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PubMed2006

Analysing the effect of candidate genes on complex traits: an application in multiple sclerosis.

The conventional approach of candidate gene studies in complex diseases is to look at the effect of one gene at a time. However, as the outcome of chronic diseases is influenced by a large number of alleles, simultaneous analysis is needed. We demonstrate the application of multivariate regression and cluster analysis to a multiple sclerosis (MS) dataset with genotypes for 489 patients at 11 candidate genes selected on their involvement in the immune response. Using multivariate regression, we observed that different sets of genes were associated with different disease characteristics that reflect different aspects of disease. Out of 15 polymorphisms, we identified one that contributed to the severity of disease. In addition, the set of 15 polymorphisms was predictive for yearly increase in lesion volume as seen on T1-weighted MRI (p=0.044). From this set, no individual polymorphisms could be identified after adjustment for multiple hypotheses testing. By means of a cluster analysis, we aimed to identify subgroups of patients with different pathogenic subtypes of MS on the basis of their genetic profile. We constructed genetic profiles from the genotypes at the 11 candidate genes. The approach proved to be feasible. We observed three clusters in the sample of patients. In this study, we observed no significant differences in the usual clinical and MRI outcome measures between the different clusters. However, a number of consistent trends indicated that this clustering might be related to the course of disease. With a larger number of genes regulating the course of disease, we may be able to identify clinically relevant clusters. The analyses are easily implemented and will be applicable to candidate gene studies of complex traits in general.

Adult↗
PubMed2003

Loci on chromosomes 2, 4, 9, and 16 for body weight, body length, and adiposity identified in a genome scan of an F2 intercross between the 129P3/J and C57BL/6ByJ mouse strains.

Mice have proved to be a powerful model organism for understanding obesity in humans. Single gene mutants and genetically modified mice have been used to identify obesity genes, and the discovery of loci for polygenic forms of obesity in the mouse is an important next step. To pursue this goal, the inbred mouse strains 129P3/J (129) and C57BL/6ByJ (B6), which differ in body weight, body length, and adiposity, were used in an F2 cross to identify loci affecting these phenotypes. Linkages were determined in a two-phase process. In the first phase, 169 randomly selected F2 mice were genotyped for 134 markers that covered all autosomes and the X Chromosome (Chr). Significant linkages were found for body weight and body length on Chr 2. In addition, we detected several suggestive linkages on Chr 2 (adiposity), 9 (body weight, body length, and adiposity), and 16 (adiposity), as well as two suggestive sex-dependent linkages for body length on Chrs 4 and 9. In the second phase, 288 additional F2 mice were genotyped for markers near these regions of linkage. In the combined set of 457 F2 mice, six significant linkages were found: Chr 2 (Bwq5, body weight and Bdln3, body length), Chr 4 (Bdln6, body length, males only), Chr 9 (Bwq6, body weight and Adip5, adiposity), and Chr 16 (Adip9, adiposity), as well as several suggestive linkages (Adip2, adiposity on Chr 2, Bdln4 and Bdln5, body length on Chr 9). In addition, there was a suggestive linkage to body length in males on Chr 9 (Bdln4). For adiposity, there was evidence for epistatic interactions between loci on Chr 9 (Adip5) and 16 (Adip9). These results reinforce the concept that obesity is a complex trait. Genetic loci and their interactions, in conjunction with sex, age, and diet, determine body size and adiposity in mice.

Animals↗
PubMed2004

A large-sample QTL study in mice: II. Body composition.

Using lines of mice having undergone long-term selection for high and low growth, a large-sample (n = approximately 1,000 F2) experiment was conducted to gain further understanding of the genetic architecture of complex polygenic traits. Composite interval mapping on data from male F2 mice (n = 552) detected 50 QTL on 15 chromosomes impacting weights of various organ and adipose subcomponents of growth, including heart, liver, kidney, spleen, testis, and subcutaneous and epididymal fat depots. Nearly all aggregate growth QTL could be interpreted in terms of the organ and fat subcomponents measured. More than 25% of QTL detected map to MMU2, accentuating the relevance of this chromosome to growth and fatness in the context of this cross. Regions of MMU7, 15, and 17 also emerged as important obesity "hot-spots." Average degrees of directional dominance are close to additivity, matching expectations for body composition traits. A strong QTL congruency is evident among heart, liver, kidney, and spleen weights. Liver and testis are organs whose genetic architectures are, respectively, most and least aligned with that for aggregate body weight. In this study, growth and body weight are interpreted in terms of organ subcomponents underlying the macro aggregate traits, and anchored on the corresponding genomic locations.

