Characterization of adiposity and inflammation genetic pleiotropy underlying cardiovascular risk factors in Hispanics.

The observed overlap between genetic variants associated with both adiposity and inflammatory markers suggests that changes in both adiposity and inflammation could be partially mediated by common pathways. The pervasive but sparsely characterized “pleiotropic” genetic variants associated with both adiposity and inflammation have been hypothesized to provide insight into the shared biology. This study explored and characterized the genetic pleiotropy underpinning adiposity and inflammation using genetic and phenotypic observations from the Cameron County Hispanic Cohort (CCHC). A total of 3,313 samples and \u3e9 million single nucleotide polymorphisms (SNPs) were examined in this study. Mixed model genome-wide association studies (GWAS) were performed for 9 phenotypes including C-reactive protein (CRP), Interleukin (IL)-6, IL-8, fibrinogen, body mass index (BMI), waist circumference (WC) in males and females, and waist to hip ratio (WHR) in males and females (separately). GWAS for WHR and WC were meta-analyzed to obtain sex-combined results. Pleiotropy assessment was completed using adaptive Sum of Powered Score (aSPU) test. Three genetic loci with evidence of pleiotropy on chromosome 3, 12 and 18 were fine-mapped to distinguish the set of likely vi causal variants. Causal mediation analysis was used to assess whether likely causal variants were independently associated with both inflammation and adiposity. At least 3 signals, on chromosomes 3, 12, and 12, were identified that suggested the presence of SNPs with strong pleiotropic p-values (\u3c 5 × 10−6 ). The fine-mapping of these three suspected pleiotropic regions distinguished 22 variants with posterior causality probabilities greater than 50%. The mediation analysis indicated that rs60505812, on chromosome 3, was independently associated with both an inflammatory marker (IL-6) and an adiposity measure (BMI). For the variant rs73093474, on chromosome 12, results indicated both a direct association with CRP and an indirect association (via WHR). The identification of likely pleiotropic variants indicated that 1) a considerable degree of overlapping genetic pleiotropy exists between adiposity and inflammation, and 2) evidence exists to support both the direct and indirect pleiotropy. The results showed the potential of these genetic variants to provide biological insight, intended to improve the cardiovascular health of the Hispanics, and by extension all populations

Leveraging Pleiotropy to Discover and interpret Gwas Results For Sleep-Associated Traits

Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases

QTL analysis for yield-related traits under different water regimes in maize

Drought is one of the most essential factors influencing maize yield. Improving maize varieties with drought tolerance by using marker-assisted or genomic selection requires more understanding of the genetic basis of yield-related traits under different water regimes. In the present study, 213 F2:3 families of the cross of H082183 (drought-tolerant) × Lv28 (drought susceptible) were phenotyped with five yield-related traits under four well-watered and six drought environments for two years. Quantitative trait loci analysis identified 133 significant QTLs (94 QTLs for ear traits and 39 QTLs for kernel traits) based on single environment analysis. The joint-environment analysis detected 25 QTLs under well-watered environments (eight QTLs for ear length, eight for ear diameter, one for ear weight, two for kernel weight per ear, and six for 100-kernel weight), and nine QTLs under water-stressed environments (two QTLs for ear length, three for ear diameter, one for ear weight, one for kernel weight, and two for 100-kernel weight). Among these joint-environment QTLs, one common QTL (qEL5) was stably identified at both of the water regimes. Meanwhile, two main-effect QTLs were detected in the well-watered environments, i.e. qEL10 for ear length and qHKW2 for 100-kernel weight. Also, qED8, qEW8, and qKW8 were found to be located in the same interval of Chr. 8. Similarly, qEL4s and qKW4s were found to be located in the same interval under water-stressed environments. These genomic regions could be candidate targets for further fine mapping and marker-assisted breeding in maize

Multiple Quantitative Trait Analysis Using Bayesian Networks

Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this paper we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP), and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.Comment: 28 pages, 1 figure, code at http://www.bnlearn.com/research/genetics1

Benchmarking of univariate pleiotropy detection methods, with an application to epilepsy phenotypes

