Genome Association Studies Research Articles

Use of Polygenic Risk Scores for Coronary Heart Disease in Ancestrally Diverse Populations.

A polygenic risk score (PRS) is a measure of genetic liability to a disease and is typically normally distributed in a population. Individuals in the upper tail of thisdistribution often have relative risk equivalent to that of monogenic form of the disease. The majority of currently available PRSs for coronary heart disease (CHD) have been generated from cohorts of European ancestry (EUR) and vary in their applicability to other ancestry groups. In this report, we review the performance of PRSs for CHD across different ancestries and efforts to reduce variability in performance including novel population and statistical genetics approaches. PRSs for CHD perform robustly in EUR populations but lag in performance in non-EUR groups, particularly individuals of African ancestry. Several large consortia have been established to enable genomic studies in diverse ancestry groups and develop methods to improve PRS performance in multi-ancestry contexts as well as admixed individuals. These include fine-mapping to ascertain causal variants, trans ancestry meta-analyses, and ancestry deconvolution in admixed individuals. PRSs are being used in the clinical setting but enthusiasm has been tempered by the variable performance in non-EUR ancestry groups. Increasing diversity in genomic association studies and continued innovation in methodological approaches are needed to improve PRS performance in non-EUR individuals for equitable implementation of genomic medicine.

From Mendel to quantitative genetics in the genome era: the scientific legacy of W. G. Hill.

The quantitative geneticist W. G. ('Bill') Hill, awardee of the 2018 Darwin Medal of the Royal Society and the 2019 Mendel Medal of the Genetics Society (United Kingdom), died on 17 December 2021 at the age of 81 years. Here, we pay tribute to his multiple key scientific contributions, which span population and evolutionary genetics, animal and plant breeding and human genetics. We discuss his theoretical research on the role of linkage disequilibrium (LD) and mutational variance in the response to selection, the origin of the widely used LD metric r2 in genomic association studies, the genetic architecture of complex traits, the quantification of the variation in realized relationships given a pedigree relationship and much more. We demonstrate that basic theoretical research in quantitative and statistical genetics has led to profound insights into the genetics and evolution of complex traits and made predictions that were subsequently empirically validated, often decades later.

Correlation between Genomic Variants and Worldwide Epidemiology of Prostate Cancer.

Prostate cancer (PCa) incidence and mortality vary across territories and populations. This can be explained by the genetic factor of this disease. This article aims to correlate the epidemiological data, worldwide incidence, and mortality of PCa with single-nucleotide polymorphisms (SNPs) associated with the susceptibility and severity of this neoplasm in different populations. Eighty-four genetic variants associated with prostate cancer susceptibility were selected from the literature through genome association studies (GWAS). Allele frequencies were obtained from the 1000 Genomes Project, and epidemiological data were obtained from Surveillance, Epidemiology, and End Results (SEER). The PCa incidence, mortality rates, and allele frequencies of variants were evaluated by Pearson’s correlation. Our study demonstrated that 12 SNPs (rs2961144, rs1048169, rs7000448, rs4430796, rs2066827, rs12500426, rs6983267, rs11649743, rs2075110, rs114798100, rs855723, and rs2075109) were correlated with epidemiological data in different ethnic groups. Ten SNPs (rs2961144, rs1048169, rs7000448, rs4430796, rs2066827, rs12500426, rs11649743, rs2075110, rs114798100, and rs2075109) were positively correlated with the mortality rate. Seven SNPs (rs1048169, rs2961144, rs7000448, rs4430796, rs2066827, rs12500426, and rs114798100) were positively correlated with incidence. Positive correlations of incidence and mortality rates were more frequent in the African population. The genetic variants investigated here are likely to predispose to PCa and could play a role in its progression and aggressiveness. This genetic study demonstrated here is promising for implementing personalized strategies to screen for prostate cancer in diverse populations.

Integrated COVID-19 Predictor: Differential expression analysis to reveal potential biomarkers and prediction of coronavirus using RNA-Seq profile data

BackgroundThe world has been battling the continuous COVID-19 pandemic spread by the SARS-CoV-2 virus for last two years. The issue of viral disease prediction is constantly a matter of interest in virology and the study of disease transmission over the long years. ObjectiveIn this study, we aimed to implement genome association studies using RNA-Seq of COVID-19 and reveal highly expressed gene biomarkers and prediction based on the machine learning model of COVID-19 analysis to combat this pandemic. MethodWe collected RNA-Seq gene count data for both healthy (Control) and non-healthy (Treated) COVID-19 cases. In this experiment, a sequence of bioinformatics strategies and statistical techniques, such as fold-change and adjusted p-value, were processed to identify differentially expressed genes (DEGs). We filtered biomarker sets of high DEGs, moderate DEGs, and low DEGs using DESeq2, Limma Trend, and Limma Voom methods based on intersection and union operations and applied machine learning techniques to predict COVID-19. ResultThrough experimental analysis, 67 potential biomarkers were extracted, comprising 49 up-regulated and 18 down-regulated genes, using statistical techniques and a set-theory consensus strategy. We trained the machine learning models on 12 different biomarker sets and found that the SVM model performed better than the other classifiers with 99.07% classification accuracy for moderate DEGs. ConclusionOur study revealed that identified differentially expressed genes of the moderate DEGs biomarker set, |log2FC| ≥ 2 with adjusted p-value < 0.05, work significantly as input features to implement a machine learning model using a kernel-based SVM technique to predict COVID-19.

Plasma Proteomic Signature of Cellular Senescence and Markers of Biological Aging Among Postmenopausal Women.

We aimed to investigate the association of circulatory senescence-associated secretory phenotypes (SASPs) produced by senescent cells with chronological and menopausal age in women aged 45 years or more. The proteomic profiles for 32 SASP factors of plasma samples were measured in 76 healthy postmenopausal women aged 46-82 years from the Korean Genome and Epidemiology Study Cardiovascular Disease Association Study (KoGES-CAVAS). We assessed the association between the SASP factors and aging indicators (chronological age, menopausal age, and years since menopause) using single- and multiprotein models. First, we composed a profile of proteins associated with chronological age, menopausal age, and years since menopause. In a single-protein model, three proteins (growth differentiation factor 15 [GDF15], insulin-like growth factor binding protein-2 [IGFBP-2], and tumor necrosis factor-α [TNF-α]) are positively associated with chronological age. Menopausal age and years since menopause are interrelated with interleukin-8 (IL-8). The direction of association between menopausal age and monocyte chemoattractant protein-1 (MCP-1) was only negative, and IGFBP-2 and TNF-α were significant in all three aging factors. We also constructed parsimonious multiprotein models to confirm the association of the proteomic signature for aging after adjusting for covariates and the combination of proteomic signature of 13 proteins (GDF15, interferon-γ [IFN-γ], IGFBP-2, IGFBP-7, IL-15, IL-1β, IL-17A, IL-8, MCP-1, tissue inhibitors of metalloproteinase-2 [TIMP-2], TNF-α, vascular endothelial growth factor-A [VEGF-A], and interferon-inducible protein 10 [IP-10]) appear to be associated with chronological age and menopausal state of individuals. Thus, by observing association between the selected SASPs and age-related markers among healthy postmenopausal women, we examine how menopause in women relates to proteomic indicators of aging and highlight the potential use of SASP factors as a marker to reflect the state of biological aging attributed by ovarian senescence.

