Single Nucleotide Polymorphisms Pairs Research Articles

Improving genomic prediction of rhizomania resistance in sugar beet (Beta vulgaris L.) by implementing epistatic effects and feature selection

Background Rhizomania counts as the most important disease in sugar beet Beta vulgaris L. for which no plant protection is available, leaving plant breeding as the only defence strategy at the moment. Five resistance genes have been detected on the same chromosome and further studies suggested that these might be different alleles at two resistance clusters. Nevertheless, it was postulated that rhizomania resistance might be a quantitative trait with multiple unknown minor resistance genes. Here, we present a first attempt at genomic prediction of rhizomania resistance in a population that carries resistances at the two known resistance clusters. The sugar beet population was genotyped using single nucleotide polymorphism (SNP) markers. Methods First, genomic prediction was performed using all SNPs. Next, we calculated the variable importance for each SNP using machine learning and performed genomic prediction by including the SNPs incrementally in the prediction model based on their variable importance. Using this method, we selected the optimal number of SNPs that maximised the prediction accuracy. Furthermore, we performed genomic prediction with SNP pairs. We also performed feature selection with SNP pairs using the information about the variable importance of the single SNPs. Results From the four methods under investigation, the latter led to the highest prediction accuracy. These results lead to the conclusion that more than the two known resistance clusters are involved in rhizomania resistance and that genetic interactions affect rhizomania resistance. Finally, we have analysed which SNPs were repeatedly detected in the feature selection process and discovered four SNPs, two of which are located on chromosomes that were previously not associated with rhizomania resistance.

Network medicine-based epistasis detection in complex diseases: ready for quantum computing.

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs)(1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

Structural variants in linkage disequilibrium with GWAS-significant SNPs

With the recent expansion of structural variant identification in the human genome, understanding the role of these impactful variants in disease architecture is critically important. Currently, a large proportion of genome-wide-significant genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) are functionally unresolved, raising the possibility that some of these SNPs are associated with disease through linkage disequilibrium with causal structural variants. Hence, understanding the linkage disequilibrium between newly discovered structural variants and statistically significant SNPs may provide a resource for further investigation into disease-associated regions in the genome. Here we present a resource cataloging structural variant-significant SNP pairs in high linkage disequilibrium. The database is composed of (i) SNPs that have exhibited genome-wide significant association with traits, primarily disease phenotypes, (ii) newly released structural variants (SVs), and (iii) linkage disequilibrium values calculated from unphased data. All data files including those detailing SV and GWAS SNP associations and results of GWAS-SNP-SV pairs are available at the SV-SNP LD Database and can be accessed at ‵https://github.com/hliang-SchrodiLab/SV_SNPs. Our analysis results represent a useful fine mapping tool for interrogating SVs in linkage disequilibrium with disease-associated SNPs. We anticipate that this resource may play an important role in subsequent studies which investigate incorporating disease causing SVs into disease risk prediction models.

Genome-wide analysis with additive and epistasis effects reveals growth-related loci and candidate genes in hybrid Argopecten scallops

The first filial (F1) generation from the successful hybridization of bay scallops (Argopecten irradians) and Peruvian scallops (A. purpuratus) exhibits significant heterosis, playing a crucial role in enhancing the germplasm and increasing yields of the scallops. However, there is significant variation in the growth traits of the F1 hybrid population, and there is little information concerning its hereditary basis. To unravel the heterosis mechanism and identify key loci and genes contributing to superhybrids, genome-wide association study (GWAS) with additive and epistasis models were conducted for six growth-related traits in the F1 hybrid scallop population in this study. In total, 18,789,547 single nucleotide polymorphisms (SNPs) were identified after filtering from 198 hybrid individuals, comprising 63.5% of maternal loci and 36.5% of paternal loci. The additive model revealed 2768 associated SNPs and 14 candidate genes, with most of the loci associated with the body mass traits. The additive-additive epistatic model identified 133,094 SNP interaction pairs and 20 candidate gene pairs with exon region SNPs, which were predominantly associated with shell morphology traits. Almost all the SNP pairs associated with body mass traits were maternal-maternal interactions, but the shell morphology traits had the most maternal-paternal and maternal-maternal interactions, suggesting that body mass traits might be affected mainly by maternal effects and that parental interaction effects mainly contributed to the shell size of the hybrid scallops. Both in the shell morphology and the body mass traits, multiple loci participate in epistasis, interacting with more than one locus and forming high-order epistatic interactions, which could be critical factors contributing to the heterosis observed in hybrid scallops. The identified associated loci and candidate genes not only constitute valuable resources for the molecular breeding of hybrid scallops, contributing to the improvement of growth-related traits, but also revealed mechanisms underlying heterosis in aquatic species, providing insights into the advantages of heterosis and paving the way for future research on hybridization in aquatic organisms.