Over the past decades, various methods have been used to scan the human genome to identify genetic variations associated with diseases, in particular with common, complex disorders. One of such approaches is the genome-wide association study (GWAS), which compares genetic variation between affected and healthy individuals to find genomic variants in the DNA sequence associated with a trait. GWAS are usually conducted separately for individual traits, and the same single nucleotide polymorphisms (SNP)/loci are associated with different traits in independent studies 7-10. These findings buttress the knowledge that most complex traits are correlated and have shared genetic architecture, therefore, sharing the same heritable risk factors11. Knowledge of the genetic risk factors can directly or indirectly contribute to improvements in risk assessment, drug target development, and ultimately in providing effective therapies to the affected individuals. Pleiotropy is the phenomenon of a hereditary unit affecting more than one trait, and the earliest reported evidence was provided by Mendel when he noted that some set of features were always observed together in a plant. Although this example could have been purely due to linkage and could be regarded as spurious pleiotropy in recent times, it opened up more discussion and research into pleiotropy, which has since been an active area of research12. In this work, I focused on complex epilepsies and the overlap in the genetic factors impacting their phenotypes. Epilepsy is a brain disorder comprising monogenic and common/complex forms characterized by recurrent partial or generalized seizures. However, the extent to which genetic variants contribute to the disorder and how much of the genetic contribution is shared between the different phenotypes is not yet fully understood. This motivated this project, where I benchmarked available pleiotropy detection approaches to select the best performing method in terms of power and false-positive rate to detect true pleiotropy. Then, I applied the selected method to summary statistics of focal epilepsy (FE) and genetic generalized epilepsy (GGE), provided by the International League Against Epilepsy Consortium (ILAE) on complex epilepsies and the EPI25 collaborative, to identify shared genetic factors in both phenotypes of epilepsy. Identifying pleiotropic SNPs or genes is an active area of research with multiple proposed approaches, broadly categorized into univariate and multivariate methods. Multivariate approaches have the limitation that they require all phenotypes to be measured in the same individual and their corresponding genotype data provided, which is often not the case since GWAS are usually performed per specific trait. However, various consortia studying complex traits readily share the summary statistics (effect sizes and p-values) from genome-wide association studies, making it easier to apply univariate pleiotropy detection approaches that combine these statistics to identify SNPs or loci with a concordant or discordant direction of effects. Therefore, in this project, I first compared the relative power and false-positive rate (FPR) performance of five univariate pleiotropy detection approaches, classic meta-analysis, cFDR, PLACO, ASSET, and CPBayes (see section 6.1), through simulation studies. After that, I applied the best-performing method to the analysis of phenotypes of epilepsy using actual data. The data simulation procedure was performed in 3 steps. First, a population of 1 million individuals of European ancestry was simulated via resampling using the HAPGEN2 software13 and haplotypes of central Europeans from the 1000 genomes project14. In the second phase of the simulation, disease SNPs were randomly selected and used for the additive liability threshold model (ALTM)15 to simulate multifactorial disease phenotypes from the simulated genetic data. As expected, the performance of the methods varied in terms of power and false positive rate (FPR). The variability between the methods is higher for FPR, while most methods are comparable in terms of power, especially for larger sample sizes and RR. Although the classical meta-analysis is very powerful, it is also riddled with a very high false-positive rate, making it less suitable for identifying pleiotropic loci. While all the methods performed well in terms of power, the ASSET method gave a better trade-off between power and FPR for the different simulation approaches. Applying ASSET to the two phenotypes of epilepsy, GGE and FE, resulted in identifying a new putative locus 17q21.32 while replicating locus 2q24.3, previously reported by the ILAE consortium 16. Further, applying the ASSET method to summary statistics of larger samples of epilepsy phenotypes resulted in the identification of loci 2q24.3 and 9q21.13. These findings corroborate the result obtained by the ILAE consortium through mega and meta-analysis. Classical meta-analysis (MA) is not recommended for pleiotropy detection, based on the simulation study results. Though MA demonstrated good power to detect pleiotropy, it also recorded high FPR across all simulation scenarios. However, the ASSET method is highly recommended as it kept the FPR low while demonstrating good power to detect pleiotropy. This study also contributed three new pleiotropic loci (2q24.3, 17q21.32, and 9q21.13) to understanding the relationship of genetic variation with epilepsy phenotypes and the inter-relationship between these phenotypes. Although the locus 17q21.32 could not be replicated in the larger sample set, it is not necessarily a false positive discovery. The locus was genome-wide significant for GGE but marginally significant for FE, which confirmed the trend observed in the FE cases in the EPI25 collaborative dataset, where no genome-wide significance result was found. Therefore, replication in an independent sample is desirable. One limitation of using the univariate pleiotropy detection approaches as seen with the classical MA is that one trait with a very low P-value could drive the observed pleiotropic association. Also, methods like cFDR and PLACO could only accommodate two traits, though this was not a challenge in this project. Despite these limitations, the presented work established a benchmark of the relative performance of the assessed methods and could also guide researchers in related fields in their future work. This study also contributed to understanding the shared genetic factors between GGE and FE with the expectation that larger sample sizes will lead to more discoveries

