However, this approach is not realistic when (1) there is departure from random mating (e.g., dominant individuals in breeding populations or monogamy in humans), or (2) the population is small and/or the individuals in the sample are ascertained by applying some particular non-random sampling scheme, as is usually the case when considering the statistical modeling and analysis of pedigree data. For such situations, we present here a data generation method where an ancestral graph with non-overlapping generations is first generated backwards in time, using ideas from coalescent theory. Alleles are randomly assigned to the founders, and subsequently the gene flow over the entire genome is simulated forwards in time by dropping alleles down the graph according to recombination model without interference. The parameters controlling the mating behavior of generated individuals in the graph (degree of monogamy) can be tuned in order to match a particular demographic situation, without restriction to simple random mating. The performance of the approach is illustrated with a simulation example. The software (written in C-language) is freely available for research purposes at http://www.rni.helsinki.fi/~dag/.

In silico analysis of disease-association mapping strategies using the coalescent process and incorporating ascertainment and selection

We present a new method for simulating samples of marker haplotypes, genotypes, or diplotypes in case-control studies in which the markers are linked to a disease locus in any specified region of the genome. The method allows realistic features to be incorporated into the simulations, including selection acting on disease alleles, sample ascertainment of disease chromosomes and polymorphic markers, a genetic dominance model of disease expression that allows incomplete penetrance and phenocopies, and an accurate genetic map of recombination rates and hotspots for recombination in the human genome (or, alternatively, an improved method for simulating the distribution of hotspots). The new method uses an approach that combines simulation of the coalescent process for the sampled chromosomes with a diffusion process used to model the evolution of the disease-mutation frequency over time. Examples illustrate how the method may be used to study the expected power of a marker-disease association study.

Recovering frequencies of known haplotype blocks from single-nucleotide polymorphism allele frequencies

Prospects for large-scale association studies rely on economical methods and powerful analysis. Representing available SNPs by small subsets and measuring allele frequencies on pooled DNA samples each improve genotyping cost effectiveness, while haplotype analysis may highlight associations in otherwise underpowered studies. This manuscript provides the mathematical framework to integrate these methodologies.

Fine mapping by linkage and association in nuclear family and case-control designs

This report summarizes the Genetic Analysis Workshop 14 contributions related to fine-mapping strategies, in which examining smaller regions by association with single-nucleotide polymorphisms (SNPs) can yield savings in genotyping and multiple-testing penalties. The aim of the analyses conducted in Group 7 contributions was to localize disease susceptibility loci from either the simulated or the Collaborative Study on the Genetics of Alcoholism (COGA) data within identified regions of linkage. Among the 10 contributions, most groups analyzed the simulated data, one group analyzed the COGA data only, and one group analyzed both data sets. The research questions included evaluation of new methods of analysis, as well as comparisons among alternative methods, analytic strategies, and study designs. Methods of interest included an algorithm for SNP marker ordering, a locally weighted transmission disequilibrium test statistic, a likelihood-ratio test statistic for family-based association in nuclear families, a robust test statistic for case-control association studies, and Bayesian spatial modeling methods for haplotype clustering and association. Evaluations included comparisons among confidence intervals for loci detected via linkage, effects of multiple testing adjustments and trade-offs between type I error and power, comparisons among haplotype-based (multilocus) and genotype-based (multilocus and single-locus) association analyses, and design of fine-mapping and replication studies. While several promising new approaches were identified, further development and evaluation of methods for multiple testing, regression modeling of association with multiple markers and haplotypes, and combined treatment of linkage and association data are necessary if we are to identify many of the genes that contribute to complex traits.

Coalescent-based association mapping and fine mapping of complex trait loci

We outline a general coalescent framework for using genotype data in linkage disequilibrium-based mapping studies. Our approach unifies two main goals of gene mapping that have generally been treated separately in the past: detecting association (i.e., significance testing) and estimating the location of the causative variation. To tackle the problem, we separate the inference into two stages. First, we use Markov chain Monte Carlo to sample from the posterior distribution of coalescent genealogies of all the sampled chromosomes without regard to phenotype. Then, averaging across genealogies, we estimate the likelihood of the phenotype data under various models for mutation and penetrance at an unobserved disease locus. The essential signal that these models look for is that in the presence of disease susceptibility variants in a region, there is nonrandom clustering of the chromosomes on the tree according to phenotype. The extent of nonrandom clustering is captured by the likelihood and can be used to construct significance tests or Bayesian posterior distributions for location. A novelty of our framework is that it can naturally accommodate quantitative data. We describe applications of the method to simulated data and to data from a Mendelian locus (CFTR, responsible for cystic fibrosis) and from a proposed complex trait locus (calpain-10, implicated in type 2 diabetes).