An integrated genome and phenome-wide association study approach to understanding Alzheimer's disease predisposition

Genome-wide association studies (GWAS) have identified common single nucleotide polymorphisms (SNPs) that increase late-onset Alzheimer's disease (LOAD) risk. To identify additional LOAD-associated variants and provide insight into underlying disease biology, we performed a phenome-wide association study on 23 known LOAD-associated SNPs and 4:1 matched control SNPs using UK Biobank data. LOAD-associated SNPs were significantly enriched for associations with 8/778 queried traits, including 3 platelet traits. The strongest enrichment was for platelet distribution width (PDW) (p = 1.2 × 10−5), but increased PDW was not associated with LOAD susceptibility in Mendelian randomization analysis. Of 384 PDW-associated SNPs identified by prior GWAS, 36 were nominally associated with LOAD risk (17,008 cases; 37,154 controls) and 5 survived false-discovery rate correction. Associations confirmed known LOAD risk loci near PICALM, CD2AP, SPI1, and NDUFAF6, and identified a novel risk locus in epidermal growth factor receptor. Integrating GWAS and phenome-wide association study data reveals substantial pleiotropy between genetic determinants of LOAD and of platelet morphology, and for the first time implicates epidermal growth factor receptor – a mediator of β-amyloid toxicity – in Alzheimer's disease susceptibility.

Abstract A018: Lung cancer associated DNA Damageome reveals a genome destabilizing role of Aquaporin 3

Abstract Lung cancer is a multifactorial disease caused by both environmental exposures and genetic factors. Genome and transcriptome-wide association studies (GWAS and TWAS) successfully identified many genetic variants and candidate causal genes associated with lung cancer. However, the lack of functional studies of these genes remains a major bottleneck to clinical translation. Recently, we discovered that large networks of evolutionarily conserved DNA damageome proteins (DDPs) promote DNA damage and genome instability. Here, we discovered a lung cancer-associated DNA damageome network. We further discovered that one of the lung cancer DDPs, Aquaporin 3 (AQP3), can promote ROS dependent DNA damage upon overproduction. Furthermore, it potentiates DNA damage by a low dose of arsenic exposure, creating unique double strand break hotspots which help predict mutational signatures. We screened for high endogenous DNA damage levels for more than 40 TWAS and GWAS-nominated candidates using high-throughput single cell-based flow cytometric assays. We optimized and performed Mre11-Cut&amp;Tag (Cleavage Under Targets and Tagmentation) to map double strand breaks, which are the most deleterious form of DNA damage. In addition, we used END-Seq to provide a much higher resolution of the DSB map and uncovered hotspots in arsenic-detoxifying genes. We are implementing duplex sequencing to map the mutational signatures caused by arsenic and the arsenic-Aquaporin 3 interaction. Our study brings function to endogenous DNA damage and DNA damageome proteins that interact with environmental toxicants. Mechanistic insights into how low-dose arsenic interacts with risk genes yield critical knowledge for the prevention, diagnosis, and treatment of arsenic-associated diseases. We also uncovered several early biomarkers to potentially predict the long-term health impacts of arsenic. Citation Format: Jun Xia, Mu Lin, Zhuoyi Song, Gail Fernandes, Susan Rosenberg, Chris Amos. Lung cancer associated DNA Damageome reveals a genome destabilizing role of Aquaporin 3 [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A018.

Genome-Wide Genomic and Functional Association Study for Workability and Calving Traits in Holstein Cattle.

Simple SummaryTraits related to the calving abilities and workability of cows are essential both for the dairy economy and for the welfare of cows. We analysed the direct and maternal components of calving ease, direct and maternal components of stillbirth, milking speed, and temperament by looking for SNPs, gene ontologies, and metabolic pathways significantly associated with the additive genetic variability of those traits. At the genetic level, 150 significant SNPs were identified whereas at the functional level we found 45 significant gene ontology terms and 5 metabolic pathways.The goal of our study was to identify the SNPs, metabolic pathways (KEGG), and gene ontology (GO) terms significantly associated with calving and workability traits in dairy cattle. We analysed direct (DCE) and maternal (MCE) calving ease, direct (DSB) and maternal (MSB) stillbirth, milking speed (MSP), and temperament (TEM) based on a Holstein-Friesian dairy cattle population consisting of 35,203 individuals. The number of animals, depending on the trait, ranged from 22,301 bulls for TEM to 30,603 for DCE. We estimated the SNP effects (based on 46,216 polymorphisms from Illumina BovineSNP50 BeadChip Version 2) using a multi-SNP mixed model. The SNP positions were mapped to genes and the GO terms/KEGG pathways of the corresponding genes were assigned. The estimation of the GO term/KEGG pathway effects was based on a mixed model using the SNP effects as dependent variables. The number of significant SNPs comprised 59 for DCE, 25 for DSB and MSP, 17 for MCE and MSB, and 7 for TEM. Significant KEGG pathways were found for MSB (2), TEM (2), and MSP (1) and 11 GO terms were significant for MSP, 10 for DCE, 8 for DSB and TEM, 5 for MCE, and 3 for MSB. From the perspective of a better understanding of the genomic background of the phenotypes, traits with low heritabilities suggest that the focus should be moved from single genes to the metabolic pathways or gene ontologies significant for the phenotype.

Abstract TP179: Candidate Genes For Small Vessel Ischemic Stroke: A Gwas Pilot Study

Background: The genetic basis of small vessel stroke (SVS) is poorly understood. Few genetic loci have been identified that may provide insights into SVS pathogenesis. We sought to identify genetic associations with incident SVS. Methods: A GWAS analysis was performed on a prospective cohort of patients enrolled in the American Stroke Association- Bugher Small Vessel Intracranial Disease Whole Genome Association Study which included patients from 4 hospitals in North Carolina recruited between December 2007-August 2012. Patients with SVS in the prior 2 years confirmed clinically and on neuroimaging were compared to a control group who had no previous history of stroke. A logistic regression model with SVS as the outcome and single nucleotide polymorphisms (SNPs) as the main predictor was performed and adjusted for baseline demographics and clinical characteristics, including age, race/ethnicity, sex, tobacco use, history of hypertension, diabetes, hyperlipidemia, atrial fibrillation, history of stroke/TIA, and principal components. Because of the relatively small sample size, SNPs with p-values &lt;1E-4 were considered significant. Results: The cohort included 139 patients who had SVS and 64 controls. Among this cohort, 50% were men and 38% African-American. The mean age of the study group was 63-years. Sixteen SNPs in 9 genes met the statistical cutoff for association with SVS. These were involved in extracellular matrix integrity (CHD23, CLDN14, SPTBN1), neuronal differentiation (DSCAML1), cholesterol transport (GRAMD1B), transcriptional regulation (JARID2, LINC02111, LINC01993) and voltage-gated potassium channel (KCND2). Conclusion: In this pilot GWAS study, several loci variations were associated with SVS. Although this study is hypothesis generating, previous studies have also shown an association of SVS with genes involved in extracellular matrix integrity. In addition, we found it interesting that SNPs in both CLDN14 and CDH23 genes, which have been linked to non-syndromic sensorineural deafness, are also more common in SVS. Some forms of sensorineural deafness have been associated with cognitive impairment and stroke, and if a genetic association is confirmed, it would suggest a common pathophysiology may link the two conditions.

Sex differences in white adipose tissue expansion: emerging molecular mechanisms.

The escalating prevalence of individuals becoming overweight and obese is a rapidly rising global health problem, placing an enormous burden on health and economic systems worldwide. Whilst obesity has well described lifestyle drivers, there is also a significant and poorly understood component that is regulated by genetics. Furthermore, there is clear evidence for sexual dimorphism in obesity, where overall risk, degree, subtype and potential complications arising from obesity all differ between males and females. The molecular mechanisms that dictate these sex differences remain mostly uncharacterised. Many studies have demonstrated that this dimorphism is unable to be solely explained by changes in hormones and their nuclear receptors alone, and instead manifests from coordinated and highly regulated gene networks, both during development and throughout life. As we acquire more knowledge in this area from approaches such as large-scale genomic association studies, the more we appreciate the true complexity and heterogeneity of obesity. Nevertheless, over the past two decades, researchers have made enormous progress in this field, and some consistent and robust mechanisms continue to be established. In this review, we will discuss some of the proposed mechanisms underlying sexual dimorphism in obesity, and discuss some of the key regulators that influence this phenomenon.

Effects of Maternal Nutrition on Female Offspring Weight Gain and Sexual Development.

Maternal nutrition during pregnancy influences postnatal life of animals; nevertheless, few studies have investigated its effects on the productive performance and reproductive development of heifers. This study evaluated the performance, reproductive development, and correlation between reproduction × fat thickness and performance × ribeye area (REA) traits of heifers. We also performed an exploratory genomic association during the rearing period in heifers submitted to fetal programming. The study comprised 55 Nellore heifers born to dams exposed to one of the following nutritional planes: control, without protein-energy supplementation; PELT, protein-energy last trimester, protein-energy supplementation offered in the final third of pregnancy; and PEWG, protein-energy whole gestation, protein-energy supplementation upon pregnancy confirmation. Protein-energy supplementation occurred at the level of 0.3% live weight. After weaning, heifers were submitted to periodic evaluations of weight and body composition by ultrasonography. From 12 to 18 months, we evaluated the reproductive tract of heifers to monitor its development for sexual precocity and ovarian follicle population. The treatments had no effect (p > 0.05) on average daily gain; however, the weight of the animals showed a significant difference over time (p = 0.017). No differences were found between treatments for REA, backfat, and rump fat thickness, nor for puberty age, antral follicular count, and other traits related to reproductive tract development (p > 0.05). The correlation analysis between performance traits and REA showed high correlations (r > 0.37) between REA at weaning and year versus weight from weaning until yearling; however, no correlation was found for reproductive development traits versus fat thickness (p > 0.05). The exploratory genomic association study showed one single-nucleotide polymorphism (SNP) for each treatment on an intergenic region for control and PEWG, and the one for PELT on an intronic region of RAPGEF1 gene. Maternal nutrition affected only the weight of the animals throughout the rearing period.

The Contribution of Copy Number Variants and Single Nucleotide Polymorphisms to the Additive Genetic Variance of Carcass Traits in Cattle.

The relative contributions of both copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) to the additive genetic variance of carcass traits in cattle is not well understood. A detailed understanding of the relative importance of CNVs in cattle may have implications for study design of both genomic predictions and genome-wide association studies. The first objective of the present study was to quantify the relative contributions of CNV data and SNP genotype data to the additive genetic variance of carcass weight, fat, and conformation for 945 Charolais, 923 Holstein-Friesian, and 974 Limousin sires. The second objective was to jointly consider SNP and CNV data in a least absolute selection and shrinkage operator (LASSO) regression model to identify genomic regions associated with carcass weight, fat, and conformation within each of the three breeds separately. A genomic relationship matrix (GRM) based on just CNV data did not capture any variance in the three carcass traits when jointly evaluated with a SNP-derived GRM. In the LASSO regression analysis, a total of 987 SNPs and 18 CNVs were associated with at least one of the three carcass traits in at least one of the three breeds. The quantitative trait loci (QTLs) corresponding to the associated SNPs and CNVs overlapped with several candidate genes including previously reported candidate genes such as MSTN and RSAD2, and several potential novel candidate genes such as ACTN2 and THOC1. The results of the LASSO regression analysis demonstrated that CNVs can be used to detect associations with carcass traits which were not detected using the set of SNPs available in the present study. Therefore, the CNVs and SNPs available in the present study were not redundant forms of genomic data.

Systematic Review of Genomic Associations with Blood Pressure and Hypertension in Populations with African-Ancestry.

Background: Despite hypertension being highly prevalent in individuals with African-ancestry, they are under-represented in large genome-wide association studies. Inclusion of African participants is essential to better understand genetic associations with blood pressure-related traits in Africans. This systematic review critically evaluates existing studies with African-ancestry participants and identifies knowledge gaps. Methods: We followed the PRISMA protocol, HuGE Review handbook to identify literature on original research, in English, on genetic association studies for blood pressure-related traits (systolic and diastolic blood pressure, pulse and mean-arterial pressure, and hypertension) in populations with African-ancestry (January 2007 to April 2020). A narrative synthesis of the evidence was conducted. Results: Twelve studies with African-ancestry participants met the eligibility criteria, within which 10 studies met the additional genetic association data criteria (i.e., reporting only on African-ancestry participants). Across the five blood pressure-related traits, 26 genome-wide significantly associated SNPs were identified, with six SNPs linked to more than one trait, illustrating pleiotropic effects. Among the SNP associations, 12 had not previously been described in non-African studies. Discussion: The limited number of relevant studies highlights the dearth of genomic association studies on participants with African-ancestry, especially those located within Africa. Variations in study methodology, participant inclusion, adjustment for covariates (e.g., antihypertensive medication) and relatively small sample sizes make comparisons challenging, and have resulted in fewer significant associations, compared to large European studies. Regional variation in the prevalence and associated risk factors of hypertension across Africa makes a compelling argument to develop African cohorts to facilitate large genomic studies, using African-centric arrays. Data harmonisation and comparable study designs, such as described in the H3Africa CHAIR initiative, provide a good example toward achieving this goal. Other relevant information: SS and J-TB were funded by the South African National Research Foundation. MR is a South African Research Chair in Genomics and Bioinformatics of African populations hosted by the University of the Witwatersrand, funded by the Department of Science and Innovation, and administered by the NRF. This review was registered at PROSPERO (registration number: CRD42020179221) and OSF (registration DOI: 10.17605/OSF.IO/QT2HA).

Host genetic factors determining COVID-19 susceptibility and severity.

The COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) poses an unprecedented challenge to humanity. SARS-CoV-2 infections range from asymptomatic to severe courses of COVID-19 with acute respiratory distress syndrome (ARDS), multiorgan involvement and death. Risk factors for disease severity include older age, male sex, increased BMI and pre-existing comorbidities. Ethnicity is also relevant to COVID-19 susceptibility and severity. Host genetic predisposition to COVID-19 is now increasingly recognized and whole genome and candidate gene association studies regarding COVID-19 susceptibility have been performed. Several common and rare variants in genes related to inflammation or immune responses have been identified. We summarize research on COVID-19 host genetics and compile genetic variants associated with susceptibility to COVID-19 and disease severity. We discuss candidate genes that should be investigated further to understand such associations and provide insights relevant to pathogenesis, risk classification, therapy response, precision medicine, and drug repurposing.

Genomics landscape of 185 Streptococcus thermophilus and identification of fermentation biomarkers

Streptococcus (S.) thermophilus, an indispensable dairy starter, has been used in autochthonous as well as industrial milk fermentation. However, the genetic architecture underlying S. thermophilus traits and phenotypes is largely unknown. Here, we sequenced 185 S. thermophilus strains, isolated from natural fermented dairy products of China and Mongolia and used comparative genomic and genome wide association study to provide novel point for genetic architecture underlying its traits and phenotypes. Genome analysis of S. thermophilus showed association of phylogeny with environmental and phenotypic features and revealed clades with high acid production potential or with substantial genome decay. A few S. thermophilus isolated from areas with high chloramphenicol emissions had a chloramphenicol-resistant gene CatB8. Most importantly, we defined a growth score and identified a missense mutation G1118698T located at the gene AcnA that were both predictive of acidification capability of S. thermophilus. Our findings provide novel insight in S. thermophilus genetic traits, antibiotic resistant and predictive of acidification capability which both may had huge help in culture starter screening.

A Genome‐First Approach to Rare Variants in Dominant Postlingual Hearing Loss Genes in a Large Adult Population

ObjectiveTo investigate the importance of rare variants in adult‐onset hearing loss.Study DesignGenomic association study.SettingLarge biobank from tertiary care center.MethodsWe investigated rare variants (minor allele frequency &lt;5%) in 42 autosomal dominant (DFNA) postlingual hearing loss (HL) genes in 16,657 unselected individuals in the Penn Medicine Biobank. We determined the prevalence of known pathogenic and predicted deleterious variants in subjects with audiometric‐proven sensorineural hearing loss. We scanned across known postlingual DFNA HL genes to determine those most significantly contributing to the phenotype. We replicated findings in an independent cohort (UK Biobank).ResultsWhile rare individually, when considering the accumulation of variants in all postlingual DFNA genes, more than 90% of participants carried at least 1 rare variant. Rare variants predicted to be deleterious were enriched in adults with audiometric‐proven hearing loss (pure‐tone average &gt;25 dB; P =. 015). Patients with a rare predicted deleterious variant had an odds ratio of 1.27 for HL compared with genotypic controls (P =. 029). Gene burden in DIABLO, PTPRQ, TJP2, and POU4F3 were independently associated with sensorineural hearing loss.ConclusionAlthough prior reports have focused on common variants, we find that rare predicted deleterious variants in DFNA postlingual HL genes are enriched in patients with adult‐onset HL in a large health care system population. We show the value of investigating rare variants to uncover hearing loss phenotypes related to implicated genes.

Unraveling the Complexities of the Lipoprotein(a) Particle and its Metabolism

Lipoprotein (a) [Lp(a)], was discovered more than 60 years ago, however, its link to human disease has just begun to unfold. Recent findings in large genome association studies and Mendelian randomization studies have highlighted its role as a causal risk factor in coronary and atherogenic vascular disease. Despite years of research into the mechanisms regulating plasma levels of Lp(a), specific sites and pathways of regulation are poorly characterized. Lp(a) contains two main apolipoproteins, apolipoprotein (a) [apo(a)] and apolipoprotein B100 (apoB100), these are bound by covalent bounds but also weak lisine binding sites are present. The most significant determinant of the level of Lp(a) is the number of DNA-base repeats coding a highly polymorphic sequence of the LPA gene called Kringle IV Type 2 (KIV-2). Importantly, the number of KIV-2 repeats range from about 3-40 resulting in a range of apo(a) mass from 200 – 700 kDA. Almost all individuals, therefore, have two apo(a) isoforms that differ in the size and the size of the apoB bound to the apo(a) has not been well characterize. The mechanism(s) by which KIV-2 isoform size affect Lp(a) concentrations remain minimally defined. Previous studies, using isolated and exogenously radio-iodinated apo(a) isoforms, indicated that the hepatic secretion rate (SR) of apo(a), rather than its fractional clearance rate (FCR), determined plasma levels of Lp(a), and that smaller isoforms (fewer KIV-2 repeats) were associated with greater SRs and higher plasma levels of Lp(a). However isoform size does not account for all the variation in plasma Lp(a) concentration. Indeed, more recent studies using stable isotopes by our group and by others provide a much more complex picture. We will present data on recently completed mechanistic studies using stable isotope in human volunteers with high and low levels of Lp(a). The studies describe the clearance and production of apo(a) and apoB100 from isolated Lp(a). Additionally proteomic composition of the isolated Lp(a) particles and isoforms and the implications of these proteins on disease will be discussed. The studies presented will highlight the complexity of Lp(a) and provide novel insights into the regulation of this cardiovascular risk.

Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study.

Population structure or genetic relatedness should be considered in genome association studies to avoid spurious association. The most used methods for genome-wide association studies (GWAS) account for population structure but are limited to genotyped individuals with phenotypes. Single-step GWAS (ssGWAS) can use phenotypes from non-genotyped relatives; however, its ability to account for population structure has not been explored. Here we investigate the equivalence among ssGWAS, efficient mixed-model association expedited (EMMAX), and genomic best linear unbiased prediction GWAS (GBLUP-GWAS), and how they differ from the single-SNP analysis without correction for population structure (SSA-NoCor). We used simulated, structured populations that mimicked fish, beef cattle, and dairy cattle populations with 1040, 5525, and 1,400 genotyped individuals, respectively. Larger populations were also simulated that had up to 10-fold more genotyped animals. The genomes were composed by 29 chromosomes, each harboring one QTN, and the number of simulated SNPs was 35,000 for the fish and 65,000 for the beef and dairy cattle populations. Males and females were genotyped in the fish and beef cattle populations, whereas only males had genotypes in the dairy population. Phenotypes for a trait with heritability varying from 0.25 to 0.35 were available in both sexes for the fish population, but only for females in the beef and dairy cattle populations. In the latter, phenotypes of daughters were projected into genotyped sires (i.e., deregressed proofs) before applying EMMAX and SSA-NoCor. Although SSA-NoCor had the largest number of true positive SNPs among the four methods, the number of false negatives was two–fivefold that of true positives. GBLUP-GWAS and EMMAX had a similar number of true positives, which was slightly smaller than in ssGWAS, although the difference was not significant. Additionally, no significant differences were observed when deregressed proofs were used as pseudo-phenotypes in EMMAX compared to daughter phenotypes in ssGWAS for the dairy cattle population. Single-step GWAS accounts for population structure and is a straightforward method for association analysis when only a fraction of the population is genotyped and/or when phenotypes are available on non-genotyped relatives.