Drug resistance markers in Plasmodium vivax isolates from a Kanchanaburi province, Thailand between January to May 2023.

Plasmodium vivax has become the predominant species in the border regions of Thailand. The emergence and spread of antimalarial drug resistance in P. vivax is one of the significant challenges for malaria control. Continuous surveillance of drug resistance is therefore necessary for monitoring the development of drug resistance in the region. This study aims to investigate the prevalence of the mutation in the P. vivax multidrug resistant 1 (Pvmdr1), dihydrofolate reductase (Pvdhfr), and dihydropteroate synthetase (Pvdhps) genes conferred resistance to chloroquine (CQ), pyrimethamine (P) and sulfadoxine (S), respectively. 100 P. vivax isolates were obtained between January to May 2023 from a Kanchanaburi province, western Thailand. Nucleotide sequences of Pvmdr1, Pvdhfr, and Pvdhps genes were amplified and sequenced. The frequency of single nucleotide polymorphisms (SNPs)-haplotypes of drug-resistant alleles was assessed. The linkage disequilibrium (LD) tests were also analyzed. In Pvmdr1, T958M, Y976F, and F1076L, mutations were detected in 100%, 21%, and 23% of the isolates, respectively. In Pvdhfr, the quadruple mutant allele (I57R58M61T117) prevailed in 84% of the samples, followed by (L57R58M61T117) in 11%. For Pvdhps, the double mutant allele (G383G553) was detected (48%), followed by the triple mutant allele (G383M512G553) (47%) of the isolates. The most prevalent combination of Pvdhfr (I57R58M61T117) and Pvdhps (G383G553) alleles was sextuple mutated haplotypes (48%). For LD analysis, the association in the SNPs pairs was found between the intragenic and intergenic regions of the Pvdhfr and Pvdhps genes. The study has recently updated the high prevalence of three gene mutations associated with CQ and SP resistance. Genetic monitoring is therefore important to intensify in the regions to further assess the spread of drug resistant. Our data also provide evidence on the distribution of drug resistance for the early warning system, thereby threatening P. vivax malaria treatment policy decisions at the national level.

Identifying potential genetic epistasis implicated in Alzheimer’s disease via detection of SNP-SNP interaction on quantitative trait CSF Aβ42

Although genome-wide association studies have identified multiple Alzheimer's disease (AD)-associated loci by selecting the main effects of individual single-nucleotide polymorphisms (SNPs), the interpretation of genetic variance in AD is limited. Based on the linear regression method, we performed genome-wide SNP-SNP interaction on cerebrospinal fluid Aβ42 to identify potential genetic epistasis implicated in AD, with age, gender, and diagnosis as covariates. A GPU-based method was used to address the computational challenges posed by the analysis of epistasis. We found 368 SNP pairs to be statistically significant, and highly significant SNP-SNP interactions were identified between the marginal main effects of SNP pairs, which explained a relatively high variance at the Aβ42 level. Our results replicated 100 previously reported AD-related genes and 5 gene-gene interaction pairs of the protein-protein interaction network. Our bioinformatics analyses provided preliminary evidence that the 5-overlapping gene-gene interaction pairs play critical roles in inducing synaptic loss and dysfunction, thereby leading to memory decline and cognitive impairment in AD-affected brains.

Genetic insights into crossbred dairy cattle of Pakistan: exploring allele frequency, linkage disequilibrium, and effective population size at a genome-wide scale.

