Presence Of Genetic Heterogeneity Research Articles

A conditional autoregressive model for genetic association analysis accounting for genetic heterogeneity.

Converging evidence from genetic studies and population genetics theory suggest that complex diseases are characterized by remarkable genetic heterogeneity, and individual rare mutations with different effects could collectively play an important role in human diseases. Many existing statistical models for association analysis assume homogeneous effects of genetic variants across all individuals, and could be subject to power loss in the presence of genetic heterogeneity. To consider possible heterogeneous genetic effects among individuals, we propose a conditional autoregressive model. In the proposed method, the genetic effect is considered as a random effect and a score test is developed to test the variance component of genetic random effect. Through simulations, we compare the type I error and power performance of the proposed method with those of the generalized genetic random field and the sequence kernel association test methods under different disease scenarios. We find that our method outperforms the other two methods when (i) the rare variants have the major contribution to the disease, or (ii) the genetic effects vary in different individuals or subgroups of individuals. Finally, we illustrate the new method by applying it to the whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative.

Extension of SKAT to multi-category phenotypes through a geometrical interpretation.

Rare genetic variants are expected to play an important role in disease and several statistical methods have been developed to test for disease association with rare variants, including variance-component tests. These tests however deal only with binary or continuous phenotypes and it is not possible to take advantage of a suspected heterogeneity between subgroups of patients. To address this issue, we extended the popular rare-variant association test SKAT to compare more than two groups of individuals. Simulations under different scenarios were performed that showed gain in power in presence of genetic heterogeneity and minor lack of power in absence of heterogeneity. An application on whole-exome sequencing data from patients with early- or late-onset moyamoya disease also illustrated the advantage of our SKAT extension. Genetic simulations and SKAT extension are implemented in the R package Ravages available on GitHub ( https://github.com/genostats/Ravages ).

Genetic Heterogeneity in Heterocellular Hereditary Persistence of Fetal Hemoglobin

AbstractA large English pedigree in which heterocellular hereditary persistence of fetal hemoglobin (HPFH) segregates is described. β-globin cluster deletions and γ gene promoter mutations associated with HPFH have been excluded. Of particular importance in this pedigree is the absence of any cosegregating hemoglobinopathy, thus allowing observation of the segregation pattern of this form of HPFH without the complicating effect of a β-globin gene mutation. Information gained in this study confirms that the extent of elevation of hemoglobin (Hb) F and F cells varies between affected individuals. There are one example of incomplete penetrance and three examples of father-to-son transmission, thus excluding X-linked inheritance. Consistent with previous reports, the most likely mode of inheritance is autosomal codominant. Linkage studies using a β-globin cluster microsatellite show no evidence of linkage to this chromosomal region implicating the presence of trans-acting regulatory factor(s). We have recently mapped one such locus to the chromosome 6q region in a very large Asian-Indian pedigree. Linkage to chromosome 6q in the English pedigree was excluded, thus indicating the presence of genetic heterogeneity in heterocellular HPFH.

Genetic heterogeneity of residual variance of hatch weight in Mazandaran native chicken

ABSTRACT 1. In current breeding programmes, uniformity of end products and producing animals that are robust to environmental challenges are desirable. Several studies have provided evidence of the presence of genetic heterogeneity of residual variance and proposed that it could be possible to increase uniformity of livestock productions by selection. The present study aimed to define the micro environmental sensitivity of dual-purpose chickens for body weight at hatch. 2. The data set consisted of 24,321 female and 21,547 male chickens’ records of hatch weight from 19 consecutive generations of Mazandaran fowl. The statistical analysis was carried out in a two-step approach: first, an animal model was fitted to the data and then, the impact of additive genetic effects on the residual variance of the studied trait was investigated. 3. The estimate of heritability for body weight at hatch was in the range of 0.23–0.25 for female and 0.14–0.16 for male offspring, respectively. The proportion of maternal environmental variance to phenotypic variance ranged from 0.24 to 0.27 for female and 0.17 to 0.24 for male offspring. Heritabilities in females were higher than males. Estimates of the heritability of residual variance ranged between 0.067 and 0.090. The genetic coefficients of variation were high ranging between 0.83 and 0.86. Genetic correlations between hatch weight and its residual variance estimates from bivariate analysis were −0.39 and −0.44 in females and males, respectively. 4. The results suggest that there is an opportunity to simultaneously improve body weight and the uniformity of body weight by selecting for lower residual variance in native chickens.

