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From the nineties, the working groups in charge of small ruminants in ICAR carried out surveys on topics related to milk recording and breeding programs in sheep and goats. These surveys were manual from 1988 to 2006, before being replaced by a yearly online questionnaire available to accept submission of data from ICAR countries having an activity in the field of dairy sheep and goats recording. This paper aims to analyse the evolution of milk recording in sheep and goats over the years, using the database (developed and maintained within the umbrella of ICAR Secretariat) updated by the responses from the on- line surveys. The database includes data on milk recording activities (by countries and by breeds), milk recording designs, average milk yield per lactation, recording devices, breeding schemes and selection criteria, and molecular information in sheep and goats. The valorisation of the on-line survey constitutes one of the main terms of reference of the sheep, goats and camelids working group and a synthesis is regularly presented at the working groups meeting. An overlook of the main evolutions of the milk recording activities in sheep and goats are presented, over the last 35 years. Ten to twelve countries filled out the questionnaire in each species. The number of animals in official milk recording reached 890, 000 ewes and 410, 000 does in 2021. In the large populations of Mediterranean countries, the impact of qualitative recording remains low, due to its cost. Use of simplified designs, which has been recommended by the working group has strongly increased over the years to exceed 90%.

Additional file 1: Figure S1. Graphical comparison of LDLA and WGS-based data association analyses within the QTL region Q_01_1 (chromosome 1). The figure shows the test statistics (��� log10(nominal p-values) profile of the LDLA analysis (LDLA Mapping, red line) and Manhattan plot of the association analysis based on imputed genotypes from re-sequenced animals (WGS Mapping, blue dots) in the QTL region Q_01_1 (chromosome 1, imputation from 99 to 100 Mb of the Ovis aries genome assembly v4.0). Figure S2. Graphical comparison of LDLA and WGS-based data association analyses within the QTL region Q_02_1 (chromosome 2). The figure shows the test statistics (��� log10(nominal p-values) profile of the LDLA analysis (LDLA Mapping, red line) and Manhattan plot of the association analysis based on imputed genotypes from re-sequenced animals in the QTL region Q_02_1 (chromosome 2, imputation from 135 to 137 Mb of the Ovis aries genome assembly v4.0). Figure S3. Graphical comparison of LDLA and WGS-based data association analyses within the QTL region Q_02_2 (chromosome 2). The figure shows the test statistics (��� log10(nominal p-values) profile of the LDLA analysis (LDLA Mapping, red line) and Manhattan plot of the association analysis based on imputed genotypes from re-sequenced animals in the QTL region Q_02_2 (chromosome 2, imputation from 212 to 214 Mb of the Ovis aries genome assembly v4.0). Figure S4. Graphical comparison of LDLA and WGS-based data association analyses within the QTL region Q_04_1 (chromosome 4). The figure shows the test statistics (��� log10(nominal p-values) profile of the LDLA analysis (LDLA Mapping, red line) and Manhattan plot of the association analysis based on imputed genotypes from re-sequenced animals in the QTL region Q_04_1 (chromosome 4, imputation from 4 to 10 Mb of the Ovis aries genome assembly v4.0). Figure S5. Graphical comparison of LDLA and WGS-based data association analyses within the QTL region Q_06_1 (chromosome 6). The figure shows the test statistics (��� log10(nominal p-values) profile of the LDLA analysis (LDLA Mapping, red line) and Manhattan plot of the association analysis based on imputed genotypes from re-sequenced animals in the QTL region Q_06_1 (chromosome 6, imputation from 12 to 14 Mb of the Ovis aries genome assembly v4.0). Figure S6. Graphical comparison of LDLA and WGS-based data association analyses within the QTL region Q_07_1 (chromosome 7). The figure shows the test statistics (��� log10(nominal p-values) profile of the LDLA analysis (LDLA Mapping, red line) and Manhattan plot of the association analysis based on imputed genotypes from re-sequenced animals in the QTL region Q_07_1 (chromosome 7, imputation from 87 to 89 Mb of the Ovis aries genome assembly v4.0). Figure S7. Graphical comparison of LDLA and WGS-based data association analyses within the QTL region Q_12_1 (chromosome 12). The figure shows the test statistics (��� log10(nominal p-values) profile of the LDLA analysis (LDLA Mapping, red line) and Manhattan plot of the association analysis based on imputed genotypes from re-sequenced animals in the QTL region Q_12_1 (chromosome 7, imputation from 35 to 42 Mb of the Ovis aries genome assembly v4.0). Figure S8. Graphical comparison of LDLA and WGS-based data association analyses within the QTL region Q_15_1 (chromosome 15). The figure shows the test statistics (��� log10(nominal p-values) profile of the LDLA analysis (LDLA Mapping, red line) and Manhattan plot of the association analysis based on imputed genotypes from re-sequenced animals in the QTL region Q_15_1 (chromosome 15, imputation from 33 to 35 Mb of the Ovis aries genome assembly v4.0). Figure S9. Graphical comparison of LDLA and WGS-based data association analyses within the QTL region Q_19_1 (chromosome 19). The figure shows the test statistics (��� log10(nominal p-values) profile of the LDLA analysis (LDLA Mapping, red line) and Manhattan plot of the association analysis based on imputed genotypes from re-sequenced animals in the QTL region Q_19_1 (chromosome 19, imputation from 18 to 32 Mb of the Ovis aries genome assembly v4.0). Figure S10. Graphical comparison of LDLA and WGS-based data association analyses within the QTL region Q_20_1 (chromosome 20). The figure shows the test statistics (��� log10(nominal p-values) profile of the LDLA analysis (LDLA Mapping, red line) and Manhattan plot of the association analysis based on imputed genotypes from re-sequenced animals in the QTL region Q_20_1 (chromosome 20, imputation from 16 to 37 Mb of the Ovis aries genome assembly v4.0).