Bayesian spatial modeling of haplotype associations

We review methods for relating the risk of disease to a collection of single nucleotide polymorphisms (SNPs) within a small region. Association studies using case-control designs with unrelated individuals could be used either to test for a direct effect of a candidate gene and characterize the responsible variant(s), or to fine map an unknown gene by exploiting the pattern of linkage disequilibrium (LD). We consider a flexible class of logistic penetrance models based on haplotypes and compare them with an alternative formulation based on unphased multilocus genotypes. The likelihood for haplotype-based models requires summation over all possible haplotype assignments consistent with the observed genotype data, and can be fitted using either Expectation-Maximization (E-M) or Markov chain Monte Carlo (MCMC) methods. Subtleties involving ascertainment correction for case-control studies are discussed. There has been great interest in methods for LD mapping based on the coalescent or ancestral recombination graphs as well as methods based on haplotype sharing, both of which we review briefly. Because of their computational complexity, we propose some alternative empirical modeling approaches using techniques borrowed from the Bayesian spatial statistics literature. Here, space is interpreted in terms of a distance metric describing the similarity of any pair of haplotypes to each other, and hence their presumed common ancestry. Specifically, we discuss the conditional autoregressive model and two spatial clustering models: Potts and Voronoi. We conclude with a discussion of the implications of these methods for modeling cryptic relatedness, haplotype blocks, and haplotype tagging SNPs, and suggest a Bayesian framework for the HapMap project.

Primary hypercholesterolemia: genetic causes and treatment of five monogenic disorders

Coronary heart disease is a major cause of death in Europe and the USA. Insudation of atherogenic lipoproteins, including low-density lipoprotein (LDL), into the artery wall is integral to atherosclerosis. It is clear that numerous genetic loci contribute to increased plasma levels of LDL. However, five specific monogenic disorders, three of which have been reported recently, are known to increase LDL. These are familial hypercholesterolemia (LDL receptor gene: LDLR); familial ligand-defective apoB- 100 (apoB gene: APOB); autosomal recessive hypercholesterolemia (ARH gene); sitosterolemia (ABCG5 or ABCG8 genes) and cholesterol 7alpha-hydroxylase deficiency (CYP7A1 gene). This review relates the mechanisms underlying these five disorders with specific therapeutic interventions.

A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other

In this report, we describe a simple correction for multiple testing of single-nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD) with each other, on the basis of the spectral decomposition (SpD) of matrices of pairwise LD between SNPs. This method provides a useful alternative to more computationally intensive permutation tests. A user-friendly interface (SNPSpD) for performing this correction is available online (http://genepi.qimr.edu.au/general/daleN/SNPSpD/). Additionally, output from SNPSpD includes eigenvalues, principal-component coefficients, and factor "loadings" after varimax rotation, enabling the selection of a subset of SNPs that optimize the information in a genomic region.

Association of tumor necrosis factor alpha gene -G308A polymorphism with schizophrenia

BACKGROUND

Tumor necrosis factor alpha (TNFalpha), a cytokine involved in inflammatory processes, has been implicated in the pathophysiology of schizophrenia. The chromosomal location in the major histocompatibility complex (MHC) region on 6p21.1-21.3, a region with evidence for linkage, suggests a role in susceptibility to schizophrenia. Association of the minor (A) allele of the -G308A TNFalpha gene polymorphism with schizophrenia has been reported [Mol. Psychiatry 6 (2001) 79].

METHODS

Association of the -G308A TNFalpha gene and the lymphotoxin alpha (LTalpha)+A252G gene polymorphisms with schizophrenia was studied in 79 sib pair families with linkage in the MHC region and in 128 trio families using the transmission disequilibrium test (TDT).