Meta-Analysis of SNP-Environment Interaction with Heterogeneity

Meta-analyses are widely used in genome-wide association studies to combine the results obtained from multiple studies. Classical random-effects methods treat genetic heterogeneity as a random effect and consider it as a portion of the variance associated with a fixed effect of the variant. Recent work suggests performing hypothesis testing with the null hypothesis under which neither fixed nor random effects exist for a variant. This method has been shown to perform better than classical random-effects methods. In this work, we propose a meta-analysis of testing single nucleotide polymorphism (SNP)-environment interaction in the presence of genetic heterogeneity. We introduced the random effects of the SNP and SNP-environment interaction under test into a meta-regression model to account for heterogeneity. A test for the SNP-environment interaction was formulated to test for fixed and random effects of the interaction simultaneously. Similarly, a test for total genetic effects was formulated to test for fixed effects of the SNP and the SNP-environment interaction together with their random effects. We performed simulations to study the null distribution and statistical power of the proposed tests. We show that the new methods have higher power than classical random-effects and fixed-effects meta-regression methods when heterogeneity effects are large.

Abstract 27: Integrated analysis of targeted single-cell genetic and transcriptional heterogeneity suggests novel drivers of chronic lymphocytic leukemia

Abstract Intratumoral genetic heterogeneity has been characterized across cancers, including chronic lymphocytic leukemia (CLL), and presents challenges to therapy as presence of phenotypically diverse cell populations commonly fuels relapse and resistance to treatment. While insights have been previously gained from bulk analysis, further characterization on the single-cell level is needed to more accurately dissect the pathway and regulatory features associated with distinct genetic subclones. In order to more accurately identify subclones, define phylogenetic relationships, and probe genotype-phenotype relationships, we developed methods for targeted mutation detection in DNA and RNA isolated from thousands of single cells from five CLL samples. To precisely establish the subclonal architecture of the five CLL samples, we first applied targeted single-cell DNA sequencing for detection of mutation and copy number variation using CLL samples previously characterized by bulk whole exome sequencing (WES). Our results identified multiple genetically distinct subclones in four out of five CLL samples. Next, to understand the subclonal architecture and their associated gene expression pattern, we performed single-cell whole transcriptome sequencing (scRNA-seq) and pathway and geneset overdispersion analysis. Although scRNA-seq identified clear transcriptional heterogeneity, these data could neither be used to confidently resolve the genetic subclone structure nor assess the correspondence of genetic structure with the observed transcriptional heterogeneity due to sparseness of coverage at mutation sites of interest. Thus, we developed a targeted RNA approach to assess transcript expression profiles and mutational status in the same single cells. Our approach reliably detects subclonal mutations and enables recapitulation of single-cell DNA information, including phylogenetic structure. Integrative analysis to correlate genotype and phenotype reveals phenotypic convergence between distinct subclones and prompts the idea that convergent evolution generates phenotypically similar cells in distinct genetic branches, thus creating a cohesive expression profile in each CLL sample despite the presence of genetic heterogeneity. Specifically, by clearly resolving phylogenic relationships and simultaneously assessing DNA and RNA-level information from the same single cells, we uncovered mutated LCP1 and WNK1 as novel CLL drivers, supported by functional evidence demonstrating their impact on CLL pathways. Overall, we demonstrate the ability to robustly integrate DNA- and RNA-level information in order to dissect the impact of somatic mutations on cellular phenotype. Our study highlights the potential for single-cell RNA-based targeted analysis to sensitively determine transcriptional and mutational profiles of individual cancer cells leading to an increased understanding of driving events in malignancy. Citation Format: Jean Fan, Lili Wang, Joshua M. Francis, George Georghiou, Sarah Hergert, Shuqiang Li, Rutendo Gambe, Chensheng W. Zhou, Chunxiao Yang, Sheng Xiao, Paola Dal Chin, Michaela Bowden, Dylan Kotliar, Sachet A. Shukla, Jennifer R. Brown, Donna Neuberg, Dario R. Alessi, Cheng-Zhong Zhang, Peter V. Kharchenko, Kenneth J. Livak, Catherine J. Wu. Integrated analysis of targeted single-cell genetic and transcriptional heterogeneity suggests novel drivers of chronic lymphocytic leukemia [abstract]. In: Proceedings of the Second AACR Conference on Hematologic Malignancies: Translating Discoveries to Novel Therapies; May 6-9, 2017; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(24_Suppl):Abstract nr 27.

Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia.

Intra-tumoral genetic heterogeneity has been characterized across cancers by genome sequencing of bulk tumors, including chronic lymphocytic leukemia (CLL). In order to more accurately identify subclones, define phylogenetic relationships, and probe genotype–phenotype relationships, we developed methods for targeted mutation detection in DNA and RNA isolated from thousands of single cells from five CLL samples. By clearly resolving phylogenic relationships, we uncovered mutated LCP1 and WNK1 as novel CLL drivers, supported by functional evidence demonstrating their impact on CLL pathways. Integrative analysis of somatic mutations with transcriptional states prompts the idea that convergent evolution generates phenotypically similar cells in distinct genetic branches, thus creating a cohesive expression profile in each CLL sample despite the presence of genetic heterogeneity. Our study highlights the potential for single-cell RNA-based targeted analysis to sensitively determine transcriptional and mutational profiles of individual cancer cells, leading to increased understanding of driving events in malignancy.

Genetic and Biological Characterization of Four Nucleopolyhedrovirus Isolates Collected in Mexico for the Control of Spodoptera exigua (Lepidoptera: Noctuidae).

This study describes four multiple nucleocapsid nucleopolyhedrovirus isolates recovered from infected larvae of beet armyworm, Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae), on crops in two different geographical regions of Mexico. Molecular and biological characterization was compared with characterized S. exigua multiple nucleopolyhedrovirus (SeMNPV) isolates from the United States (SeUS1 and SeUS2) and Spain (SeSP2). Restriction endonuclease analysis of viral DNA confirmed that all Mexican isolates were SeMNPV isolates, but molecular differences between the Mexican and the reference isolates were detected using PCR combined with restriction fragment length polymorphism (RFLP). Amplification of the variable region V01 combined with RFLP distinguished the two Mexican isolates, SeSLP6 and SeSIN6. BglII digestions showed that the majority of the isolates contained submolar bands, indicating the presence of genetic heterogeneity. Amplification of the variable regions V04 and V05 distinguished between American and the Spanish isolates. Biological characterization was performed against two laboratory colonies of S. exigua, one from Mexico, and another from Switzerland. Insects from the Mexican colony were less susceptible to infection than insects from Se-Swiss colony. In the Se-Mex colony, SeSP2 was the most pathogenic isolate followed by SeSIN6, although their virulence was similar to most of the isolates tested. In Se-Swiss colony, similar LD50 values were observed for the five isolates, although the virulence was higher for the SeSLP6 isolate, which also had the highest OB (occlusion body) yield. We conclude that the Mexican isolates SeSIN6 and SeSLP6 possess insecticidal traits of value for the development of biopesticides for the control of populations of S. exigua.

Sequencing Circulating Cell-Free DNA: The Potential to Refine Precision Cancer Medicine.