The Data Availability section in the original version of this Article was omitted. It now appears as below: “The genotyping data for the Italian goat considered in this study are deposited and publicly available on Mendeley Data (DOI: 10.17632/hnd59x6gmg.1; URL: https:// data. mende ley. com/ datas ets/ hnd59 x6gmg/1).” The original Article has been corrected.

LaMaedi Visna(MV) è unapatologia ovina cronicacon manifestazioni cliniche molto variabili, causata da un virus appartenente al genereLentivirusche comprende anche il virus dell’Artrite Encefalite Caprina (CAEV). I virus responsabili di MV e CAEV appartengono al gruppoSmall Ruminants Lentivirus(SRLV). In passato, si ritenevano somiglianti e specifici dell’ospite mentre in realtà si tratta di virus adattati all’ospite. In effetti, vengono identificati ceppi MV-like nelle capre e ceppi CAEV-like nelle pecore. Gliorgani bersaglionell’ovino sono ilpolmone, la cui infezione provoca la forma clinica indicata comeMaedi, e moltopiù raramente il sistema nervoso centrale,determinando la forma clinica indicata comeVisna. Inoltre, il virus della MV si localizza molto frequentemente anche nellamammella, dove viene escreto con il colostro ed il latte e determina forme dimastite cronicaquasi sempre non apprezzabili clinicamente. L’andamento della malattia è generalmente lento, progressivo, persistente e spesso paucisintomatico mentre i casi clinici conclamati sono rari. Una volta che il virus penetra nell’allevamento, si diffonde soprattutto attraverso l’assunzione di colostro e di latte, che costituisce lavia principale di contagio, mentre è relativamente meno importante la trasmissione attraverso il contatto diretto tra animali. Alcune patologie concomitanti come l’Adenomatosi Polmonare, che spesso è accompagnata dall’emissione di muco dal naso con conseguente contaminazione dell’ambiente, possono accelerarne la trasmissione orizzontale. Una valutazione precisa dei danni economici diretti negli allevamenti ovini è dunque difficile. Relativamente agli effetti sulle produzioni lattee e sugli accrescimenti degli agnelli, in letteratura si riportano risultati contraddittori probabilmente legati alla variabilità dei disegni sperimentali. L’altissima prevalenza del virus di MV negli allevamenti ovini della Sardegna e la sua elevata variabilità genetica, lo rendono in ogni caso unagente infettivo potenzialmente pericolosoche merita l’attenzione di operatori sanitari, zootecnici e allevatori. Attualmentenon esistono in Sardegna piani di controllo. Eventuali piani di eradicazione si fonderebbero sulla separazione degli agnelli dalle madri alla nascita con successivo allattamento artificiale. Un piano così concepito, anche se limitato alla sola quota di rimonta, è laborioso e costoso in quanto prevedrebbe la presenza praticamente continua di operatori nel periodo dei parti. In Sardegna, i tentativi di eradicazione della CAEV nelle capre con protocolli di questo tipo non hanno avuto molte adesioni, anche quando è stato previsto un incentivo pubblico per la loro realizzazione. L’ultimo studio epidemiologicamente rilevante in Sardegna è stato realizzato nel 2007 su 306 allevamenti con 18.513 capi testati (Puggioni et al., 2007). Circa il 90% degli allevamenti ovini presentavano almeno un capo sieropositivo con una prevalenza media (percentuale di capi positivi per gregge) del 33%. Non esistono dati più recenti rilevati sistematicamente, ma è presumibile che in assenza di piani di controllo e di divieti nella circolazione di capi positivi la situazione si sia ulteriormente deteriorata negli ultimi anni. Alla luce di queste considerazioni, il Ministero della Salute e il Centro Regionale di Programmazione hanno finanziato aIZS,AGRISeDipartimento Medicina Veterinaria di UNISSricerche mirate a verificare la possibilità di attuarestrategie di riduzione della prevalenza della patologiafondate sullaselezione di ovini geneticamente resistenti. Di seguito verranno illustrati i principali risultati di queste ricerche.