Linkage disequilibrium (LD) affects genomic studies accuracy. High-density genotyping platforms identify SNPs across animal genomes, increasing LD evaluation resolution for accurate analysis. This study aimed to evaluate the decay and magnitude of LD in a cohort of 81 crossbred dairy cattle using the GGP_HDv3_C Bead Chip. After quality control, 116,710 Single Nucleotide Polymorphisms (SNPs) across 2520.241Mb of autosomes were retained. LD extent was assessed between autosomal SNPs within a 10Mb range using the r2 statistics. LD value declined as inter-marker distance increased. The average r2 value was 0.24 for SNP pairs < 10kb apart, decreasing to 0.13 for 50-100kb distances. Minor allele frequency (MAF) and sample size significantly impact LD. Lower MAF thresholds result in smaller r2 values, while higher thresholds show increased r2 values. Additionally, smaller sample sizes exhibit higher average r2 values, especially for larger physical distance intervals (> 50kb) between SNP pairs. Effective population size and inbreeding coefficient were 150 and 0.028 for the present generation, indicating a decrease in genetic diversity over time. These findings imply that the utilization of high-density SNP panels and customized/breed-specific SNP panels represent a highly favorable approach for conducting genome-wide association studies (GWAS) and implementing genomic selection (GS) in the Bos indicus cattle breeds, whose genomes are still largely unexplored. Furthermore, it is imperative to devise a meticulous breeding strategy tailored to each herd, aiming to enhance desired traits while simultaneously preserving genetic diversity.

Interaction Analysis Reveals Complex Genetic Associations with Alzheimer's Disease in the CLU and ABCA7 Gene Regions.

Sporadic Alzheimer's disease (AD) is a polygenic neurodegenerative disorder. Single-nucleotide polymorphisms (SNPs) in multiple genes (e.g., CLU and ABCA7) have been associated with AD. However, none of them were characterized as causal variants that indicate the complex genetic architecture of AD, which is likely affected by individual variants and their interactions. We performed a meta-analysis of four independent cohorts to examine associations of 32 CLU and 50 ABCA7 polymorphisms as well as their 496 and 1225 pair-wise interactions with AD. The single SNP analyses revealed that six CLU and five ABCA7 SNPs were associated with AD. Ten of them were previously not reported. The interaction analyses identified AD-associated compound genotypes for 25 CLU and 24 ABCA7 SNP pairs, whose comprising SNPs were not associated with AD individually. Three and one additional CLU and ABCA7 pairs composed of the AD-associated SNPs showed partial interactions as the minor allele effect of one SNP in each pair was intensified in the absence of the minor allele of the other SNP. The interactions identified here may modulate associations of the CLU and ABCA7 variants with AD. Our analyses highlight the importance of the roles of combinations of genetic variants in AD risk assessment.

Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort.

Alzheimer's disease (AD) is the main cause of dementia worldwide, and the genetic mechanism of which is not yet fully understood. Much evidence has accumulated over the past decade to suggest that after the first large-scale genome-wide association studies (GWAS) were conducted, the problem of "missing heritability" in AD is still a great challenge. Epistasis has been considered as one of the main causes of "missing heritability" in AD, which has been largely ignored in human genetics. The focus of current genome-wide epistasis studies is usually on single nucleotide polymorphisms (SNPs) that have significant individual effects, and the amount of heritability explained by which was very low. Moreover, AD is characterized by progressive cognitive decline and neuronal damage, and some studies have suggested that hyperphosphorylated tau (P-tau) mediates neuronal death by inducing necroptosis and inflammation in AD. Therefore, this study focused on identifying epistasis between two-marker interactions at marginal main effects across the whole genome using cerebrospinal fluid (CSF) P-tau as quantitative trait (QT). We sought to detect interactions between SNPs in a multi-GPU based linear regression method by using age, gender, and clinical diagnostic status (cds) as covariates. We then used the STRING online tool to perform the PPI network and identify two-marker epistasis at the level of gene-gene interaction. A total of 758 SNP pairs were found to be statistically significant. Particularly, between the marginal main effect SNP pairs, highly significant SNP-SNP interactions were identified, which explained a relatively high variance at the P-tau level. In addition, 331 AD-related genes were identified, 10 gene-gene interaction pairs were replicated in the PPI network. The identified gene-gene interactions and genes showed associations with AD in terms of neuroinflammation and neurodegeneration, neuronal cells activation and brain development, thereby leading to cognitive decline in AD, which is indirectly associated with the P-tau pathological feature of AD and in turn supports the results of this study. Thus, the results of our study might be beneficial for explaining part of the "missing heritability" of AD.