Most cancer treatments benefit only a minority of the patients who receive them. This is particularly true for molecularly targeted drugs, unless we know which patients to target. The completion of the first human reference genome in 2003 and the introduction of massively parallel sequencing (MPS)2 technology in 2004 ushered in the “genomic era” of cancer research and triggered the concept of precision cancer medicine, in which treatment is tailored to the genetic make-up of an individual's tumor. Since then, efforts from large-scale cancer genome sequencing projects, such as The Cancer Genome Atlas, have led to the discovery of many important cancer genes [for example, isocitrate dehydrogenase 1 ( IDH 1)3 and isocitrate dehydrogenase 2 ( IDH2 ) for glioblastoma multiforme and acute myeloid leukemia, respectively] as well as new targets for therapy. Today, affordable targeted MPS-based assays have become commonplace in the clinical research setting, particularly in cancer centers with early-phase drug development programs, as molecular screening tools to triage patients for molecularly targeted therapies. Molecular testing is traditionally conducted with tumor tissue derived from a diagnostic biopsy or an archival resection sample. However, the existence of intratumoral, intermetastatic, and intrametastatic genetic heterogeneity implies that the genomic analysis of a single tissue sample may represent an incomplete view of the genetic landscape of the tumor. The presence of genetic heterogeneity is particularly relevant in patients with heavily pretreated advanced cancers. In such cases, the current molecular profile of the disease may be substantially different from that of the archival tissue obtained several years prior. Recent studies evaluating the clonal relationships among primary and metastatic cancers have demonstrated that seeding metastases require very few additional driver or founder mutations beyond those found in the primary tumors. However, as the metastatic lesion grows, it acquires new mutations with each cell division, providing …

Abstract 3240: Understanding clonal complexity of a tumor xenograft model via cellular barcoding technology

Abstract Recent advances in next-generation sequencing have revealed the presence of genetic heterogeneity and clonal evolution within tumors. Intratumoral heterogeneity has been implicated in the disease progression, metastasis, therapeutic responses and development of drug resistance. Although cancer cell line xenograft models have been extensively used in cancer research to test drug efficacy, it is unknown how much clonal heterogeneity is maintained in the process of cell line xenograft establishment. In order to quantitatively assess the clonal complexity in xenograft models, here we applied a cellular barcoding technology using the HCC827 cell line. HCC827 is a non-small cell lung cancer cell line containing an exon 19 deletion which has been shown to be clinically relevant due to its initial response to EGFR inhibitors followed by resistance. This barcoding tool allowed us to label each individual cell with one unique DNA barcode via lentiviral infection and monitor the clonal heterogeneity within the implanted cell population by quantifying the number of unique barcodes. We were able to estimate the percentage of implanted clones that actually contributed to the formation of cell line xenografts. This study provides valuable insight on the clonal diversity present in xenograft models, which will further elucidate the heterogeneous nature of tumors. Citation Format: Justina X. Caushi, Hyo-eun C. Bhang, Jie Li, Iris Kao, Viveksagar Krishnamurthy Radhakrishna, Vesselina G. Cooke, Joshua M. Korn, David A. Ruddy, Shailaja Kasibhatla, Frank Stegmeier. Understanding clonal complexity of a tumor xenograft model via cellular barcoding technology. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3240. doi:10.1158/1538-7445.AM2015-3240