Animals↗
PubMed2004

A large-sample QTL study in mice: I. Growth.

By use of long-term selection lines for high and low growth, a large-sample (n = approximately 1,000 F2) experiment was conducted in mice to further understand the genetic architecture of complex polygenic traits. In combination with previous work, we conclude that QTL analysis has reinforced classic polygenic paradigms put in place prior to molecular analysis. Composite interval mapping revealed large numbers of QTL for growth traits with an exponential distribution of magnitudes of effects and validated theoretical expectations regarding gene action. Of particular significance, large effects were detected on Chromosome (Chr) 2. Regions on Chrs 1, 3, 6, 10, 11, and 17 also harbor loci with significant contributions to phenotypic variation for growth. Despite the large sample size, average confidence intervals of approximately 20 cM exhibit the poor resolution for initial estimates of QTL location. Analysis with genome-wide and chromosomal polygenic models revealed that, under certain assumptions, large fractions of the genome may contribute little to phenotypic variation for growth. Only a few epistatic interactions among detected QTL, little statistical support for gender-specific QTL, and significant age effects on genetic architecture were other primary observations from this study.

Animals↗
PubMed2004

Genetic structure of the LXS panel of recombinant inbred mouse strains: a powerful resource for complex trait analysis.

The set of LXS recombinant inbred (RI) strains is a new and exceptionally large mapping panel that is suitable for the analysis of complex traits with comparatively high power. This panel consists of 77 strains-more than twice the size of other RI sets--and will typically provide sufficient statistical power (beta = 0.8) to map quantitative trait loci (QTLs) that account for approximately 25% of genetic variance with a genomewide p < 0.05. To characterize the genetic architecture of this new set of RI strains, we genotyped 330 MIT microsatellite markers distributed on all autosomes and the X Chromosome and assembled error-checked meiotic recombination maps that have an average F2-adjusted marker spacing of approximately 4 cM. The LXS panel has a genetic structure consistent with random segregation and subsequent fixation of alleles, the expected 3-4 x map expansion, a low level of nonsyntenic association among loci, and complete independence among all 77 strains. Although the parental inbred strains-Inbred Long-Sleep (ILS) and Inbred Short-Sleep (ISS)--were derived originally by selection from an 8-way heterogeneous stock selected for differential sensitivity to sedative effects of ethanol, the LXS panel is also segregating for many other traits. Thus, the LXS panel provides a powerful new resource for mapping complex traits across many systems and disciplines and should prove to be of great utility in modeling the genetics of complex diseases in human populations.

Alleles↗
PubMed2004

Quasi-linkage: a confounding factor in linkage analysis of complex diseases?

Human linkage analysis is based on the assumption that unlinked genomic loci, particularly loci located on non-homologous chromosomes, segregate independently during meiosis. An exception to this rule is the phenomenon of quasi-linkage (QL) that describes the non-random segregation of non-homologous chromosomes, which can undermine the basic concept of linkage. Molecular mechanisms of QL are not clear; however, observations in mice and plants suggest a possible affinity between non-homologous chromosomal regions containing repetitive or like sequences. QL has not been investigated in humans. As QL may generate false linkages in genome scans of complex diseases, we sought to determine whether genomic loci detected in such genome scans exhibit QL. A number of individual markers showing linkage to schizophrenia, asthma, multiple sclerosis, inflammatory bowel disease and type-1 diabetes were tested for QL in a pairwise linkage analysis against all other markers exhibiting evidence for linkage in each specific study. The Marshfield genotype dataset of eight CEPH families was used for this purpose. The best QL lod scores generated from the analysis were within the range of the "lukewarm" lod scores reported in the majority of linkage studies for complex disorders. In addition, we performed a genome-wide QL analysis on the Marshfield family database which detected eight QL lod scores >6. The replication of the best Marshfield QL scores was performed using the deCODE families and although none of the eight pairs demonstrated independent evidence for QL, three pairs generated maximal lod scores of 0.11, 0.3, and 1.51. In conclusion, although complex disease relevant markers did not produce high QL lod scores, the general phenomenon of QL in humans cannot be excluded and potentially can be a confounding factor in genetic studies of complex traits.

Asthma↗
PubMed2005

Discordant evolution of nephrotic syndrome in mono- and dizygotic twins.

