Arrays and beyond: Evaluation of marker technologies for chicken genomics

Eine zentrale Forschungsfrage in der Nutztierforschung ist, wie die phänotypische Vielfalt von Nutztieren durch ihre genomische Vielfalt geprägt wird. Die genomische Vielfalt wird dabei durch genomische Marker beschrieben. Die Verwendung und Definition von genomischen Markern ist stark technologieabhängig und ändert sich daher im Laufe der Zeit. In den letzten Jahren haben sich Einzelnukleotidpolymorphismen (SNPs) zur wichtigsten Markerklasse entwickelt. Außerdem waren SNP-Arrays in den letzten Jahren aufgrund ihrer frühen Verfügbarkeit die Genotypisierungstechnologie der Wahl. Sie werden jedoch derzeit teilweise durch die Ganzgenomsequenzierung (WGS) zur SNP-Bestimmung verdrängt. Darüber hinaus rücken Strukturelle Varianten (SV) mehr und mehr in den Fokus der Forschung. In diesem Zusammenhang zielt die vorliegende Arbeit darauf ab, die Aussagekraft von SNP-Markern auf verschiedene Weise zu bewerten, wobei der Schwerpunkt auf Hühnern als einer vielfältigen Nutztierart mit großer landwirtschaftlicher Bedeutung liegt. In Kapitel 1 wird der aktuelle Wissensstand über genomische Variation, Markertechnologien und deren Einsatz in der Nutztierwissenschaft, insbesondere bei Hühnern, dargestellt. Kapitel 2 und 3 befassen sich dann mit einem systematischen Fehler von SNP-Arrays, dem SNP Ascertainment Bias. Der SNP Ascertainment Bias ist eine systematische Verschiebung des Allelfrequenzspektrums von SNP-Arrays hin zu häufigeren SNPs aufgrund der Vorauswahl von SNPs in einer begrenzten Anzahl von Individuen aus wenigen Populationen. Kapitel 2 zielt darauf ab, das Ausmaß des Bias für einen Standard-SNP-Array für Hühner und die Schritte des Array-Designs, die den Bias verursacht haben, zu bewerten. In der Studie haben wir daher den Designprozess des Hühnerarrays auf der Grundlage von (gepoolten) WGS verschiedener Hühnerpopulationen nachgestellt. Dabei zeigte sich eine sequentielle Reduktion seltener Allele während des Designprozesses, die vor allem durch die anfängliche Begrenzung des Discovery Sets und eine spätere Selektion von häufigen SNPs innerhalb der Populationen bei gleichzeitigem anstreben von äquidistanten Abständen verursacht wurde. Eine Vergrößerung des Discovery Panels hatte den größten Einfluss auf eine Begrenzung des Ascertainment Bias. Andere Schritte, wie z. B. die Validierung der SNPs in einem breiteren Set von Populationen, zeigten keine relevanten Auswirkungen. Korrekturmethoden für den Ascertainment Bias sind in Studien bisher meist nicht durchführbar. In Kapitel 3 wird daher vorgeschlagen, die Imputation der Array-Daten auf WGS-Niveau als in silico Korrekturmethode für das Allelfrequenzspektrum zu verwenden. Die Studie zeigte, dass die Imputation in der Lage ist, die Auswirkungen von Erhebungsfehlern stark zu reduzieren, selbst wenn ein sehr kleines Referenzpanel verwendet wurde. Es wurde jedoch auch deutlich, dass das Referenzpanel dann den gleichen Effekt wie das Discovery-Panel während des Array-Designs hat. Daher ist es von entscheidender Bedeutung, dass die Proben für das Referenzpanel gleichmäßig über das Populationsspektrum verteilt ausgewählt werden. SVs sind schwieriger zu bestimmen und zu genotypisieren als SNPs. Daher stellt sich die Frage, ob die Effekte von SV auch durch SNP-basierte Studien erfasst werden. Das wäre der Fall, wenn zwischen SNPs und SVs ein starkes Kopplungsungleichgewicht (LD) besteht. Dies wird in Kapitel 4 für drei kommerzielle Hühnerrassen auf der Grundlage von WGS-Daten untersucht. Die Studie zeigte, dass das LD zwischen Deletionen und SNPs auf dem gleichen Niveau lag wie das LD zwischen SNPs und anderen SNPs, was darauf hindeutet, dass Effekte von Deletionen von SNP-Marker-Panels genauso gut erfasst werden wie SNP-Effekte. Das LD zwischen SNPs und anderen SVs war stark reduziert. Der Hauptfaktor für diese Verringerung waren lokale Unterschiede zu SNPs in Bezug auf die Minor-Allel-Frequenz. Eine Reduktion der homozygoten Varianten für Nicht-Deletions-SVs im Vergleich zur Erwartung unter Hardy-Weinberg-Gleichgewicht kann jedoch auf Probleme der verwendeten SV-Genotypisierer hinweisen. Im letzten Kapitel (Kapitel 5) werden die Auswirkungen des Ascertainment Bias und die Möglichkeiten, damit in der Hühnergenomforschung (und auch generell in der Nutztiergenomforschung) umzugehen, diskutiert. Außerdem werden die Möglichkeiten der Einbeziehung von SV in Studien bewertet. Es wird auch erörtert, was notwendig ist, um die Informationen aus verschiedenen genomischen Datensätzen zu kombinieren damit der Aussagewert von Studien erhöht wird. Abschließend wird ein Ausblick darauf gegeben, welche Informationen aufgrund der jüngsten technologischen Fortschritte in naher Zukunft zusätzlich verfügbar sein werden.A key research question in livestock research is how livestock’s phenotypic diversity is shaped by its genomic diversity. Genomic diversity is thereby assessed through genomic markers. The use and definition of genomic markers is strongly technology driven and therefore changes through time. During the last years, single nucleotide polymorphisms (SNPs) have become the main marker class. Additionally, SNP arrays have been the genotyping technology of choice during the last years due to their early availability. They are, however, currently partially displaced by whole-genome-sequencing (WGS) for SNP calling. Further, structural variants (SV) are moving more and more into the focus of researchers. In this context, the thesis aims in evaluating the value of SNP markers in various ways with its main focus on chickens as a diverse livestock species with major agricultural value. In Chapter 1, the current knowledge of genomic variation, marker technologies, and their use in livestock sciences, especially in chickens, is reviewed. Chapter 2 and 3 then address a systematic error of SNP arrays, the SNP ascertainment bias. SNP ascertainment bias is a systematic shift of the allele frequency spectrum of SNP arrays towards more common SNPs due to the pre-selection of SNPs in a limited number of individuals of few populations. Chapter 2 aims in assessing the magnitude of the bias for a standard chicken SNP array and the steps of array design that created the bias. In the study, we therefore remodeled the design process of the chicken array based on (pooled) WGS of various chicken populations. This revealed a sequential reduction of rare alleles during the design process, which was mainly caused by the initial limitation of the discovery set and a later within-population selection of common SNPs while aiming for equidistant spacing. Increasing the discovery set had the largest impact on limiting ascertainment bias. Other steps, as e.g. validation of the SNPs in a broader set of populations did not show relevant effects. Correction methods for ascertainment bias are by now often unfeasible in studies. Chapter 3 therefore proposes to use imputation of the array data to WGS level as an in silico correction method of the allele frequency spectrum. The study revealed that imputation is able to strongly reduce the effects of ascertainment bias, even when a very sparse reference panel was used. However, it became also obvious that the reference panel then has the same effect as the discovery panel during array design. It is therefore crucial to select samples for the reference panel evenly spaced across the intended range of populations. SVs are harder to call and genotype than SNPs. Therefore, the question arises whether effects of SV are captured by SNP-based studies due to strong linkage disequilibrium between SNPs and SVs. This is assessed in Chapter 4 for three commercial chicken breeds, based on WGS data. The study showed that LD between deletions and SNPs was on the same level as LD between SNPs and other SNPs, indicating that deletion effects are captured by SNP marker panels as good as SNP effects. LD between SNPs and other SVs was strongly reduced. The main factor for this reduction was local differences to SNPs in terms of minor allele frequency. However, a reduction of homozygous variant calls for non-deletion SVs compared to the Hardy-Weinberg-expectation may indicate problems of the used SV genotypers. In the last chapter (Chapter 5), the impact of ascertainment bias and possibilities to deal with it in chicken genomics (and also more general in livestock genomics) is discussed. Further, the potentials of including SVs into studies are evaluated. It also discusses what is necessary to combine the information of different genomic data sets to leverage the value of analyses. Finally, an outlook on what information will be additionally available in near future based on recent technological advances is given.2022-01-1

Bimetallic Palladium Catalysis: Direct Observation of Pd(III)–Pd(III) Intermediates

$PhI(OAc)_2$ is a common oxidant for Pd-catalyzed C−H bond functionalizations. Mechanistic hypotheses since the 1960s have suggested a Pd(II)/Pd(IV) mechanism. Here we present evidence for the relevance of bimetallic Pd(III) complexes to catalysis. A bimetallic Pd(III) acetate was isolated and can afford product by bimetallic reductive elimination.Chemistry and Chemical Biolog

Suppression of the magnetic order in CeFeAsO: non-equivalence of hydrostatic and chemical pressure

We present a detailed investigation of the electronic properties of CeFeAsO under chemical (As by P substitution) and hydrostatic pressure by means of in-house and synchrotron M\"ossbauer spectroscopy. The Fe magnetism is suppressed due to both pressures and no magnetic order was observed above a P-substitution level of 40% or 5.2 GPa hydrostatic pressure. We compared both pressures and found that the isovalent As by P substitution change the crystallographic and electronic properties differently than hydrostatic pressure.Comment: supplement is included in the pdf fil

Genetics of adaptation in modern chicken

This work is licensed under a Creative Commons Attribution 4.0 International License.We carried out whole genome resequencing of 127 chicken including red jungle fowl and multiple populations of commercial broilers and layers to perform a systematic screening of adaptive changes in modern chicken (Gallus gallus domesticus). We uncovered >21 million high quality SNPs of which 34% are newly detected variants. This panel comprises >115,000 predicted amino-acid altering substitutions as well as 1,100 SNPs predicted to be stop-gain or -loss, several of which reach high frequencies. Signatures of selection were investigated both through analyses of fixation and differentiation to reveal selective sweeps that may have had prominent roles during domestication and breed development. Contrasting wild and domestic chicken we confirmed selection at the BCO2 and TSHR loci and identified 34 putative sweeps co-localized with ALX1, KITLG, EPGR, IGF1, DLK1, JPT2, CRAMP1, and GLI3, among others. Analysis of enrichment between groups of wild vs. commercials and broilers vs. layers revealed a further panel of candidate genes including CORIN, SKIV2L2 implicated in pigmentation and LEPR, MEGF10 and SPEF2, suggestive of production-oriented selection. SNPs with marked allele frequency differences between wild and domestic chicken showed a highly significant deficiency in the proportion of amino-acid altering mutations (P<2.5×10−6). The results contribute to the understanding of major genetic changes that took place during the evolution of modern chickens and in poultry breeding

Review on Superconducting Materials

Short review of the topical comprehension of the superconductor materials classes Cuprate High-Temperature Superconductors, other oxide superconductors, Iron-based Superconductors, Heavy-Fermion Superconductors, Nitride Superconductors, Organic and other Carbon-based Superconductors and Boride and Borocarbide Superconductors, featuring their present theoretical understanding and their aspects with respect to technical applications.Comment: A previous version of this article has been published in \" Applied Superconductivity: Handbook on Devices and Applications \", Wiley-VCH ISBN: 978-3-527-41209-9. The new extended and updated version will be published in \" Encyclopedia of Applied Physics \", Wiley-VC

AFe2As2 (A = Ca, Sr, Ba, Eu) and SrFe_(2-x)TM_(x)As2 (TM = Mn, Co, Ni): crystal structure, charge doping, magnetism and superconductivity

The electronic structure and physical properties of the pnictide compound families $RE$OFeAs ($RE$ = La, Ce, Pr, Nd, Sm), $A$Fe$_{2}$As$_{2}$ ($A$ = Ca, Sr, Ba, Eu), LiFeAs and FeSe are quite similar. Here, we focus on the members of the $A$Fe$_{2}$As$_{2}$ family whose sample composition, quality and single crystal growth are better controllable compared to the other systems. Using first principles band structure calculations we focus on understanding the relationship between the crystal structure, charge doping and magnetism in $A$Fe$_{2}$As$_{2}$ systems. We will elaborate on the tetragonal to orthorhombic structural distortion along with the associated magnetic order and anisotropy, influence of doping on the $A$ site as well as on the Fe site, and the changes in the electronic structure as a function of pressure. Experimentally, we investigate the substitution of Fe in SrFe$_{2-x}TM_{x}$As$_{2}$ by other 3$d$ transition metals, $TM$ = Mn, Co, Ni. In contrast to a partial substitution of Fe by Co or Ni (electron doping) a corresponding Mn partial substitution does not lead to the supression of the antiferromagnetic order or the appearance of superconductivity. Most calculated properties agree well with the measured properties, but several of them are sensitive to the As $z$ position. For a microscopic understanding of the electronic structure of this new family of superconductors this structural feature related to the Fe-As interplay is crucial, but its correct ab initio treatment still remains an open question.Comment: 27 pages, single colum

How array design creates SNP ascertainment bias.

Single nucleotide polymorphisms (SNPs), genotyped with arrays, have become a widely used marker type in population genetic analyses over the last 10 years. However, compared to whole genome re-sequencing data, arrays are known to lack a substantial proportion of globally rare variants and tend to be biased towards variants present in populations involved in the development process of the respective array. This affects population genetic estimators and is known as SNP ascertainment bias. We investigated factors contributing to ascertainment bias in array development by redesigning the Axiom™ Genome-Wide Chicken Array in silico and evaluating changes in allele frequency spectra and heterozygosity estimates in a stepwise manner. A sequential reduction of rare alleles during the development process was shown. This was mainly caused by the identification of SNPs in a limited set of populations and a within-population selection of common SNPs when aiming for equidistant spacing. These effects were shown to be less severe with a larger discovery panel. Additionally, a generally massive overestimation of expected heterozygosity for the ascertained SNP sets was shown. This overestimation was 24% higher for populations involved in the discovery process than not involved populations in case of the original array. The same was observed after the SNP discovery step in the redesign. However, an unequal contribution of populations during the SNP selection can mask this effect but also adds uncertainty. Finally, we make suggestions for the design of specialized arrays for large scale projects where whole genome re-sequencing techniques are still too expensive