Targeted therapies in cancer and mechanisms of resistance

Targeted therapies by means of compounds that inhibit a specific target molecule represent a new perspective in the treatment of cancer. In contrast to conventional chemotherapy, which acts on all dividing cells, targeted drugs allow to hit subpopulations of cells directly involved in tumor progression in a more specific manner [1]. The frequent alteration of receptor tyrosine kinases (RTK) in human malignancies led them to be considered as targets for anti-neoplastic therapies; this resulted in the development of several inhibitors that have shown a strong clinical activity [2]. The concept of “oncogene addiction” has added further rationale to the use of targeted therapies [3]. Even if the recent introduction in cancer therapy of several selective tyrosine kinase inhibitors has had a dramatic effect in oncology, after the first excitement following the initial results, the clinicians had to face soon the problem of primary and secondary resistance to treatment: a percentage of patients expressing the target in their tumors does not respond to the treatment (primary or “de novo” resistance), while in most of the responders, the treatment is quite rapidly no longer effective (secondary or “acquired” resistance) (Fig. 1) [4]. Therefore currently, the main challenges associated to targeted therapies are the relatively small proportion of patients that can benefit from these treatments and the almost inevitable occurrence of resistance. Thus, the identification of predictive biomarkers of resistance and the understanding of the resistance mechanisms are mandatory to improve the efficacy of these therapies. Genetic studies have recently highlighted the high level of heterogeneity in tumors, where many clones with different genetic features coexist. Cancers, in fact, evolve through a series of waves of clonal growth where single clones acquire genetic alterations conferring differential fitness [5]. Moreover, environmental factors (such as secretion of cytokines by the stromal cells or the availability of nutrients and oxygen) can favor the onset and permanence of such different cell populations. In this scenario, exposing a tumor to a targeted therapy can abruptly subvert the equilibrium that allows the coexistence of these cell populations. A paradigmatic example is represented by lung cancers displaying activating epidermal growth factor receptor (EGFR) mutations that render them responsive to EGFR inhibitors. Treatment with EGFR inhibitors, in fact, results in massive death of EGFR-addicted cancer cells, thus profoundly impacting on the overall architecture of the tumor. Cells that are not responsive to the inhibitor (such as those harboring the “resistant” EGFR mutation T790M) preexist inside the tumor and can outgrow under the selective pressure imposed by the drug [6, 7]. So, a cell population present in the primary cancer at low frequency due to a decreased fitness compared to cells devoid of the mutation becomes enriched over time and can reconstitute the tumor, leading to relapse (Fig. 1). Thus, from the beginning, the tumor contains cells harboring genetic lesions that can sustain resistance to targeted therapies. As mentioned by Dr. Vogelstein in a recent paper, “resistance is a fait accompli and the time to recurrence is simply the interval required for the subclone to repopulate the lesion” [8]. The presence of genetic heterogeneity in the primary tumor may also explain the common finding of patients presenting, at relapse, different metastases, each bearing a diverse genetic alteration responsible for drug resistance. The knowledge of the existence in primary tumors of cell subpopulations harboring genetic alterations leading to drug resistance has induced researchers and clinicians to test the efficacy of combination therapies in S. Corso : S. Giordano (*) Department of Oncology, University of Torino, Strada Provinciale 142, Candiolo, Torino 10060, Italy e-mail: silvia.giordano@ircc.it

Using Linkage Analysis to Detect Gene-Gene Interaction by Stratifying Family Data on Known Disease, or Disease-Associated, Alleles

Detecting gene-gene interaction in complex diseases is a major challenge for common disease genetics. Most interaction detection approaches use disease-marker associations and such methods have low power and unknown reliability in real data. We developed and tested a powerful linkage-analysis-based gene-gene interaction detection strategy based on conditioning the family data on a known disease-causing allele or disease-associated marker allele. We computer-generated multipoint linkage data for a disease caused by two epistatically interacting loci (A and B). We examined several two-locus epistatic inheritance models: dominant-dominant, dominant-recessive, recessive-dominant, recessive-recessive. At one of the loci (A), there was a known disease-related allele. We stratified the family data on the presence of this allele, eliminating family members who were without it. This elimination step has the effect of raising the “penetrance” at the second locus (B). We then calculated the lod score at the second locus (B) and compared the pre- and post-stratification lod scores at B. A positive difference indicated interaction. We also examined if it was possible to detect interaction with locus B based on a disease-marker association (instead of an identified disease allele) at locus A. We also tested whether the presence of genetic heterogeneity would generate false positive evidence of interaction. The power to detect interaction for a known disease allele was 60–90%. The probability of false positives, based on heterogeneity, was low. Decreasing linkage disequilibrium between the disease and marker at locus A decreased the likelihood of detecting interaction. The allele frequency of the associated marker made little difference to the power.

IL36RN Mutations in Generalized Pustular Psoriasis: Just the Tip of the Iceberg?

As IL36RN mutations are a cause of generalized pustular psoriasis (GPP), three recent investigations attempted to correlate the IL36RN genotype with GPP clinical presentations. These studies found that IL36RN mutations account for only a fraction of GPP cases presenting with concomitant psoriasis vulgaris (PV; common or typical psoriasis). Pathogenic alleles were also found in control populations, indicating that environmental triggers and/or modifier genes may contribute to the disease.

High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma

Although the presence of genetic heterogeneity within the tumors of individual patients is established, it is unclear whether greater heterogeneity predicts a worse outcome. A quantitative measure of genetic heterogeneity based on next-generation sequencing (NGS) data, mutant-allele tumor heterogeneity (MATH), was previously developed and applied to a data set on head and neck squamous cell carcinoma (HNSCC). Whether this measure correlates with clinical outcome was not previously assessed. The authors examined the association between MATH and clinical, pathologic, and overall survival data for 74 patients with HNSCC for whom exome sequencing was completed. High MATH (a MATH value above the median) was found to be significantly associated with shorter overall survival (hazards ratio, 2.5; 95% confidence interval, 1.3-4.8). MATH was similarly found to be associated with adverse outcomes in clinically high-risk patients with an advanced stage of disease, and in those with tumors classified as high risk on the basis of validated biomarkers including those that were negative for human papillomavirus or having disruptive tumor protein p53 mutations. In patients who received chemotherapy, the hazards ratio for high MATH was 4.1 (95% confidence interval, 1.6-10.2). This novel measure of tumor genetic heterogeneity is significantly associated with tumor progression and adverse treatment outcomes, thereby supporting the hypothesis that higher genetic heterogeneity portends a worse clinical outcome in patients with HNSCC. The prognostic value of some known biomarkers may be the result of their association with high genetic heterogeneity. MATH provides a useful measure of that heterogeneity to be prospectively validated as NGS data from homogeneously treated patient cohorts become available.

An omnibus permutation test on ensembles of two-locus analyses can detect pure epistasis and genetic heterogeneity in genome-wide association studies.

This article presents the ability of an omnibus permutation test on ensembles of two-locus analyses (2LOmb) to detect pure epistasis in the presence of genetic heterogeneity. The performance of 2LOmb is evaluated in various simulation scenarios covering two independent causes of complex disease where each cause is governed by a purely epistatic interaction. Different scenarios are set up by varying the number of available single nucleotide polymorphisms (SNPs) in data, number of causative SNPs and ratio of case samples from two affected groups. The simulation results indicate that 2LOmb outperforms multifactor dimensionality reduction (MDR) and random forest (RF) techniques in terms of a low number of output SNPs and a high number of correctly-identified causative SNPs. Moreover, 2LOmb is capable of identifying the number of independent interactions in tractable computational time and can be used in genome-wide association studies. 2LOmb is subsequently applied to a type 1 diabetes mellitus (T1D) data set, which is collected from a UK population by the Wellcome Trust Case Control Consortium (WTCCC). After screening for SNPs that locate within or near genes and exhibit no marginal single-locus effects, the T1D data set is reduced to 95,991 SNPs from 12,146 genes. The 2LOmb search in the reduced T1D data set reveals that 12 SNPs, which can be divided into two independent sets, are associated with the disease. The first SNP set consists of three SNPs from MUC21 (mucin 21, cell surface associated), three SNPs from MUC22 (mucin 22), two SNPs from PSORS1C1 (psoriasis susceptibility 1 candidate 1) and one SNP from TCF19 (transcription factor 19). A four-locus interaction between these four genes is also detected. The second SNP set consists of three SNPs from ATAD1 (ATPase family, AAA domain containing 1). Overall, the findings indicate the detection of pure epistasis in the presence of genetic heterogeneity and provide an alternative explanation for the aetiology of T1D in the UK population.