A genome-wide association study of freezing tolerance in red clover (Trifolium pratense L.) germplasm of European origin.

Improvement of persistency is an important breeding goal in red clover (Trifolium pratense L.). In areas with cold winters, lack of persistency is often due to poor winter survival, of which low freezing tolerance (FT) is an important component. We conducted a genome wide association study (GWAS) to identify loci associated with freezing tolerance in a collection of 393 red clover accessions, mostly of European origin, and performed analyses of linkage disequilibrium and inbreeding. Accessions were genotyped as pools of individuals using genotyping-by-sequencing (pool-GBS), generating both single nucleotide polymorphism (SNP) and haplotype allele frequency data at accession level. Linkage disequilibrium was determined as a squared partial correlation between the allele frequencies of pairs of SNPs and found to decay at extremely short distances (< 1 kb). The level of inbreeding, inferred from the diagonal elements of a genomic relationship matrix, varied considerably between different groups of accessions, with the strongest inbreeding found among ecotypes from Iberia and Great Britain, and the least found among landraces. Considerable variation in FT was found, with LT50-values (temperature at which 50% of the plants are killed) ranging from -6.0°C to -11.5°C. SNP and haplotype-based GWAS identified eight and six loci significantly associated with FT (of which only one was shared), explaining 30% and 26% of the phenotypic variation, respectively. Ten of the loci were found within or at a short distance (<0.5 kb) from genes possibly involved in mechanisms affecting FT. These include a caffeoyl shikimate esterase, an inositol transporter, and other genes involved in signaling, transport, lignin synthesis and amino acid or carbohydrate metabolism. This study paves the way for a better understanding of the genetic control of FT and for the development of molecular tools for the improvement of this trait in red clover through genomics assisted breeding.

Design and validation of a high-density single nucleotide polymorphism array for the Eastern oyster (Crassostrea virginica).

Dense single nucleotide polymorphism (SNP) arrays are essential tools for rapid high-throughput genotyping for many genetic analyses, including genomic selection and high-resolution population genomic assessments. We present a high-density (200 K) SNP array developed for the Eastern oyster (Crassostrea virginica), which is a species of significant aquaculture production and restoration efforts throughout its native range. SNP discovery was performed using low-coverage whole-genome sequencing of 435 F1 oysters from families from 11 founder populations in New Brunswick, Canada. An Affymetrix Axiom Custom array was created with 219,447 SNPs meeting stringent selection criteria and validated by genotyping more than 4,000 oysters across 2 generations. In total, 144,570 SNPs had a call rate >90%, most of which (96%) were polymorphic and were distributed across the Eastern oyster reference genome, with similar levels of genetic diversity observed in both generations. Linkage disequilibrium was low (maximum r2 ∼0.32) and decayed moderately with increasing distance between SNP pairs. Taking advantage of our intergenerational data set, we quantified Mendelian inheritance errors to validate SNP selection. Although most of SNPs exhibited low Mendelian inheritance error rates overall, with 72% of called SNPs having an error rate of <1%, many loci had elevated Mendelian inheritance error rates, potentially indicating the presence of null alleles. This SNP panel provides a necessary tool to enable routine application of genomic approaches, including genomic selection, in C. virginica selective breeding programs. As demand for production increases, this resource will be essential for accelerating production and sustaining the Canadian oyster aquaculture industry.