Twins represent a powerful resource for revealing multifactorial mechanisms in human diseases. Few reports are available on nephrotic syndrome in twins, and most furnish only a partial description of genetic identity based on human leukocyte antigens (HLA) analysis. We describe two pairs of mono and dizygotic twins with nephrotic syndrome who presented discordant outcomes in terms of length and required therapies. In one case, evolution to focal glomerulosclerosis was also documented. The basic molecular work-up included analysis of concordance based on 10 polymorphic markers (D3S1358, vVA, FGA, amelogenin, D8S1179, D21S11, D18S51, D5S818, D13S317, D7S820) and exclusion of the major slit-diaphragm gene mutation (NPHS2, CD2AP, WT1) causing nephrotic syndrome. To our knowledge, this is the first description of long-term outcome in mono- and dizygotic twins with proven genetic concordance. Discordant outcomes indicate a major influence of environmental and/or epigenetic multifactorial mechanisms on persistence and evolution of the disease to focal-segmental glomerulosclerosis.

Adult↗
PubMed2004

High throughput multiple combination extraction from large scale polymorphism data by exact tree method.

Single nucleotide polymorphisms (SNPs) are increasingly becoming important in clinical settings as useful genetic markers. For the evaluation of genetic risk factors of multifactorial diseases, it is not sufficient to focus on individual SNPs. It is preferable to evaluate combinations of multiple markers, because it allows us to examine the interactions between multiple factors. If all the combinations possible were evaluated round-robin, the number of calculations would rapidly explode as the number of markers analyzed increased. To overcome this limitation, we devised the exact tree method based on decision tree analysis and applied it to 14 SNP data from 68 Japanese stroke patients and 189 healthy controls. From the obtained tree models, we succeeded in extracting multiple statistically significant combinations that elevate the risk of stroke. From this result, we inferred that this method would work more efficiently in the whole genome study, which handles thousands of genetic markers. This exploratory data mining method will facilitate the extraction of combinations from large-scale genetic data and provide a good foothold for further verificatory research.

Adult↗
PubMed1999

Association of diffuse panbronchiolitis with microsatellite polymorphism of the human interleukin 8 (IL-8) gene.

Diffuse panbronchiolitis (DPB) is a distinctive chronic inflammatory lung disease predominantly found in Asian populations. Although its etiology is unknown, DPB is considered to be a multifactorial disease of whose susceptibility is determined by genetic predisposition unique to Asians. We and others have previously reported that the B*5401 allele of the human leukocyte antigen (HLA)-B gene or a closely linked gene in the HLA region on 6p21.3 is one of the major genetic factors in susceptibility to this disease. However, the association with B*5401 is not absolute and the contribution of other genetic or environmental factors should also be considered. Here, four candidate genes that are postulated to play a role in the pathophysiology of DPB, namely, RON-kinase, CYP3A4, motilin, and interleukin (IL)-8, were chosen, and association studies between microsatellite markers at these loci and DPB were conducted. We demonstrated the presence of a specific allele at the IL-8 locus was associated with the disease (c2 = 9.13; P = 0.0025; corrected P [Pc] < 0.05). Although further studies are needed to examine whether neutrophil accumulation in the airways of patients with DPB is controlled by a possible genetic variation of IL-8 or other chemokine genes located in the region 4q12-q13, our data suggest that genes other than those of the HLA system may also contribute to a genetic predisposition to DPB.

Asian People↗
PubMed2005

Genetic control of the opaque-2 gene and background polygenes over some kernel traits in maize (Zea mays L.).

Some kernel traits of agronomical importance in maize are affected by the opaque-2 (o2) gene and background polygenes, which express in different genetic systems such as embryo, endosperm, cytoplasm and maternal plant. A genetic model for seed quantitative traits with the o2 gene effects and polygenic effects as well as their GE interactions was used for protein content, lysine content, oil content and kernel density in maize. The results suggested that the o2 gene was involved in the traits investigated but the effects of the o2 gene were distinctive on various traits. The effects of the o2 gene were large on lysine content and protein content while minor on oil content. There was a substantially wide quantitative variation from polygenes expressing in different genetic systems for the traits evaluated. Significant GE interactions of the o2 gene and background polygenes declared that not only the main effects but also specific expressions depending on environments were responsible for variation of the traits studied. There seemed to have strong maternal heterosis and slight embryo heterosis for kernel density.

DNA-Binding Proteins↗
PubMed2026

Advancing genetic evaluation of milk yield and composition using a genomic-polygenic model in smallholder dairy cattle farms in Thailand.