Genetic heterogeneity in HER2 testing may influence therapy eligibility

Prospective studies have demonstrated that approximately 20% of HER2 testing may be inaccurate. When carefully validated testing is conducted, available data do not clearly demonstrate the superiority of either IHC or fluorescence in situ hybridization (FISH) as a predictor of benefit from anti-HER2 therapy. In addition, the interpretation of the findings of HER2 tests according to international guidelines is not uniform. The American Society of Clinical Oncology (ASCO) and the College of American Pathologists (CAP) recently published practice guidelines for a definition of HER2 amplification heterogeneity that can give rise to discrepant results between IHC and FISH assays for HER2. In this article, we compare the HER2 status of 291 non consecutive breast cancers. The status is determined by both IHC and FISH approaches, using a specific FISH strategy to investigate genetic heterogeneity. Our data demonstrate that HER2 amplified cells may be found as diffuse, clustered in a specific area or section, intermingled with non-amplified cells or confined to metastatic nodules. The correct evaluation of ratio value in the presence of genetic heterogeneity and of polysomy contributes to the accurate assessment of HER2 status and potentially affects the selection of appropriate anti-HER2 therapy. By taking into account the presence of different genetic cell populations, the immunotherapy eligibility criteria for HER2 FISH scoring proposed in the CAP (2009) and SIGU guidelines identify an additional subset of cases for trastuzumab or lapatinib therapy compared to the ASCO/CAP (2007) guidelines.

Model-Based Multifactor Dimensionality Reduction for detecting epistasis in case-control data in the presence of noise

Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies.

Comparison of information-theoretic to statistical methods for gene-gene interactions in the presence of genetic heterogeneity

BackgroundMultifactorial diseases such as cancer and cardiovascular diseases are caused by the complex interplay between genes and environment. The detection of these interactions remains challenging due to computational limitations. Information theoretic approaches use computationally efficient directed search strategies and thus provide a feasible solution to this problem. However, the power of information theoretic methods for interaction analysis has not been systematically evaluated. In this work, we compare power and Type I error of an information-theoretic approach to existing interaction analysis methods.MethodsThe k-way interaction information (KWII) metric for identifying variable combinations involved in gene-gene interactions (GGI) was assessed using several simulated data sets under models of genetic heterogeneity driven by susceptibility increasing loci with varying allele frequency, penetrance values and heritability. The power and proportion of false positives of the KWII was compared to multifactor dimensionality reduction (MDR), restricted partitioning method (RPM) and logistic regression.ResultsThe power of the KWII was considerably greater than MDR on all six simulation models examined. For a given disease prevalence at high values of heritability, the power of both RPM and KWII was greater than 95%. For models with low heritability and/or genetic heterogeneity, the power of the KWII was consistently greater than RPM; the improvements in power for the KWII over RPM ranged from 4.7% to 14.2% at for α = 0.001 in the three models at the lowest heritability values examined. KWII performed similar to logistic regression.ConclusionsInformation theoretic models are flexible and have excellent power to detect GGI under a variety of conditions that characterize complex diseases.

Selection of the most informative individuals from families with multiple siblings for association studies

Association analyses may follow an initial linkage analysis for mapping and identifying genes underlying complex quantitative traits and may be conducted on unrelated subsets of individuals where only one member of a family is included. We evaluate two methods to select one sibling per sibship when multiple siblings are available: (1) one sibling with the most extreme trait value; and (2) one sibling using a combination score statistic based on extreme trait values and identity-by-descent sharing information. We compare the type I error and power. Furthermore, we compare these selection strategies with a strategy that randomly selects one sibling per sibship and with an approach that includes all siblings, using both simulation study and an application to fasting blood glucose in the Framingham Heart Study. When genetic effect is homogeneous, we find that using the combination score can increase power by 30-40% compared to a random selection strategy, and loses only 8-13% of power compared to the full sibship analysis, across all additive models considered, but offers at least 50% genotyping cost saving. In the presence of genetic heterogeneity, the score offers a 50% increase in power over a random selection strategy, but there is substantial loss compared to the full sibship analysis. In application to fasting blood sample, two SNPs are found in common for the selection strategies and the full sample among the 10 highest ranked single nucleotide polymorphisms. The EV strategy tends to agree with the IBD-EV strategy and the analysis of the full sample.

Ordered‐subset analysis (OSA) for family‐based association mapping of complex traits

Association analysis provides a powerful tool for complex disease gene mapping. However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered-subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait-related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family-based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL-OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL-OSA statistic under the null hypothesis that there is no relationship between the family-specific covariate and the family-specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL-OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL-OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL-OSA to a family study of age-related macular degeneration, where cigarette smoking was used as a covariate.