RESULTS

Weak association of the common G allele was detected for TNFalpha -G308A in both samples independently with borderline significance in the sib pair families (0.064) and with a nominally significant value of P=0.022 in the trio families. Combining both samples produced P=0.003, while LTalpha+A252G, located approximately 2-3 kb distally, revealed P=0.03 and the two locus haplotype yielded a P value of 0.001.

CONCLUSION

Our data suggests association of the common G allele of the -G308A TNFalpha gene polymorphism with schizophrenia in a sample of 207 families. However, linkage disequilibrium with a different allele of the TNFalpha gene or another gene in the MHC region cannot be excluded.

Estimating recombination rates from population-genetic data

Obtaining an accurate measure of how recombination rates vary across the genome has implications for understanding the molecular basis of recombination, its evolutionary significance and the distribution of linkage disequilibrium in natural populations. Although measuring the recombination rate is experimentally challenging, good estimates can be obtained by applying population-genetic methods to DNA sequences taken from natural populations. Statistical methods are now providing insights into the nature and scale of variation in the recombination rate, particularly in humans. Such knowledge will become increasingly important owing to the growing use of population-genetic methods in biomedical research.

Comparison of strategies for selecting single nucleotide polymorphisms for case/control association studies

It is widely believed that a subset of single nucleotide polymorphisms (SNPs) is able to capture the majority of the information for genotype-phenotype association studies that is contained in the complete compliment of genetic variations. The question remains, how does one select that particular subset of SNPs in order to maximize the power of detecting a significant association? In this study, we have used a simulation approach to compare three competing methods of site selection: random selection, selection based on pair-wise linkage disequilibrium, and selection based on maximizing haplotype diversity. The results indicate that site selection based on maximizing haplotype diversity is preferred over random selection and selection based on pair-wise linkage disequilibrium. The results also indicate that it is more prudent to increase the sample size to improve a study's power than to continuously increase the number of SNPs. These results have direct implications for designing gene-based and genome-wide association studies.

Linkage disequilibrium analysis of the human adenosine deaminase (ada) gene provides evidence for a lack of correlation between hot spots of equal and unequal homologous recombination

The linkage disequilibrium (LD) pattern within the adenosine deaminase (ADA) gene was analyzed by studying 13 polymorphic loci in 137 families from two European and three African populations. Evidence for the presence of a 12-kb meiotic crossover hot spot, spanning part of the first and the second intron and flanked by regions of reduced recombination activity, was obtained. Moreover, segregation analysis of 113 informative meioses revealed two recombination events that are internal or overlap the 12-kb region, thus suggesting a recombination rate for the hot-spot region about 50-fold higher than the mean rate across the human genome. Within the hot spot, a 144-bp palindromic sequence was also identified and its possible involvement in the recombination process is discussed. The 12-kb region characterized by the low degree of LD does not include the 3.2-kb region that is deleted, as a result of recurrent unequal homologous recombination between two Alu elements, in patients affected by autosomal severe combined immunodeficiency. This observation provides the first evidence for an absence of correlation between hot spots of equal and unequal homologous recombination.

Genetics of obesity

Obesity is a typical common multifactorial disease in which environmental and genetic factors interact. In rare cases of severe obesity with childhood onset, a single gene has a major effect in determining the occurrence of obesity, with the environment having only a permissive role in the severity of the phenotype. Exceptional mutations of the leptin gene and its receptor, pro-opiomelanocortine (POMC), prohormone convertase 1 (PC1) and more frequently, mutations in the melanocortin receptor 4 (1 to 4% of very obese cases) have been described. All these obesity genes encode proteins that are strongly connected as part of the same loop of the regulation of food intake. They all involve the leptin axis and one of its hypothalamic targets; the melanocortin pathway. Pathways of bodyweight regulation involved in monogenic forms of obesity might represent targets for future drug development. Successful leptin protein replacement in a leptin-deficient child has contributed to the validation of the usefulness of gene screening in humans. However, the individual variability in response to leptin treatment might be related to genetic variability. The efficiency of leptin itself or of small-molecule agonists of the leptin receptor should be studied in relation with genetic variations in the leptin gene promoter. The most common forms of obesity are polygenic. Two general approaches have been used to date in the search for genes underlying common polygenic obesity in humans. The first approach focuses on selected genes having some plausible role in obesity on the basis of their known or presumed biological role. This approach yielded putative susceptibility genes with only small or uncertain effects. The second approach attempts to map genes purely by position and requires no presumptions on the function of genes. Genome-wide scans identify chromosomal regions showing linkage with obesity in large collections of nuclear families. Genome-wide scans in different ethnic populations have localized major obesity loci on chromosomes 2, 5, 10, 11 and 20. Susceptibility gene(s) for obesity may be positionally cloned in the intervals of linkage. The candidate gene and positional cloning of major obesity-linked regions approaches are discussed in this paper.