Score tests for scale effects, with application togenomic analysis.

Tests for variance or scale effects due to covariates are used in many areas and recently, in genomic and genetic association studies. We study score tests based on location-scale models with arbitrary error distributions that allow incorporation of additional adjustment covariates. Tests based on Gaussian and Laplacian double generalized linear models are examined in some detail. Numerical properties of the tests under Gaussian and other error distributions are examined. Our results show that the use of model-based asymptotic distributions with score tests for scale effects does not control type 1 error well in many settings of practical relevance. We consider simple statistics based on permutation distribution approximations, which correspond to well-known statistics derived by another approach. They are shown to give good type 1 error control under different error distributions and under covariate distribution imbalance. The methods are illustrated through a differential gene expression analysis involving breast cancer tumor samples.

Direct Phenotyping and Principal Component Analysis of Type Traits Implicate Novel QTL in Bovine Mastitis through Genome-Wide Association.

Simple SummaryIt is well established that the physical conformation of a cow’s udder and teats may influence her susceptibility to mastitis, an inflammatory condition of the udder, which has 25% prevalence in the United States. Our aim was to improve the biological understanding of the genetics underlying mastitis by intensively characterizing cows for udder and teat conformation, including the novel traits of teat width and end shape, and directly associating those phenotypes with high-density genotypes for those exact same cows. We also generated a composite measure that accounts for multiple high-mastitis-risk udder and teat conformations in a single index for risk phenotypes. Using this approach, we identified novel genetic markers associated with udder and teat conformation, which may be good candidates for inclusion in national genetic evaluations for selection of mastitis-resistant cows. Mastitis is the costliest disease facing US dairy producers, and integrating genetic information regarding disease susceptibility into breeding programs may be an efficient way to mitigate economic loss, support the judicious use of antimicrobials, and improve animal welfare.Our objectives were to robustly characterize a cohort of Holstein cows for udder and teat type traits and perform high-density genome-wide association studies for those traits within the same group of animals, thereby improving the accuracy of the phenotypic measurements and genomic association study. Additionally, we sought to identify a novel udder and teat trait composite risk index to determine loci with potential pleiotropic effects related to mastitis. This approach was aimed at improving the biological understanding of the genetic factors influencing mastitis. Cows (N = 471) were genotyped on the Illumina BovineHD777k beadchip and scored for front and rear teat length, width, end shape, and placement; fore udder attachment; udder cleft; udder depth; rear udder height; and rear udder width. We used principal component analysis to create a single composite measure describing type traits previously linked to high odds of developing mastitis within our cohort of cows. Genome-wide associations were performed, and 28 genomic regions were significantly associated (Bonferroni-corrected p < 0.05). Interrogation of these genomic regions revealed a number of biologically plausible genes whicht may contribute to the development of mastitis and whose functions range from regulating cell proliferation to immune system signaling, including ZNF683, DHX9, CUX1, TNNT1, and SPRY1. Genetic investigation of the risk composite trait implicated a novel locus and candidate genes that have potentially pleiotropic effects related to mastitis.