Functionally informed fine-mapping and polygenic localization of complex trait heritability

Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome-not just genome-wide-significant loci-to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well calibrated and identified >20% more variants with a posterior causal probability >0.95 than identified in their nonfunctionally informed counterparts. In analyses of 49 UK Biobank traits (average n = 318,000), PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement versus SuSiE. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures. PolyFun is a computationally scalable framework for functionally informed fine-mapping that makes full use of genome-wide data. It prioritizes more variants than previous methods when applied to 49 complex traits from UK Biobank.Peer reviewe

Molecular genetics of chicken egg quality

Faultless quality in eggs is important in all production steps, from chicken to packaging, transportation, storage, and finally to the consumer. The egg industry (specifically transportation and packing) is interested in robustness, the consumer in safety and taste, and the chicken itself in the reproductive performance of the egg. High quality is commercially profitable, and egg quality is currently one of the key traits in breeding goals. In conventional breeding schemes, the more traits that are included in a selection index, the slower the rate of genetic progress for all the traits will be. The unveiling of the genes underlying the traits, and subsequent utilization of this genomic information in practical breeding, would enhance the selection progress, especially with traits of low inheritance, genderconfined traits, or traits which are difficult to assess. In this study, two experimental mapping populations were used to identify quantitative trait loci (QTL) of egg quality traits. A whole genome scan was conducted in both populations with different sets of microsatellite markers. Phenotypic observations of albumen quality, internal inclusions, egg taint, egg shell quality traits, and production traits during the entire production period were collected. To study the presence of QTL, a multiple marker linear regression was used. Polymorphisms found in candidate genes were used as SNP (single nucleotide polymorphism) markers to refine the map position of QTL by linkage and association. Furthermore, independent commercial egg layer lines were utilized to confirm some of the associations. Albumen quality, the incidence of internal inclusions, and egg taint were first mapped with the whole genome scan and fine-mapped with subsequent analyses. In albumen quality, two distinct QTL areas were found on chromosome 2. Vimentin, a gene maintaining the mechanical integrity of the cells, was studied as a candidate gene. Neither sequencing nor subsequent analysis using SNP within the gene in the QTL analysis suggested that variation in this gene could explain the effect on albumen thinning. The same mapping approach was used to study the incidence of internal inclusions, specifically, blood and meat spots. Linkage analysis revealed one genome-wide significant region on chromosome Z. Fine-mapping exposed that the QTL overlapped with a tight junction protein gene ZO-2, and a microsatellite marker inside the gene. Sequencing of a fragment of the gene revealed several SNPs. Two novel SNPs were found to be located in a miRNA (gga-mir-1556) within the ZO-2. MicroRNA-SNP and an exonic synonymous SNP were genotyped in the populations and showed significant association to blood and meat spots. A good congruence between the experimental population and commercial breeds was achieved both in QTL locations and in association results. As a conclusion, ZO-2 and gga-mir-1556 remained candidates for having a role in susceptibility to blood and meat spot defects across populations. This is the first report of QTL affecting blood and meat spot frequency in chicken eggs, albeit the effect explained only 2 % of the phenotypic variance. Fishy taint is a disorder, which is a characteristic of brown layer lines. Marker-trait association analyses of pooled samples indicated that egg-taint and the FMO3 gene map to chicken chromosome 8 and that the variation found by sequencing in the chicken FMO3 gene was associated with the TMA content of the egg. The missense mutation in the FMO3 changes an evolutionary, highly conserved amino acid within the FMO-characteristic motif (FATGY). In conclusion, several QTL regions affecting egg quality traits were successfully detected. Some of the QTL findings, such as albumen quality, remained at the level of wide chromosomal regions. For some QTL, a putative causative gene was indicated: miRNA gga-mir-1556 and/or its host gene ZO-2 might have a role in susceptibility to blood and meat spot defects across populations. Nonetheless, fishy taint in chicken eggs was found to be caused with a substitution within a conserved motif of the FMO3 gene. This variation has been used in a breeding program to eliminate fishy-taint defects from commercial egg layer lines. Objective The objective of this thesis was to map loci affecting economically important egg quality traits in chickens and to increase knowledge of the molecular genetics of these complex traits. The aim was to find markers linked to the egg quality traits, and finally unravel the variation in the genes underlying the phenotypic variation of internal egg quality. QTL mapping methodology was used to identify chromosomal regions affecting various production and egg quality traits (I, III, IV). Three internal egg quality traits were selected for fine-mapping (II, III, IV). Some of the results were verified in independent mapping populations and present-day commercial lines (III, IV). The ultimate objective was to find markers to be applied in commercial selection programs

Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program

One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations