Haplotype-based quantitative trait mapping using a clustering algorithm

BACKGROUND: With the availability of large-scale, high-density single-nucleotide polymorphism (SNP) markers, substantial effort has been made in identifying disease-causing genes using linkage disequilibrium (LD) mapping by haplotype analysis of unrelated individuals. In addition to complex diseases, many continuously distributed quantitative traits are of primary clinical and health significance. However the development of association mapping methods using unrelated individuals for quantitative traits has received relatively less attention. RESULTS: We recently developed an association mapping method for complex diseases by mining the sharing of haplotype segments (i.e., phased genotype pairs) in affected individuals that are rarely present in normal individuals. In this paper, we extend our previous work to address the problem of quantitative trait mapping from unrelated individuals. The method is non-parametric in nature, and statistical significance can be obtained by a permutation test. It can also be incorporated into the one-way ANCOVA (analysis of covariance) framework so that other factors and covariates can be easily incorporated. The effectiveness of the approach is demonstrated by extensive experimental studies using both simulated and real data sets. The results show that our haplotype-based approach is more robust than two statistical methods based on single markers: a single SNP association test (SSA) and the Mann-Whitney U-test (MWU). The algorithm has been incorporated into our existing software package called HapMiner, which is available from our website at . CONCLUSION: For QTL (quantitative trait loci) fine mapping, to identify QTNs (quantitative trait nucleotides) with realistic effects (the contribution of each QTN less than 10% of total variance of the trait), large samples sizes (≥ 500) are needed for all the methods. The overall performance of HapMiner is better than that of the other two methods. Its effectiveness further depends on other factors such as recombination rates and the density of typed SNPs. Haplotype-based methods might provide higher power than methods based on a single SNP when using tag SNPs selected from a small number of samples or some other sources (such as HapMap data). Rank-based statistics usually have much lower power, as shown in our study

Association of CDX1 binding site of periostin gene with bone mineral density and vertebral fracture risk

Summary Periostin (POSTN) as a regulator of osteoblast differentiation and bone formation may affect susceptibility to osteoporosis. This study suggests POSTN as a candidate gene for bone mineral density (BMD) variation and vertebral fracture risk, which could better our understanding about the genetic pathogenesis of osteoporosis and will be useful in clinic in the future. Introduction The genetic determination of osteoporosis is complex and ill-defined. Periostin (POSTN), an extracellular matrix secreted by osteoblasts and a regulator of osteoblast differentiation and bone formation, may affect susceptibility to osteoporosis. Methods We adopted a tag-single nucleotide polymorphism (SNP) based association method followed by imputationbased verification and identification of a causal variant. The association was investigated in 1,572 subjects with extremeBMD and replicated in an independent population of 2,509 subjects. BMD was measured by dual X-ray absorptiometry. Vertebral fractures were identified by assessing vertebral height from X-rays of the thoracolumbar spine. Association analyses were performed with PLINK toolset and imputation analyses with MACH software. The top imputation finding was subsequently validated by genotyping. Interactions between POSTN and another BMD-related candidate gene sclerostin (SOST) were analyzed using MDR program and validated by logistical regression analyses. The putative transcription factor binding with target sequence was confirmed by electrophoretic mobility shift assay (EMSA). Results Several SNPs of POSTN were associated with BMD or vertebral fractures. The most significant polymorphism was rs9547970, located at the -2,327bpupstream(P06.8×10-4)of POSTN. Carriers of the minor allele G per copy of rs9547970 had1.33higherriskofvertebralfracture(P00. 007). An interactive effect between POSTN and SOST upon BMD variation was suggested (P<0.01). A specific binding of CDX1 to the sequence of POSTN with the major allele A of rs9547970 but not the variant G allele was confirmed by EMSA. Conclusions Our results suggest POSTN as a candidate gene for BMD variation and vertebral fracture risk. © 2012 International Osteoporosis Foundation and National Osteoporosis Foundation.published_or_final_versionSpringer Open Choice, 28 May 201

Pleiotropic Relationships among Measures of Bone Mineral Density, Bone Geometry, Lean Muscle Mass and Fat Mass

Osteoporosis, sarcopenia and changes in fat distribution with age increase risk of fractures, affect quality of life, and are of major public health significance. Investigations into the genetic architecture of endophenotypes of these conditions could lead to better prediction of who is at greatest risk as well as revealing targets for therapies to delay disease onset or diminish their effects on afflicted individuals. Covariation among these conditions may be due to pleiotropy, although little is known about the specific genes involved. I explored relationships among twenty-two measures of arm and leg bone mineral density and geometry, arm and leg lean mass and arm and leg fat mass using data from two populations of Afro-Caribbeans from the island of Tobago: a sample of 1,937 unrelated men aged ≥ 40 years and a set of 470 men and women aged ≥ 18 years in seven extended pedigrees (mean family size = 67). I also performed genomewide association (GWA) studies of lumber spine and femoral neck bone mineral density (BMD) and fractures in an older (aged ≥ 70 years) population of European and African Americans (n = 1,663 and 1,139 respectively). Hierarchical and principal component (PC) analysis revealed three clusters: (1) a “geometry group” that comprises mostly bone geometry traits and lean mass (PC1); (2) a “density group” that comprises mostly BMD traits (PC2); and (3) a “fat mass group” that comprises measures of fat mass (PC3). Estimates of residual heritability ranged from 0.206 to 0.763 (p 3.3) for quantitative trait loci (QTLs) on two chromosomes: 10q for PC1 and tibial periosteal circumference and 21q for PC3 and arm fat mass. GWA analyses of BMD and fractures in European and African Americans revealed several dozen potential candidate loci with suggestive levels of significance (p ≤ 5 × 10⁻⁶), the most promising of which is SLC4A7 on 3p24.1, a sodium bicarbonate cotransporter expressed in osteoclasts. Thus, I present evidence for specific QTLs with pleiotropic effects on multiple body composition traits, as well as loci associated with areal BMD and fracture risk. Additional analyses of these regions could reveal genes that jointly influence susceptibility to osteoporosis, sarcopenia and obesity

Deciphering the genetic background of quantitative traits using machine learning and bioinformatics frameworks

In dieser Doktorarbeit habe ich zwei Ansätze verfolgt, mit denen genetische Mechanismen, welche quantitativen Merkmalen zugrunde liegen, aufgezeigt und bestimmt werden können. In diesem Zusammenhang lag mein Fokus auf der Entwicklung effizienter Methoden um Genotyp-Phänotyp Assoziationen zu identifizieren. Durch diese lassen sich im Weiteren regulatorische Mechanismen beschreiben, welche phänotypische Unterschiede zwischen Individuen verursachen. Im ersten Ansatz habe ich Schlüsselmechanismen der Genregulation untersucht, welche die Entwicklung der Bruchfestigkeit von Eierschalen steuern. Das Ziel war es zeitliche Unterschiede der Signalkaskaden, welche die Eierschalen Bruchfestigkeit im Verlauf eines Vogellebens regulieren, zu detektieren. Hierfür habe ich die Bruchfestigkeit zu zwei verschiedenen Zeitpunkten innerhalb eines Produktionszyklus betrachtet und die Genotyp-Phänotyp Assoziationen mithilfe eines Random Forest-Algorithmus bestimmt. Für die Analyse der entsprechenden Gene wurde ein etablierter systembiologischer Ansatz verfolgt, mit dem genregulatorische Pathways und Master-Regulatoren identifiziert werden konnten. Meine Ergebnisse zeigen, dass einige Pathways und Master-Regulatoren (z.B. Slc22a1 und Sox11) gleichzeitig in verschiedenen Legephasen identifiziert wurden, andere (z.B. Scn11a, St8sia2 oder der TGF-beta Pathway) speziell in lediglich einer Phase gefunden wurden. Sie stellen somit altersspezifische Mechanismen dar.Insgesamt liefern meine Ergebnisse (i) signifikante Einblicke in altersspezifische und allgemeine molekulare Mechanismen, welche die Eierschalen-Bruchfestigkeit regulieren und bestimmen; und (ii) neue Zuchtziele, um die Bruchstärke von Eierschalen vor allem in späteren Legephasen zu erhöhen und somit die Eierschalen Qualität zu verbessern. In meinem zweitem Ansatz, habe ich die Methode der Random Forests mit einer Strategie zur Signaldetektierung kombiniert, um robuste Genotyp-Phänotyp-Beziehungen zu identifizieren. Ziel dieses Ansatzes war die Verbesserung der Effizienz der Einzel-SNP basierten Assoziationsanalyse. Genomweite Assoziationsstudien (GWAS) sind ein weit verbreiteter Ansatz zur Identifikation genomischer Varianten und Genen, die verantwortlich sind für Merkmale, welche von Interesse sowohl für den akademischen als auch den wirtschaftlichen Sektor sind. Trotz des langjährigen Einsatzes verschiedener GWAS-Methoden stellt die zuverlässige Identifikation von Genotyp-Phänotyp-Beziehungen noch immer eine Herausforderung für viele quantitative Merkmale dar. Dies wird hauptsächlich durch die große Anzahl genomischer Loci begründet, welche lediglich einen schwachen Effekt auf das zu untersuchende Merkmal haben. Daher lässt sich Hypothese aufstellen, dass genomische Varianten, welche zwar einen geringen, aber dennoch realen Einfluss ausüben, in vielen GWAS-Ansätzen unentdeckt bleiben. Zur Behandlung dieser Unzulänglichkeiten wird in der Arbeit ein zweistufiges Verfahren verwendet. Zunächst werden kubische Splines für Teststatistiken und genomische Regionen angepasst. Die Spline-Maxima, welche höher als die zu erwartenden zufallsbasierten Maximalwerte ausfallen, werden als quantitative Merkmals-Loci (QTL) eingestuft. Anschließend werden die SNPs in diesen QTLs, basierend auf ihrer Assoziationsstärke mit den Phänotypen, durch einen Random Forests-Ansatz priorisiert. Im Rahmen einer Fallstudie haben wir unseren Ansatz auf reale Datensätze angewendet und eine plausible Anzahl, teilweise neuartiger, genomischer Varianten und Genen identifiziert, welche verschiedenen Qualitätsmerkmalen zugrunde liegen.In this thesis, I developed two frameworks that can help highlight the genetic mechanisms underlying quantitative traits. In this regard, my focus was to design efficient methodologies to discover genotype-phenotype associations and then use these identified associations to describe the regulatory mechanism that affects the manifestation of phenotypic differences among the individuals. In the first framework, I investigated key regulatory mechanisms governing the development of eggshell strength. The aim was to highlight the temporal changes in the signaling cascades governing the dynamic eggshell strength during the life of birds. I considered chicken eggshell strength at two different time points during the egg production cycle and studied the genotype-phenotype associations by employing the Random Forest algorithm on genotypic data. For the analysis of corresponding genes, a well established systems biology approach was adopted to delineate gene regulatory pathways and master regulators underlying this important trait. My results indicate that, while some of the master regulators (Slc22a1 and Sox11) and pathways are common at different laying stages of chicken, others (e.g., Scn11a, St8sia2, or the TGF-beta pathway) represent age-specific functions. Overall, my results provide: (i) significant insights into age-specific and common molecular mechanisms underlying the regulation of eggshell strength; and (ii) new breeding targets to improve the eggshell quality during the later stages of the chicken production cycle. In my second framework, I combined the Random Forests and a signal detection strategy to identify robust genotype-phenotype associations. The objective of this framework was to improve on the efficiency of single-SNP based association analysis. Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype-phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect~remain unnoticed in many GWAS approaches. Here, we propose a two-step procedure to address this problem. In a first step, cubic splines are fitted to the test statistic values and genomic regions with spline-peaks that are higher than expected by chance are considered as quantitative trait loci (QTL). Then the SNPs in these QTLs are prioritized with respect to the strength of their association with the phenotype using a Random Forests approach. As a case study, we apply our procedure to real data sets and find trustworthy numbers of, partially novel, genomic variants and genes involved in various egg quality traits.2021-10-1

A Quantitative Genetic Analysis of Limb Segment Morphology in Humans and Other Primates: Genetic Variance, Morphological Integration, and Linkage Analysis

Limb segment lengths (and, by extension, limb proportions) are widely studied postcranial features in biological anthropology due to the seemingly consistent phenotypic patterning among human and fossil hominin groups. This patterning, widely presumed to be the result of adaptation to thermoregulatory efficiency, has led to the assumption among biological anthropologists that limb proportions in humans are phenotypically stable unless long periods of extreme environmental conditions force adaptive change. Because these traits are considered stable, they have been used to inform multiple areas of anthropological inquiry, including investigations of phylogenetic relationships and fossil species identification, locomotor behavior and the evolution of bipedalism, and migration patterns. The problem with this assumption is that phenotypic patterns may not accurately reflect evolutionary processes, and even if they do, there is no reason to expect phenotype to respond to natural selection solely. Investigations of phenotypic variation need to incorporate genetic variation and covariation to better understand the processes that produced observable patterns, including evolutionary processes. However, the incorporation of genetic parameters is often difficult given that knowledge of familial relationships are required. Therefore, the goal of this project is to use a quantitative genetics approach to estimate the genetic variance and covariance in limb segment lengths and then begin the task of identifying genes which may influence this normal variation. These tasks are accomplished using multiple large, pedigreed samples of primate species, including humans. Linkage analysis on a baboon sample, a well-accepted model organism for humans, is used to identify regions of the genome which may influence limb segment variation. The results presented here suggest that 1) while patterns of genetic and phenotypic variance and covariance across limb segments are broadly similar, there are differences in the details, and 2) while patterns of genetic and phenotypic variance and covariance within and among limb segments generally adhere to expectations set forth by developmental and evolutionary-based hypotheses, there are exceptions. Additionally, several genomic regions are identified which influence limb segment variation. Thus, biological anthropologists must use caution in their assumptions and interpretations regarding limb segment lengths and limb proportions in humans and other primates

Developing Statistical Methods for Incorporating Complexity in Association Studies

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with hundreds of human traits. Yet the common variant model tested by traditional GWAS only provides an incomplete explanation for the known genetic heritability of many traits. Many divergent methods have been proposed to address the shortcomings of GWAS, including most notably the extension of association methods into rarer variants through whole exome and whole genome sequencing. GWAS methods feature numerous simplifications designed for feasibility and ease of use, as opposed to statistical rigor. Furthermore, no systematic quantification of the performance of GWAS across all traits exists. Beyond improving the utility of data that already exist, a more thorough understanding of the performance of GWAS on common variants may elucidate flaws not in the method but rather in its implementation, which may pose a continued or growing threat to the utility of rare variant association studies now underway. This thesis focuses on systematic evaluation and incremental improvement of GWAS modeling. We collect a rich dataset containing standardized association results from all GWAS conducted on quantitative human traits, finding that while the majority of published significant results in the field do not disclose sufficient information to determine whether the results are actually valid, those that do replicate precisely in concordance with their statistical power when conducted in samples of similar ancestry and reporting accurate per-locus sample sizes. We then look to the inability of effectively all existing association methods to handle missingness in genetic data, and show that adapting missingness theory from statistics can both increase power and provide a flexible framework for extending most existing tools with minimal effort. We finally undertake novel variant association in a schizophrenia cohort from a bottleneck population. We find that the study itself is confounded by nonrandom population sampling and identity-by-descent, manifesting as batch effects correlated with outcome that remain in novel variants after all sample-wide quality control. On the whole, these results emphasize both the past and present utility and reliability of the GWAS model, as well as the extent to which lessons from the GWAS era must inform genetic studies moving forward

Whole-genome sequencing of European autochthonous and commercial pig breeds allows the detection of signatures of selection for adaptation of genetic resources to different breeding and production systems

Background: Natural and artificial directional selection in cosmopolitan and autochthonous pig breeds and wild boars have shaped their genomes and resulted in a reservoir of animal genetic diversity. Signatures of selection are the result of these selection events that have contributed to the adaptation of breeds to different environments and production systems. In this study, we analysed the genome variability of 19 European autochthonous pig breeds (Alentejana, Bísara, Majorcan Black, Basque, Gascon, Apulo-Calabrese, Casertana, Cinta Senese, Mora Romagnola, Nero Siciliano, Sarda, Krškopolje pig, Black Slavonian, Turopolje, Moravka, Swallow-Bellied Mangalitsa, Schwäbisch-Hällisches Schwein, Lithuanian indigenous wattle and Lithuanian White old type) from nine countries, three European commercial breeds (Italian Large White, Italian Landrace and Italian Duroc), and European wild boars, by mining wholegenome sequencing data obtained by using a DNA-pool sequencing approach. Signatures of selection were identified by using a single-breed approach with two statistics [within-breed pooled heterozygosity (HP) and fixation index (FST)] and group-based FST approaches, which compare groups of breeds defined according to external traits and use/specialization/type. Results: We detected more than 22 million single nucleotide polymorphisms (SNPs) across the 23 compared populations and identified 359 chromosome regions showing signatures of selection. These regions harbour genes that are already known or new genes that are under selection and relevant for the domestication process in this species, and that affect several morphological and physiological traits (e.g. coat colours and patterns, body size, number of vertebrae and teats, ear size and conformation, reproductive traits, growth and fat deposition traits). Wild boar related signatures of selection were detected across all the genome of several autochthonous breeds, which suggests that crossbreeding (accidental or deliberate) occurred with wild boars. Conclusions: Our findings provide a catalogue of genetic variants of many European pig populations and identify genome regions that can explain, at least in part, the phenotypic diversity of these genetic resources

Genetic and environmental prediction of opioid cessation using machine learning, GWAS, and a mouse model

The United States is currently experiencing an epidemic of opioid use, use disorder, and overdose-related deaths. While studies have identified several loci that are associated with opioid use disorder (OUD) risk, the genetic basis for the ability to discontinue opioid use has not been investigated. Furthermore, very few studies have investigated the non-genetic factors that are predictive of opioid cessation or their predictive ability. In this thesis, I studied a novel phenotype–opioid cessation, defined as the time since last use of illicit opioids (1 year ago as cease) among persons meeting lifetime DSM-5 criteria for opioid use disorder (OUD). In chapter two, I identified novel genetic variants and biological pathways that potentially regulate opioid cessation success through a genome wide study, as well as genetic overlap between opioid cessation and other substance cessation traits. In chapter three, I identified multiple non-genetic risk factors specific to each racial group that are predictive of opioid cessation from the same individuals analyzed in chapter two by applying several linear and non-linear machine learning techniques to a set of more than 3,000 variables assessed by a structured psychiatric interview. Factors identified from this atheoretical approach can be grouped into opioid use activities, other drug use, health conditions, and demographics, while the predictive accuracy as high as nearly 80% was achieved. The findings from this research generated more hypotheses for future studies to reference. In chapter four, I performed differential gene expression and network analysis on mice with different oxycodone (an opioid receptor agonist)-induced behaviors and compared the significantly associated genes and network modules with top-ranked genes identified in humans. The pathway cross-talks and gene homologs identified from both species illuminate the potential molecular mechanism of opioid behaviors. In summary, this thesis utilized statistical genetics, machine learning, and a computational biology framework to address factors that are associative with opioid cessation in humans, and cross-referenced the genetic findings in a mouse model. These findings serve as references for future studies and provide a framework for personalizing the treatment of OUD

Whole-genome sequencing of European autochthonous and commercial pig breeds allows the detection of signatures of selection for adaptation of genetic resources to different breeding and production systems

Background Natural and artificial directional selection in cosmopolitan and autochthonous pig breeds and wild boars have shaped their genomes and resulted in a reservoir of animal genetic diversity. Signatures of selection are the result of these selection events that have contributed to the adaptation of breeds to different environments and production systems. In this study, we analysed the genome variability of 19 European autochthonous pig breeds (Alentejana, Bísara, Majorcan Black, Basque, Gascon, Apulo-Calabrese, Casertana, Cinta Senese, Mora Romagnola, Nero Siciliano, Sarda, Krškopolje pig, Black Slavonian, Turopolje, Moravka, Swallow-Bellied Mangalitsa, Schwäbisch-Hällisches Schwein, Lithuanian indigenous wattle and Lithuanian White old type) from nine countries, three European commercial breeds (Italian Large White, Italian Landrace and Italian Duroc), and European wild boars, by mining whole-genome sequencing data obtained by using a DNA-pool sequencing approach. Signatures of selection were identified by using a single-breed approach with two statistics [within-breed pooled heterozygosity (HP) and fixation index (FST)] and group-based FST approaches, which compare groups of breeds defined according to external traits and use/specialization/type. Results We detected more than 22 million single nucleotide polymorphisms (SNPs) across the 23 compared populations and identified 359 chromosome regions showing signatures of selection. These regions harbour genes that are already known or new genes that are under selection and relevant for the domestication process in this species, and that affect several morphological and physiological traits (e.g. coat colours and patterns, body size, number of vertebrae and teats, ear size and conformation, reproductive traits, growth and fat deposition traits). Wild boar related signatures of selection were detected across all the genome of several autochthonous breeds, which suggests that crossbreeding (accidental or deliberate) occurred with wild boars. Conclusions Our findings provide a catalogue of genetic variants of many European pig populations and identify genome regions that can explain, at least in part, the phenotypic diversity of these genetic resources.info:eu-repo/semantics/publishedVersio