Improving the accuracy of genetic evaluation in smallholder dairy systems is essential for sustainable productivity. However, traditional polygenic models (PM) are often constrained by incomplete pedigree, heterogeneous management, and limited genotyping resources. This study evaluated a genomic-polygenic model (GPM) relative to a PM using data from a multibreed dairy population raised under Thai tropical smallholder conditions. Phenotypic records for 305-day milk yield (MY), fat percentage (FP), protein percentage (PP), and somatic cell count (SCC) from 14,417 first-lactation cows across 1,321 farms were analyzed together with genotypes from 5,479 animals generated using GeneSeek Genomic Profiler (GGP) arrays ranging from 9&#xa0;K to 150&#xa0;K SNPs. Both models included herd-year-season of calving, age at first calving, and heterosis as fixed effects, and additive genetic and residual components treated as random. The GPM yielded higher additive genetic variances and heritability estimates and produced more biologically consistent antagonistic correlations among traits than the PM. Prediction accuracy was improved for all animal groups under the GPM, with the largest gain observed in genotyped young sires (18.36%). Pedigree connectedness analysis indicated that genotyping animals with low to moderate relationships enhances accuracy cost-effectively. Despite persistent challenges associated with heterogeneous management and limited pedigree depth, the results demonstrate the practical value of genomic-polygenic evaluation for smallholder multibreed dairy populations in tropical environments.

Animals↗
PubMed2005

Complex phenotypes and complex genetics: an introduction to genetic studies of complex traits.

There is currently intense interest in the genetic factors contributing to many diseases with cardiovascular complications. Diseases like atherosclerosis, diabetes, and hypertension are referred to as complex traits because multiple genes contribute to the phenotype either individually or through interactions with each other or the environment. Enabled and energized by the striking successes over the past 20 years in identifying genes that are responsible for single gene traits, many geneticists have turned to the investigation of methods that will allow for the dissection of complex traits. There have already been some successes, so there is no reason to consider the problem as inherently intractable. However, it is important to reflect on what conditions are necessary for the identification of genes that operate in complex traits. A recurring theme in this research area has been difficulty in repeating and validating research findings, and this most often can be attributed to limitations in study design. It is also important to consider that any particular research strategy can only hope to describe a portion of factors that contribute to variation in the population; therefore, the genetic approach cannot be a panacea. New efficient technologies for genotyping and public databases describing the fine structure of genetic correlations in the genome should aid many aspects of the gene discovery process.

Genetic Predisposition to Disease↗
PubMed2000

Genetic contributions to left ventricular hypertrophy.

Left ventricular (LV) hypertrophy is a common condition that profoundly affects morbidity and mortality from cardiovascular diseases, including myocardial infarction, congestive heart failure, and stroke. Noninvasive imaging methods have greatly expanded our ability to evaluate cardiac structural and functional characteristics, and enhanced our understanding of the natural history of LV hypertrophy. The etiology of LV hypertrophy is likely due to the effects of multiple genes interacting with other genes and the environment. Although hypertension is recognized as a strong determinant of LV hypertrophy, blood pressure explains only a limited amount of the interindividual variation in LV mass. Moreover, LV hypertrophy occurs in the absence of hypertension, and in some cases precedes its development. Genes encoding proteins involved in the structure of the LV, as well as genes encoding cell signal transduction, hormones, growth factors, calcium homeostasis, and blood pressure, are likely candidates for the development of common forms of LV hypertrophy. An overview of the epidemiology and pathophysiology of LV hypertrophy and dysfunction is provided, in addition to evidence of the genetic basis for LV hypertrophy.

Black People↗
PubMed2026

Advancements in the Understanding of the Genetics of Obsessive-Compulsive Disorder (OCD).

PURPOSE OF REVIEW: This review summarizes recent advances in the genetics of Obsessive-Compulsive Disorder (OCD), their contribution to understanding disorder biology, and implication for clinical translation. RECENT FINDINGS: Recent GWAS identified 30 genome-wide significant loci and prioritized 25 putatively causal genes. Rare variant studies implicated specific genes, including CHD8, CELSR3, SLITRK5, and QRICH1. Evidence from common and rare variants support brain- and immune-related pathways. Genetic overlap with obsessive compulsive symptoms and other psychiatric disorders indicate shared underlying biology. Current evidence is largely based on individuals of European ancestry, although global efforts are underway to improve ancestral diversity in OCD genetics. Given the urgent need for improved treatment, genetically informed clinical translation approaches hold promise, including pharmacogenetics and drug repurposing. Recent advances in OCD genetics support a highly polygenic architecture, implicate specific neuro-biological and immune pathways, and provide new opportunities for clinical translation.

Humans↗
PubMed2025

Exploring depression treatment response by using polygenic risk scoring across diverse populations.

