Two‐sample Comparison Based on Prediction Error, with Applications to Candidate Gene Association Studies

The transmission disequilibrium test (TDT) based on case-parents trios is a powerful tool in linkage analysis and association studies. When only one parent is available, the 1-TDT is applicable in the absence of imprinting. In the presence of imprinting, a statistic is proposed, based on case-mother pairs and case-father pairs to test for linkage when association is present as well as to test for association when linkage is present. The recombination fractions are allowed to be sex-specific in this test statistic. Meanwhile, a statistic based on case-parent pairs is proposed to test for imprinting. Both test statistics can be extended to include families with more than one affected offspring. A number of simulation studies are conducted to investigate the validity of the proposed tests. The effects of different ratios of the numbers of case-mother pairs and case-father pairs on the powers of the proposed tests are studied through simulation. The results show that the optimal ratio is 1:1. How to combine case-parents, case-mother pairs, and case-father pairs jointly in testing for linkage, association, and imprinting is addressed.

Complex etiologic heterogeneity among non-Hodgkin's lymphoma (NHL) subtypes has made it difficult to identify risk factors for this disease. However, recent genetic association analyses, including two genome-wide association studies, suggest that genetic susceptibility to NHL may be strongest for the follicular lymphoma subtype. As recent studies have demonstrated that microRNAs (miRNAs) may be important in the etiology of many cancer types, including NHL, we performed a population-based genetic association study which identified a polymorphism in the precursor of miR-202 that is strongly associated with follicular lymphoma risk (rs12355840 in miR-202: PTREND = 0.006). An in vitro functional genetics assay demonstrated that the polymorphism is biologically active, and operates by altering the processing efficiency of the miR-202 precursor to the mature effector miRNA species. A ribonucleoprotein immunoprecipitation-microarray (RIP-Chip) assay, performed with an anti-AGO2 antibody followed by a whole-genome array, was used to characterize the miR-202 targetome, and selected targets were confirmed using a 3′UTR luciferase assay. Among the identified targets were several transcripts with relevance for tumorigenesis, including the miRNA biogenesis gene DICER1 and the lymphoma-associated cell cycle regulator and proto-oncogene SKP2. Both of these genes have been shown to be upregulated in lymphomas, which is consistent with our data, which show that the miR-202 risk allele is associated with diminished mature miR-202 levels in vitro. Taken together, these findings suggest that rs12355840 may represent a novel risk biomarker for follicular lymphoma, and implicate miR-202 in follicular lymphomagenesis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1652. doi:1538-7445.AM2012-1652

Getting more grip on genetic risk prediction studies: the GRIPS statement

BackgroundClassical genetic studies provide strong evidence for heritable contributions to susceptibility to developing dependence on addictive substances. Candidate gene and genome-wide association studies (GWAS) have sought genes, chromosomal regions and allelic variants likely to contribute to susceptibility to drug addiction.ResultsHere, we performed a meta-analysis of addiction candidate gene association studies and GWAS to investigate possible functional mechanisms associated with addiction susceptibility. From meta-data retrieved from 212 publications on candidate gene association studies and 5 GWAS reports, we linked a total of 843 haplotypes to addiction susceptibility. We mapped the SNPs in these haplotypes to functional and regulatory elements in the genome and estimated the magnitude of the contributions of different molecular mechanisms to their effects on addiction susceptibility. In addition to SNPs in coding regions, these data suggest that haplotypes in gene regulatory regions may also contribute to addiction susceptibility. When we compared the lists of genes identified by association studies and those identified by molecular biological studies of drug-regulated genes, we observed significantly higher participation in the same gene interaction networks than expected by chance, despite little overlap between the two gene lists.ConclusionsThese results appear to offer new insights into the genetic factors underlying drug addiction.

A family-based study design is commonly used in gene mapping studies of complex human diseases. Most family-based studies use the transmission of alleles to assess evidence of association. It is generally believed that the transmission disequilibrium test (TDT) is robust against spurious association due to population stratification or admixture. While this is true when population stratification is due to discrete population structure, one should use the TDT-type methods with caution when they are applied to admixed populations in which population structure exists in local genomic regions. In a recently admixed population, such as African Americans and Hispanic Americans, the linkage disequilibrium coefficient between a marker and disease loci in the parental generation contains a spurious component from the admixture process. In this paper, we show that the general belief that family-based design would guard against spurious association caused by population stratification does not always hold in admixed populations. It is safe to use the TDT as a test of association when population stratification is due to global genome ancestry difference. However, when population stratification is due to local ancestry difference in certain genomic regions, the use of the TDT as a test of association can lead to spurious association. Second, we present a statistical framework for fine mapping of disease-associated genetic variants in admixed families. Unlike the TDT and other family-based association tests, this method does not rely on transmission disequilibrium and therefore can control local ancestry difference between transmitted and untransmitted alleles. Through simulations, we show that this method can control type I error rates under a wide range of population stratification mechanisms.

When studying the genetics of inherited diseases, researchers often collect data on affected families, unrelated cases, and healthy controls. However, the joint analysis of such heterogeneous data is difficult, and the simpler analysis of homogeneous subsets is often suboptimal. For example, while case-control tests of association are sensitive to allele frequency differences, the preferential transmission of risk alleles from heterozygous parents to their affected offspring is typically ignored. Similarly, the transmission disequilibrium test (TDT) fails to incorporate the difference in allele frequencies when testing for association. To boost the power of modern genetic studies, we propose POPFAM – a fast and efficient test of association that can accommodate large affected families, unrelated cases, and controls. We use simulations to assess the type I error and power of POPFAM across different genetic models, and minor allele frequencies. For comparison, we examine the power of competing methods: the trend test, a Wald test (equivalent to the TDT), and SCOUT. Our results show that POPFAM maintains the correct type I error, and that it is more powerful than the trend test or the TDT. It performs as well as, or better than the likelihood ratio test SCOUT, which was developed specifically for case-parent/case-control data. Furthermore, when applied to the human leukocyte antigen genotypes of 401 type 1 diabetic families, POPFAM confirmed the previously reported association between DRB1*03:01 and microvascular complications (p = 0.04). In general, we expect our proposed test to facilitate the identification of clinically important genomic regions, and to better inform the design of follow-up sequencing efforts.

Exploiting Excess Sharing: A More Powerful Test of Linkage for Affected Sib Pairs than the Transmission/Disequilibrium Test

Case-control genetic association studies in admixed populations are known to be susceptible to genetic confounding due to population stratification. The transmission/disequilibrium test (TDT) approach can avoid this problem. However, the TDT is expensive and impractical for late-onset diseases. Case-control study designs, in which, cases and controls are matched by admixture, can be an appealing and a suitable alternative for genetic association studies in admixed populations. In this study, we applied this matching strategy when recruiting our African American participants in the Study of African American, Asthma, Genes and Environments. Group admixture in this cohort consists of 83% African ancestry and 17% European ancestry, which was consistent with reports from other studies. By carrying out several complementary analyses, our results show that there is a substructure in the cohort, but that the admixture distributions are almost identical in cases and controls, and also in cases only. We performed association tests for asthma-related traits with ancestry, and only found that FEV(1), a measure for baseline pulmonary function, was associated with ancestry after adjusting for socio-economic and environmental risk factors (P=0.01). We did not observe an excess of type I error rate in our association tests for ancestry informative markers and asthma-related phenotypes when ancestry was not adjusted in the analyses. Furthermore, using the association tests between genetic variants in a known asthma candidate gene, beta(2) adrenergic receptor (beta(2)AR) and DeltaFEF(25-75), an asthma-related phenotype, as an example, we demonstrated population stratification was not a confounder in our genetic association. Our present work demonstrates that admixture-matched case-control strategies can efficiently control population stratification confounding in admixed populations.

Many family-based association tests rely on the random transmission of alleles from parents to offspring. Among them, the transmission/disequilibrium test (TDT) may be considered to be the most popular statistical test. The TDT statistic and its variations were proposed to evaluate nonrandom transmission of alleles from parents to the diseased children. However, in family studies, parental genotypes may be missing due to parental death, loss, divorce, or other reasons. Under some missingness conditions, nonrandom transmission of alleles may still occur even when the gene and disease are not associated. As a consequence, the usual TDT-type tests would produce excessive false positive conclusions in association studies. In this paper, we propose a novel TDT-type association test which is not only simple in computation but also robust to the joint effect of population stratification and informative parental missingness. Our test is model-free and allows for different mechanisms of parental missingness across subpopulations. We use a simulation study to compare the performance of the new test with TDT and point out the advantage of the new method.

Tests for Linkage and Association in Nuclear Families

Detecting genetic interactions associated with complex disease is a major issue in genetic studies. Although a number of methods to detect gene-gene interactions for population-based Genome-Wide Association Studies (GWAS) have been developed, the statistical methods for family-based GWAS have been limited. In this study, we propose a new Bayesian approach called MB-TDT to find high-order genetic interactions for pedigree data. The MB-TDT method combines the Markov blanket algorithm with classical Transmission Disequilibrium Test (TDT) statistic. The Incremental Association Markov Blanket (IAMB) algorithm was adopted for large-scale Markov blanket discovery. We evaluated the proposed method using both real and simulated data sets. In a simulation study, we compared the power of MB-TDT with conditional logistic regression, Multifactor Dimensionality Reduction (MDR) and MDR-pedigree disequilibrium test (MDR-PDT). We demonstrated the superior power of MB-TDT in many cases. To demonstrate the approach, we analysed the Korean autism disorder GWAS data. The MB-TDT method can identify a minimal set of causal SNPs associated with a specific disease, thus avoiding an exhaustive search.

Until about a decade ago, genetic association testing essentially meant case control association analysis using genetic markers. Concerns about population stratification propelled family-based tests of association into widespread use and challenged the classic case control design. The literature now contains a vast collection of different family-based methods, most of which are based on the transmission/disequilibrium test (TDT). Some methods extend the original TDT to accommodate multiallelic markers, variable pedigree constellations, multiple loci, etc. Other methods go beyond the original design of the TDT to detect genetic association via haplotype sharing. Most recently, we have witnessed a revival of case control methods that control for population stratification. The purpose of this review is to help orient readers to the rapidly developing methods of association testing and enhance their understanding of the basic principles of these approaches. We present an overview of the development of genetic association tests, with practical guidelines on which test might be the most suitable for a given study.

Population stratification is a form of confounding by ethnicity that may cause bias to effect estimates and inflate test statistics in genetic association studies. Unlinked genetic markers have been used to adjust for test statistics, but their use in correcting biased effect estimates has not been addressed. We evaluated the potential of bias correction that could be achieved by a single null marker (M) in studies involving one candidate gene (G). When the distribution of M varied greatly across ethnicities, controlling for M in a logistic regression model substantially reduced biases on odds ratio estimates. When M had same distributions as G across ethnicities, biases were further reduced or eliminated by subtracting the regression coefficient of M from the coefficient of G in the model, which was fitted either with or without a multiplicative interaction term between M and G. Correction of bias due to population stratification depended specifically on the distributions of G and M, the difference between baseline disease risks across ethnicities, and whether G had an effect on disease risk or not. Our results suggested that marker choice and the specific treatment of that marker in analysis greatly influenced bias correction.

Introduction: With over 80, 000 new cases of non-Hodgkin's lymphoma (NHL) diagnosed annually, modifiable risk factors remain unclear. Circadian disruption, linked to cancers like breast and prostate, affects immune cells (e.g., natural killer cells and T-helper cells) by altering their trafficking and proliferation. Some studies (PMID: 30566672, 27611440) suggest long sleep may increase overall NHL risk, including a Mendelian randomization (MR) study (PMID: 32895918). However, no research has examined the causal relationship between sleep traits, including long sleep, and NHL risk, by subtypes. We hypothesize that sleep traits are causally associated with NHL subtypes in European populations, testing this using MR with data from the International Lymphoma Epidemiology Consortium (InterLymph). Methods: We conducted two-sample MR using genome-wide association studies (GWAS) summary data for sleep traits and NHL subtypes. Only independent, genome-wide significant (p < 5×10-8) Single Nucleotide Polymorphisms (SNPs) were selected as valid instruments, sourced from the UK Biobank. Sleep traits included chronotype (153 SNPs), insomnia (48 SNPs), sleep duration (78 SNPs), excessive daytime sleepiness (37 SNPs), short sleep (27 SNPs), and long sleep (8 SNPs). NHL subtype data were from InterLymph: follicular lymphoma (6, 508 cases / 64, 183 controls), mantle cell lymphoma (1, 169 cases / 61, 603 controls), Waldenstrom macroglobulinemia/lymphoplasmacytic lymphoma (WM/LPL: 1, 697 cases / 59, 333 controls), and chronic lymphocytic leukemia (8, 522 cases / 67, 653 controls). The primary analysis used inverse-variance weighted (IVW) random-effects, with MR-Egger for pleiotropy adjustment. Results: Using the IVW method, we did not identify any statistically significant causal association between sleep traits and the risk of NHL subtypes, but did yield several intriguing trends. Long sleep (sleep duration >= 9 h per night) showed a marginally increased NHL risk [Odds ratio (OR) range: 1.51 - 247.9] across each subtype. Short sleep (sleep duration < 7 h per night) showed a similar trend [OR range: 1.25 - 1.81], except for WM/LPL, where we noticed a negative trend. The confidence limit for long sleep was imprecise due to the small number of variants. We did not notice any significant directional horizontal pleiotropy or weak instrument bias. Conclusions: We found no conclusive evidence of a relationship between sleep traits and four NHL subtypes. However, we see suggestive trends of long and short sleep associated with higher risk of some NHL subtypes. These findings align with the previous studies, which showed long sleep was associated with higher risk of NHL overall. Future directions include expanding our analysis to additional NHL subtypes, generating polygenic risk scores, and conducting stratified analyses. Citation Format: Pankhil Shah, Brittany Crawford, Anwar Merchant, Brenda Birmann, Angelica Macauda, Michelle A. Hildebrandt, Aaron Norman, Neil E. Caporaso, Meredith Yeager, Michael Dean, Immaculata De Vivo, Lynn Goldin, Nicola J. Camp, Rosalie Griffin, Delphine Casabonne, Federico Canzian, Pelin Unal, Elad Ziv, Catherine R. Marinac, Alexandra Nieters, Stephen J. Chanock, Mitchell J. Machiela, Michael Conry, Charlie Zhong, Hanla A. Park, Simon Cheah, Jonathan N. Hofmann, Elizabeth E. Brown, Celine Vachon, Susan Slager, Sonja Berndt, Sophia S. Wang, Vijai Joseph, James McKay, Henrik Hjalgrim, Lara Sucheston-Campbell, Karl Smith-Byrne, Alyssa Clay-Gilmour. Association between sleep traits and risk of non-Hodgkin's lymphoma subtypes: a mendelian randomization study in the International Lymphoma Epidemiology (InterLymph) Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7415.

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 χ² procedure (T_CHI), the regression procedure (Hotelling T² 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.