Improving genomic prediction of rhizomania resistance in sugar beet (Beta vulgaris L.) by implementing epistatic effects and feature selection

Background: Rhizomania counts as the most important disease in sugar beet Beta vulgaris L. for which no plant protection is available, leaving plant breeding as the only defence strategy at the moment. Five resistance genes have been detected on the same chromosome and further studies suggested that these might be different alleles at two resistance clusters. Nevertheless, it was postulated that rhizomania resistance might be a quantitative trait with multiple unknown minor resistance genes. Here, we present a first attempt at genomic prediction of rhizomania resistance in a population that was genotyped using single nucleotide polymorphism (SNP) markers. Methods: First, genomic prediction was performed using all SNPs. Next, we calculated the variable importance for each SNP using machine learning and performed genomic prediction by including the SNPs incrementally in the prediction model based on their variable importance. Using this method, we selected the optimal number of SNPs that maximised the prediction accuracy. Furthermore, we performed genomic prediction with SNP pairs. We also performed feature selection with SNP pairs using the information about the variable importance of the single SNPs. Results: From the four methods under investigation, the latter led to the highest prediction accuracy. These results lead to the following conclusions: (I) The genotypes that were resistant at all known resistance genes, provided the highest possible variation of virus concentrations that the machine can measure. Thus, it can be assumed that more genes must be involved in the resistance towards rhizomania. (II) We show that prediction models that include SNP interactions increased the prediction accuracy. Conclusions: Altogether, our findings suggest that rhizomania resistance is a complex quantitative trait that is affected by multiple genes as well as their interaction.

Genome-wide unraveling SNP pairwise epistatic effects associated with sheep body weight

Epistatic effects are an important part of the genetic effect of complex traits in livestock. In this study, we used 218 synthetic ewes from the Xinjiang Academy of Agricultural Reclamation in China to identify interacting paired with genome-wide single nucleotide polymorphisms (SNPs) associated with birth weight, weaning weight, and one-yearling weight. We detected 2 and 66 SNP-SNP interactions of sheep birth weight and weaning weight, respectively. No significant epistatic interaction of one-year-old body weight was detected. The genetic interaction of sheep body weight is dynamic and time-dependent. Most significant interactions of weaning body weight contributed 1% or higher. In the weaning weight trait, 66 significant SNP pairs consisted of 98 single SNPs covering 23 chromosomes, 5 of which were nonsynonymous SNPs (nsSNPs), resulting in single amino acid substitution. We found that genes that interact with transcription factors (TFs) are target genes for the corresponding TFs. Four epitron networks affecting weaning weight, including subnetworks of HIVEP3 and BACH2 transcription factors, constructed using significant SNP pairs, were also analyzed and annotated. These results suggest that transcription factors may play an important role in explaining epistatic effects. It provides a new idea to study the genetic mechanism of weight developing.

Multigraph Cluster Analysis of AD Candidate Genes via Human Brain Mapping

AbstractBackgroundBrain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer’s diseases (AD). Given the high dimensionality, the individual level SNP‐QT signals typically have small effect sizes and are hard to be detected and replicated. To overcome this limitation, this work proposes a new approach to identify high‐level imaging genetic associations through applying multi‐graph clustering to SNP‐QT association maps.MethodsParticipants include 255 cognitive normal (CN) and 218 late mild cognitive impairment (LMCI) subjects from the ADNI data. Linear regression is performed to regress regional AV‐45 measures on each of the 54 AD SNPs to obtain a brain association map across 116 AAL‐ROIs. To quantify the similarity between each SNP pair, 5 scoring functions are used to calculate 5 different similarity measures between the respective brain maps of the two SNPs. Each scoring function yields a similarity graph of all 54 SNPs. Multigraph min‐max clustering is applied to 5 different graphs simultaneously to group similar SNPs together. Functional annotation is performed on each SNP cluster and the corresponding average brain association map to provide high level interpretations.ResultsFigure 1 shows the genetic effect on regional AV‐45 measures for each pair of SNP and ROI. The multi‐graph clustering result is shown in Figure 2, where 54 AD‐related SNPs are clustered into two groups. Figure 3a shows the results of Enrichr Elsevier pathway analysis. SNP group 1 is associated with pathways such as amyloid beta clearance, while SNP group 2 is associated with pathways including amyloid beta formation and APP processing in AD. Figure 3b shows the NeuroSynth annotation of the average brain effect maps associated with the two SNP clusters, respectively.ConclusionWe proposed an approach that transforms individual level imaging genetic associations (SNP vs ROI) to high‐level associations (i.e., SNP cluster vs brain association map), and applied it to an LMCI study. These high‐level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.