Treatment-resistant depression (TRD), usually defined as limited or no response to at least two antidepressants, occurs in approximately one-third of individuals diagnosed with major depressive disorder (MDD). Studies of individuals of European ancestry highlight a genetic overlap between TRD and MDD. We analyzed two large and diverse biobanks, the UCLA ATLAS Community Health Study (ATLAS) and the All of Us Research Program (AoU), to test for associations between a polygenic score for major depression (MDD-PGS) and TRD. Compared to treatment responders, TRD individuals have higher MDD-PGS across all ancestries. MDD-PGS was significantly associated with response to selective serotonin reuptake inhibitors in individuals of European and Hispanic/Latin American genetic ancestries in both biobanks. In AoU, a decreased MDD-PGS was observed in response to tricyclics or serotonin modulators in individuals of European American ancestry and in response to serotonin and norepinephrine reuptake inhibitors in individuals of African American ancestry. ATLAS found that MDD-PGS showed lower odds of responding to atypical agents than did TRD in MDD-affected individuals belonging to the Hispanic/Latin American group, MDD-PGS was associated with atypical agents. Overall, by leveraging larger sample sizes from two diverse biobanks, we provide new insights into antidepressant response and treatment specificity for MDD in individuals of diverse genetic ancestries.

Adult↗
PubMed2025

Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations.

The broader application of polygenic risk score (PRS) is hindered by the limited transferability of PRS developed in Europeans to non-European populations. While many statistical methods have been developed to improve the performance of PRS in non-European populations, most of them focused on discrete genetic ancestry clusters and did not consider admixed individuals. Admixed individuals pose a unique challenge for PRS calculation due to the complexity of local ancestry and cross-ancestry effect sizes. Here, we present a statistical method called SDPR_admix for calculating PRS in admixed individuals. SDPR_admix characterizes the joint distribution of the effect sizes of a genetic variant with two ancestries to be both zero, ancestry enriched, or shared with correlation. SDPR_admix outperformed other methods in simulations and improved the prediction of real traits in European-African admixed individuals in UK Biobank when trained on the Population Architecture using Genomics and Epidemiology (PAGE) dataset (N = 13,000). Deployment of SDPR_admix on All of Us (N = 52,000) further increased the prediction accuracy by approximately 5-fold on average compared with training on PAGE. This enhancement was achieved with manageable computational time and cost, demonstrating the feasibility of training PRS models on large-scale All of Us data. We provided several examples demonstrating that both ancestral-enriched and shared effects, as included in the SDPR_admix prediction model, are helpful for improving polygenic prediction in admixed populations. We also applied SDPR_admix to construct PRS for admixed Americans with mixture of European and Amerindigenous ancestries and showed that SDPR_admix overall outperformed other methods.

Humans↗
PubMed2026

Additive value of polygenic risk and family history for coronary heart disease risk stratification in two diverse US cohorts.

Whether polygenic risk, monogenic familial hypercholesterolemia (FH), and family history (FamHx) are additively informative for coronary heart disease (CHD) risk prediction across self-identified race/ethnicity (SIRE) groups has not been established. In two diverse cohorts-Electronic Medical Records and Genomics (eMERGE) phase IV (eIV; n = 19,348) and All of Us (AoU; n = 239,645)-we quantified the associations of a polygenic risk score (PRSCHD), pathogenic/likely pathogenic variants in genes associated with FH, and FamHx with CHD and evaluated their incremental value when added to the pooled cohort equations (PCEs). CHD was defined as myocardial infarction, unstable angina, or coronary revascularization. We modeled associations with multivariable logistic regression (prevalent CHD in eIV) and Cox proportional hazards (incident CHD in AoU) and characterized predictive performance with the c-statistic and reclassification and decision-curve net benefits across actionable 10-year risk thresholds. The effects of PRSCHD and FamHx were independent and additive in both cohorts and consistent across White, Black, and Latino SIRE groups. In eIV, adding PRSCHD and FamHx to the PCE increased the c-statistic for prevalent CHD from 0.719 to 0.753 (p-diff = 9.1 &#xd7; 10-3) and reclassified 18.8% of participants at the 7.5% 10-year threshold, yielding approximately 4 additional true-positive CHD identifications per 1,000 screened. Net benefit gains were observed between the 7.5% and 10% thresholds across all three SIRE groups. In conclusion, PRSCHD and FamHx were independently and additively associated with CHD across major SIRE groups in two diverse cohorts in the United States (US), motivating the addition of these factors to clinical risk algorithms.

Humans↗