Estimation of haplotype frequencies, linkage-disequilibrium measures, and combination of haplotype copies in each pool by use of pooled DNA data

Inference of haplotypes is important for many genetic approaches, including the process of assigning a phenotype to a genetic region. Usually, the population frequencies of haplotypes, as well as the diplotype configuration of each subject, are estimated from a set of genotypes of the subjects in a sample from the population. We have developed an algorithm to infer haplotype frequencies and the combination of haplotype copies in each pool by using pooled DNA data. The input data are the genotypes in pooled DNA samples, each of which contains the quantitative genotype data from one to six subjects. The algorithm infers by the maximum-likelihood method both frequencies of the haplotypes in the population and the combination of haplotype copies in each pool by an expectation-maximization algorithm. The algorithm was implemented in the computer program LDPooled. We also used the bootstrap method to calculate the standard errors of the estimated haplotype frequencies. Using this program, we analyzed the published genotype data for the SAA (n=156), MTHFR (n=80), and NAT2 (n=116) genes, as well as the smoothelin gene (n=102). Our study has shown that the frequencies of major (frequency >0.1 in a population) haplotypes can be inferred rather accurately from the pooled DNA data by the maximum-likelihood method, although with some limitations. The estimated D and D' values had large variations except when the /D/ values were >0.1. The estimated linkage-disequilibrium measure rho2 for 36 linked loci of the smoothelin gene when one- and two-subject pool protocols were used suggested that the gross pattern of the distribution of the measure can be reproduced using the two-subject pool data.

Linkage disequilibrium mapping identifies a 390 kb region associated with CYP2D6 poor drug metabolising activity

The cytochrome p450 enzyme, CYP2D6, metabolises approximately 20% of marketed drugs. CYP2D6 multiple variants are associated with altered enzyme activities. Genotyping 1018 Caucasians for CYP2D6 polymorphisms (G1846A, delT1707, delA2549 and A2935C), known to result in the recessive CYP2D6 poor drug metaboliser (PM) phenotype, identified 41 individuals with predicted PM phenotype. These 41 individuals were classified as 'cases'. Single nucleotide polymorphisms (SNPs) mapping within an 880 kb region flanking CYP2D6, were identified to evaluate potential association between genetic variation and the CYP2D6 PM phenotype. The 41 PM cases and 977 controls were genotyped and analysed for 27 SNPs. Associations were observed across a 390 kb region between 14 SNPs and the PM phenotype (P values from 6.20 x 10(-4) to 4.54 x 10(-35)). Haplotype analysis revealed more significant levels of association (P = 3.54 x 10(-56)). Strong (D' > 0.7) linkage disequilibrium (LD) between SNPs was observed across the same 390 kb region associated with the CYP2D6 phenotype. The observed phenotype:genotype association reached genome-wide levels of significance, and supports the strategy for potential application of LD mapping and whole genome association scans to pharmacogenetic studies.

Linkage disequilibrium between dopamine D1 receptor gene (DRD1) and bipolar disorder

BACKGROUND

Based on the dopamine hypothesis, the dopamine D1 receptor gene (DRD1) is considered to be a good candidate gene for bipolar disorder (BP).

METHODS

In our study, three polymorphisms of the DRD1 gene, -800T/C, -48A/G, and 1403T/C, were analyzed in 286 BP trios. Both the transmission disequilibrium test (TDT) and haplotype TDT were performed on the genotype data to test for the presence of linkage disequilibrium between DRD1 and bipolar disorder. With the extended transmission disequilibrium test (ETDT), we also calculated the maternal transmission and paternal transmission for each allele.

RESULTS

Although no association was found for each individual polymorphism, there is a significant association between DRD1 and BP for haplotype TDT analysis (chi(2) = 16.068, df = 3, p =.0011).

CONCLUSIONS

These results indicate that DRD1 may play a role in the etiology of bipolar disorder.

DNA Pooling: a tool for large-scale association studies

DNA pooling is a practical way to reduce the cost of large-scale association studies to identify susceptibility loci for common diseases. Pooling allows allele frequencies in groups of individuals to be measured using far fewer PCR reactions and genotyping assays than are used when genotyping individuals. Here, we discuss recent developments in quantitative genotyping assays and in the design and analysis of pooling studies. Sophisticated pooling designs are being developed that can take account of hidden population stratification, confounders and inter-loci interactions, and that allow the analysis of haplotypes.

An efficient haplotyping method with DNA pools

Determination of haplotype frequencies (the joint distribution of genetic markers) in large population samples is a powerful tool for association studies. This is due to their greater extent of polymorphism since any two bi-allelic single nucleotide polymorphisms (SNPs) generate a potential four-allele genetic marker. Therefore, a haplotype may capture a given functional polymorphism with higher statistical power than its SNP components. The statistical estimation of haplotype frequencies, usually employed in linkage disequilibrium studies, requires individual genotyping for each SNP in the haplotype, thus making it an expensive process. In this study, we describe a new method for direct measurement of haplotype frequencies in DNA pools by allele-specific, long-range haplotype amplification. The proposed method allows the efficient determination of haplotypes composed of two SNPs in close vicinity (up to 20 kb).

Precision and high-resolution mapping of quantitative trait loci by use of recurrent selection, backcross or intercross schemes

Dissecting quantitative genetic variation into genes at the molecular level has been recognized as the greatest challenge facing geneticists in the twenty-first century. Tremendous efforts in the last two decades were invested to map a wide spectrum of quantitative genetic variation in nearly all important organisms onto their genome regions that may contain genes underlying the variation, but the candidate regions predicted so far are too coarse for accurate gene targeting. In this article, the recurrent selection and backcross (RSB) schemes were investigated theoretically and by simulation for their potential in mapping quantitative trait loci (QTL). In the RSB schemes, selection plays the role of maintaining the recipient genome in the vicinity of the QTL, which, at the same time, are rapidly narrowed down over multiple generations of backcrossing. With a high-density linkage map of DNA polymorphisms, the RSB approach has the potential of dissecting the complex genetic architecture of quantitative traits and enabling the underlying QTL to be mapped with the precision and resolution needed for their map-based cloning to be attempted. The factors affecting efficiency of the mapping method were investigated, suggesting guidelines under which experimental designs of the RSB schemes can be optimized. Comparison was made between the RSB schemes and the two popular QTL mapping methods, interval mapping and composite interval mapping, and showed that the scenario of genomic distribution of QTL that was unlocked by the RSB-based mapping method is qualitatively distinguished from those unlocked by the interval mapping-based methods.

Extensive linkage disequilibrium in small human populations in Eurasia

The extent of linkage disequilibrium (LD) was studied in two small food-gathering populations-Evenki and Saami-and two larger food-producing populations-Finns and Swedes-in northern Eurasia. In total, 50 single-nucleotide polymorphisms (SNPs) from five genes were genotyped using real-time pyrophosphate DNA sequencing, whereas 14 microsatellites were genotyped in two X-chromosomal regions. In addition, hypervariable region I of the mtDNA was sequenced to shed light on the demographic history of the populations. The SNP data, as well as the microsatellite data, reveal extensive levels of LD in Evenki and Saami when compared to Finns and Swedes. mtDNA-sequence variation is compatible with constant population size over time in Evenki and Saami but indicates population expansion in Finns and Swedes. Furthermore, the similarity between Finns and Swedes in SNP allele- and haplotype-frequency distributions indicate that these two populations may share a recent common origin. These findings suggest that populations such as the Evenki and the Saami, rather than the Finns, may be particularly suited for the initial coarse mapping of common complex diseases.

Prospects for association-based fine mapping of a susceptibility gene for a complex disease

The potential of association studies for fine-mapping loci with common disease susceptibility alleles for complex genetic diseases in outbred populations is unclear. For a battery of tightly linked anonymous genetic markers spanning a candidate region centered around a disease locus, simulation methods based on a coalescent process with mutation, recombination, and genetic drift were used to study the spatial distribution of markers with large noncentrality parameters in a case-control study design. Simulations with a disease allele at intermediate frequency, presumably representing an old mutation, tend to exhibit the largest noncentrality parameter values at markers near the disease locus. In contrast, simulations with a disease allele at low frequency, presumably representing a young mutation, often exhibit the largest noncentrality parameter values at markers scattered over the candidate region. In the former case, sample sizes or marker densities sufficient to detect association are likely to lead to useful localization, whereas, in the latter case, localization of the disease locus within the candidate region is much less likely, regardless of the sample size or density of the map. The simulations suggest that for a single marker analysis, the simple strategy of choosing the marker with smallest associated P value to begin a laboratory search for the disease locus performs adequately for a common disease allele.

Protein variation in Adh and Adh-related in Drosophila pseudoobscura. Linkage disequilibrium between single nucleotide polymorphisms and protein alleles

A 3.5-kb segment of the alcohol dehydrogenase (Adh) region that includes the Adh and Adh-related genes was sequenced in 139 Drosophila pseudoobscura strains collected from 13 populations. The Adh gene encodes four protein alleles and rejects a neutral model of protein evolution with the McDonald-Kreitman test, although the number of segregating synonymous sites is too high to conclude that adaptive selection has operated. The Adh-related gene encodes 18 protein haplotypes and fails to reject an equilibrium neutral model. The populations fail to show significant geographic differentiation of the Adh-related haplotypes. Eight of 404 single nucleotide polymorphisms (SNPs) in the Adh region were in significant linkage disequilibrium with three ADHR protein alleles. Coalescent simulations with and without recombination were used to derive the expected levels of significant linkage disequilibrium between SNPs and 18 protein haplotypes. Maximum levels of linkage disequilibrium are expected for protein alleles at moderate frequencies. In coalescent models without recombination, linkage disequilibrium decays between SNPs and high frequency haplotypes because common alleles mutate to haplotypes that are rare or that reach moderate frequency. The implication of this study is that linkage disequilibrium mapping has the highest probability of success with disease-causing alleles at frequencies of 10%.

Genetics of human obesity

Obesity is a multifactorial condition. Environmental risk factors related to a sedentary life-style and unlimited access to food apply constant pressure in subjects with a genetic predisposition to gain weight. The fact that genetic defects can result in human obesity has been unequivocally established over the past 3 years with the identification of the genetic defects responsible for different monogenic forms of human obesity: the leptin, leptin receptor, pro-opiomelanocortin, pro-hormone convertase-1 and melanocortin-4 receptor genes. The common forms of obesity are, however, polygenic. The examination of specific genes for involvement in the susceptibility to common obesity has not yet yielded convincing results. Approaches involving the candidate genes and the positional cloning of major obesity-linked regions (state-of-the-art future prospects) will be discussed.

Are rare variants responsible for susceptibility to complex diseases?

Little is known about the nature of genetic variation underlying complex diseases in humans. One popular view proposes that mapping efforts should focus on identification of susceptibility mutations that are relatively old and at high frequency. It is generally assumed-at least for modeling purposes-that selection against complex disease mutations is so weak that it can be ignored. In this article, I propose an explicit model for the evolution of complex disease loci, incorporating mutation, random genetic drift, and the possibility of purifying selection against susceptibility mutations. I show that, for the most plausible range of mutation rates, neutral susceptibility alleles are unlikely to be at intermediate frequencies and contribute little to the overall genetic variance for the disease. Instead, it seems likely that the bulk of genetic variance underlying diseases is due to loci where susceptibility mutations are mildly deleterious and where there is a high overall mutation rate to the susceptible class. At such loci, the total frequency of susceptibility mutations may be quite high, but there is likely to be extensive allelic heterogeneity at many of these loci. I discuss some practical implications of these results for gene mapping efforts.

Issues concerning association studies for fine mapping a susceptibility gene for a complex disease

The usefulness of association studies for fine mapping loci with common susceptibility alleles for complex genetic diseases in outbred populations is unclear. We investigate this issue for a battery of tightly linked anonymous genetic markers spanning a candidate region centered around a disease locus, and study the joint behavior of chi-square statistics used to discover and to localize the disease locus. We used simulation methods based on a coalescent process with mutation, recombination, and genetic drift to examine the spatial distribution of markers with large noncentrality parameters in a case-control study design. Simulations with a disease allele at intermediate frequency, presumably representing an old mutation, tend to exhibit the largest noncentrality parameter values at markers near the disease locus. In contrast, simulations with a disease allele at low frequency, presumably representing a young mutation, often exhibit the largest noncentrality parameter values at markers scattered over the candidate region. In the former cases, sample sizes or marker densities sufficient to detect association are likely to lead to useful localization, whereas, in the latter case, localization of the disease locus within the candidate region is much less likely, regardless of the sample size or density of the map. The effects of increasing sample size or marker density are also investigated. Based upon a single marker analysis, we find that a simple strategy of choosing the marker with the smallest associated P value to begin a laboratory search for the disease locus performs adequately for a common disease allele. We also investigated a strategy of pooling nearby sites to form multiple allele markers. Using multiple degree of freedom chi-square tests for two or three nearby sites, we found no clear advantage of this form of pooling over a single marker analysis. Genet. Epidemiol. 20:432-457, 2001. Published by Wiley-Liss, 2001.

A chronology of fine-scale gene mapping by linkage disequilibrium

The past decade produced several proposals for fine-scale gene mapping using linkage disequilibrium data. The suggested methods fall into two main groups, those that rely on pairwise statistics and those that rely on haplotypes. This paper reviews each strategy's development from a chronological perspective.

Using single nucleotide polymorphisms as a means to understanding the pathophysiology of asthma

Asthma is the most common chronic childhood disease in the developed nations, and is a complex disease that has high social and economic costs. Studies of the genetic etiology of asthma offer a way of improving our understanding of its pathogenesis, with the goal of improving preventive strategies, diagnostic tools, and therapies. Considerable effort and expense have been expended in attempts to detect specific polymorphisms in genetic loci contributing to asthma susceptibility. Concomitantly, the technology for detecting single nucleotide polymorphisms (SNPs) has undergone rapid development, extensive catalogues of SNPs across the genome have been constructed, and SNPs have been increasingly used as a method of investigating the genetic etiology of complex human diseases. This paper reviews both current and potential future contributions of SNPs to our understanding of asthma pathophysiology.

An isochore transition in the NF1 gene region coincides with a switch in the extent of linkage disequilibrium

Whole-genome association studies will be a powerful tool to identify genes responsible for common human diseases. A crucial task for association-mapping studies is the evaluation of the relationship between linkage disequilibrium (LD) and physical distance for the genomic region under study. Since it is known that the extent of LD is nonuniformly distributed throughout the human genome, the required marker density has to be determined specifically for the region under study. These regions may be related to isochores and chromosomal bands, as indicated by earlier cytogenetic findings concerning chiasma distribution in meiosis. Therefore we analyzed the neurofibromatosis type 1 (NF1) gene region on chromosome 17q11.2, which is characterized by a nonuniform LD pattern and an L1-to-H2 isochore transition. Long-range LD within the NF1 gene was found to extend over 200 kb (D' = 0.937) in the L1 isochore, whereas, in the neighboring H2 isochore, no LD is apparent between markers spaced by 26 kb (D' = 0.144). Recombination frequencies derived from the LD are at.00019 (high LD) and.01659 (low LD) per megabase, the latter identical to the average value from segregation analysis. The boundary between these regions coincides precisely with a transition in the GC content of the sequences, with low values (37.2%) in the region with long-range LD and high values (51%) in the other. Our results suggest a correlation between the LD pattern and the isochores, at least in the NF1 region. If this correlation can be generalized, the marker densities required for association studies have to be adjusted to the regional GC content and may be chosen according to the isochores.