Associations between ALOX5 and ALOX5AP Genetic Polymorphisms and Cognitive Performance in Patients with Alzheimer’s Dementia and Mild Cognitive Impairment

AbstractBackgroundLeukotriene activity may contribute to cognitive deficits in Alzheimer’s disease (AD). Leukotrienes are synthesized by 5‐lipoxygenase (5‐LOX) and its activating protein (FLAP), encoded by ALOX5 and ALOX5AP, respectively. This cross‐sectional study investigated the associations of ALOX5 and ALOX5AP single nucleotide polymorphisms (SNPs) with cognitive performance in patients with AD or mild cognitive impairment (MCI).Method ALOX5 SNP rs2029253 and ALOX5AP SNPs rs10507391, rs12429692, rs4147064, rs9551963, rs9579646 were selected based on previous associations to ischemic stroke. Participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) diagnosed with AD or MCI at baseline were separated by genotype for each SNP. Rey Auditory Verbal Learning Test (RAVLT), Mini Mental State Exam (MMSE), and Trail Making Part B (TRAB) tests were selected to measure memory, overall cognitive status, and executive function, respectively. Associations between SNPs and white matter hyperintensities (WMH) were also explored. Additive, dominant, and recessive models were tested for each SNP and outcome pair. Linear regressions were used to assess associations between genotypes and outcomes controlling for age, sex, and education.Resultrs12429692 TT homozygotes (MAF = 25.3%) had lower RAVLT learning (i.e. Trial 5 minus Trial 1) scores (F1,508 = 7.19, p = 0.008, n = 513, recessive model). rs4147064 TC heterozygotes (MAF = 46.5%) had higher MMSE scores than TT homozygotes (F2,508 = 3.34, p = 0.036, n = 514, additive model). rs9551963 CC homozygotes (MAF = 49.1%) had higher TRAB times (F1,481 = 6.06, p = 0.014, n = 486, recessive model). Split by sex, rs12429692 was associated with TRAB times in males (F1,277 = 5.52, p = 0.020, n = 281, dominant model) and rs9551963 was associated with RAVLT % forgetting scores in females (F1,212 = 4.98, p = 0.027, n = 216, recessive model). The rs12429692 A/T SNP (MAF = 25.4%) trended towards an association with WMH (F1,507 = 3.33, p = 0.069, n = 512, recessive model).ConclusionThree ALOX5AP SNPs were associated with aspects of cognitive performance. This preliminary clinical evidence suggests a possible effect of leukotriene synthesis on cognition in patients with AD or MCI.

Failing the four-gamete test enables exact phasing: the Corners’ Algorithm

BackgroundFailing the four-gamete test for two polymorphic DNA markers is an indication that two or three rather than four haplotypes segregate in the population. The objective of this paper is to show that when just three haplotypes are segregating, all three haplotypes can be fully and unambiguously phase-resolved.Theory and methodsThe Corners’ Algorithm tests the four corners in a 3 × 3 table of two-locus genotypes. If one of the four corners is filled with zeroes, then the missing haplotype is identified and the phases of all three haplotypes can be unambiguously resolved for all individuals. Three applications of this method are proposed when the four-gamete test fails: (1) direct estimation of linkage disequilibrium (LD), (2) haplotype-based genome-wide association studies (GWAS) of three haplotypes (single-marker GWAS tests for two out of three haplotypes only), and (3) haplotyping of chromosomal regions that are comprised of pairs of single nucleotide polymorphisms (SNPs) that consist of just three haplotypes. An example based on 435 sows with performance records for total number of piglets born is used to illustrate the methods.ResultsOf 20,339 SNPs, approximately 50% of the pairs of flanking SNPs failed the four-gamete test. For those, the expectation maximization (EM) algorithm gave the same results. The average of the absolute value of the difference in r2 between flanking SNPs across the genome between the two methods was 0.00082. Single-marker GWAS (using two of three haplotypes) detected significant associations for total number of piglets born on chromosomes 1, 2, 6, 9, 10, 12, 13, 14, 15, and 18. Haplotype-based GWAS using the third haplotype resolved with the Corners’ Algorithm detected additional significant associations for total number of piglets born on chromosomes 2, 5, 10, 13, 14, 15, and 18. Estimated substitution effects ranged from 0.40 to 1.35 piglets. Haplotyping of chromosomal regions that failed the four-gamete test for any pair of SNPs covered 961 Mb out of the 2249 Mb by the SNP array.ConclusionsThe Corner’s Algorithm allows to fully phase haplotypes when the four-gamete test fails. Longer haplotypes in chromosomal regions in which the four-gamete test fails for any pair of SNPs can be used as a multi-allelic marker with increased polymorphism information content.

Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network.

ObjectivesGenome-wide association studies (GWAS) are performed to study the associations between genetic variants with respect to certain phenotypic traits such as cancer. However, the method that is commonly used in GWAS assumes that certain traits are solely affected by a single mutation. We propose a network analysis method, in which we generate association networks of single-nucleotide polymorphisms (SNPs) that can differentiate case and control groups. We hypothesize that certain phenotypic traits are attributable to mutations in groups of associated SNPs.MethodsWe propose a method based on a network analysis framework to study SNP-SNP interactions related to cancer incidence. We employed logistic regression to measure the significance of all SNP pairs from GWAS for the incidence of colorectal cancer and computed a cancer risk score based on the generated SNP networks.ResultsWe demonstrated our method in a dataset from a case-control study of colorectal cancer in the South Sulawesi population. From the GWAS results, 20,094 pairs of 200 SNPs were created. We obtained one cluster containing four pairs of five SNPs that passed the filtering threshold based on their p-values. A locus on chromosome 12 (12:54410007) was found to be strongly connected to the four variants on chromosome 1. A polygenic risk score was computed from the five SNPs, and a significant difference in colorectal cancer risk was obtained between the case and control groups.ConclusionsOur results demonstrate the applicability of our method to understand SNP-SNP interactions and compute risk scores for various types of cancer.

Identification of the associations between genes and quantitative traits using entropy-based kernel density estimation

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.

A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians

IntroductionAlthough genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC). MethodsLeveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers. ResultsWith the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10−13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10−13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10−13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10−13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10−4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification. ConclusionsImportant G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.

Genome-wide Association Study Reveals New Loci Associated With Pyrethroid Resistance in Aedes aegypti.

Genome-wide association studies (GWAS) use genetic polymorphism across the genomes of individuals with distinct characteristics to identify genotype-phenotype associations. In mosquitoes, complex traits such as vector competence and insecticide resistance could benefit from GWAS. We used the Aedes aegypti 50k SNP chip to genotype populations with different levels of pyrethroid resistance from Northern Brazil. Pyrethroids are widely used worldwide to control mosquitoes and agricultural pests, and their intensive use led to the selection of resistance phenotypes in many insects including mosquitoes. For Ae. aegypti, resistance phenotypes are mainly associated with several mutations in the voltage-gated sodium channel, known as knockdown resistance (kdr). We phenotyped those populations with the WHO insecticide bioassay using deltamethrin impregnated papers, genotyped the kdr alleles using qPCR, and determined allele frequencies across the genome using the SNP chip. We identified single-nucleotide polymorphisms (SNPs) directly associated with resistance and one epistatic SNP pair. We also observed that the novel SNPs correlated with the known kdr genotypes, although on different chromosomes or not in close physical proximity to the voltage gated sodium channel gene. In addition, pairwise comparison of resistance and susceptible mosquitoes from each population revealed differentiated genomic regions not associated with pyrethroid resistance. These new bi-allelic markers can be used to genotype other populations along with kdr alleles to understand their worldwide distribution. The functional roles of the genes near the newly discovered SNPs require new studies to determine if they act synergistically with kdr alleles or reduce the fitness cost of maintaining resistant alleles.