AdaptMap Consortium.

Additional file 1. Description of each Italian local cattle breed involved in this study [68].

In recent decades, changes in climate have caused impacts on natural and human systems. Mediterranean countries will be particularly affected by this phenomenon, with growing temperatures and reduced rainfall. Understanding how species and ecosystems respond to climate change has become a crucial focus in biodiversity conservation and management. The genome-wide SNP panels allows providing background information on genome structure in domestic animals, opening new perspectives to livestock genetics. The International Project GALIMED "Genetic Adaptation of Bovine Livestock and production systems in MEDiterranean region", develops an integrated approach that combines the analysis of SNP markers, bioclimatic variables and farming system information to identify genotypes and breeding practices able to respond to climate change. Italian and Corsican local breeds are part of this project. The aim of the study is to identify genomic regions related to adaptation to climate change in these local breeds. Individuals of Cinisara (71), Modicana (72), Maremmana (25), Piemontese (21), Romagnola (21), Sarda (30), Sardo-Modicana (28) and Corse (31) breeds were genotyped using Bovine SNP 50k. Farming systems data were collected by interviewing breeders. Geographic coordinates and 19 bioclimatic variables were also available. Principal Components Analysis (PCA) was performed on SNPs data and climatic variables. Co-inertia (CIA) analysis was realized to detect a possible common structure between such different information. After edits, 43,625 SNPs were retained. The PC1 on individual genotypes differentiates Modicana from other breeds, and shows that Sardo-Modicana is close to Modicana, but is clearly admixed to another breed. The PC2 differentiates Cinisara from a cluster "Romagnola/Maremmana" and shows a large variability of the Modicana breed. The CIA coefficient between molecular data and both geographic and climatic information is equal to 0.38, (P-value=0.001), suggesting evidence of genetic adaptation to different climatic pressures. Further analyses are on going to identify the genetic regions with a potential adaptive role. The results will provide a solid scientific foundation to reconsider objectives and selection criteria and to improve farming practices to prepare livestock to new environmental conditions.

Our aims were to: identify loci underlying variation in faecal egg count (FEC) both within and across sheep populations; evaluate the accuracy of genomic EBVs (GEBV) for FEC within and across populations; and explore non-additive genetic variation (i.e. epistasis and heterozygote advantage) for FEC. Data were available on 752 Scottish Blackface lambs, 2,371 Sarda-Lacaune backcross ewes, 1,000 Martinik Blackbelly-Romane backcross lambs and 64 Texel lambs. Phenotypes were FEC for Nematodirus and/or Strongyles at different ages. Several genomic regions of interest were identified, both within and across populations. Moreover, GEBV had moderate to good within-population predictive accuracy, whereas across-population predictions had accuracies close to zero. Epistasis analysis identified two pairwise SNP interactions significant at the suggestive level for Strongyles, and the heterozygote advantage analysis identified some SNPs reaching suggestive significance. Therefore, results suggest the presence a missing heritability undetectable via conventional GWAS, which warrants further exploration.

International audience

International audience; The diversity of populations in domestic species offers great opportunities to study genome response to selection. The recently published Sheep HapMap dataset is a great example of characterization of the world wide genetic diversity in sheep. In this study, we re-analyzed the Sheep HapMap dataset to identify selection signatures in worldwide sheep populations. Compared to previous analyses, we made use of statistical methods that (i) take account of the hierarchical structure of sheep populations, (ii) make use of linkage disequilibrium information and (iii) focus specifically on either recent or older selection signatures. We show that this allows pinpointing several new selection signatures in the sheep genome and distinguishing those related to modern breeding objectives and to earlier post-domestication constraints. The newly identified regions, together with the ones previously identified, reveal the extensive genome response to selection on morphology, color and adaptation to new environments.

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National audience

From the beginning of domestication, the transportation of domestic animals resulted in genetic and demographic processes that explain their present distribution and genetic structure. Thus studying the present genetic diversity helps to better understand the history of domestic species. Methodology/Principal Findings. The genetic diversity of domestic goats has been characterized with 2430 individuals from all over the old world, including 946 new individuals from regions poorly studied until now (mainly the Fertile Crescent). These individuals represented 1540 haplotypes for the HV segment of the mitochondrial DNA (mtDNA) control region. This large-scale study allowed the establishment of a clear nomenclature of the goat maternal haplogroups. Only five of the six previously defined groups of haplotypes were divergent enough to be considered as different haplogroups. Moreover a new mitochondrial group has been localized around the Fertile Crescent. All groups showed very high haplotype diversity. Most of this diversity was distributed among groups and within geographic regions. The weak geographic structure may result from the worldwide distribution of the dominant A haplogroup (more than 90% of the individuals). The large-scale distribution of other haplogroups (except one), may be related to human migration. The recent fragmentation of local goat populations into discrete breeds is not detectable with mitochondrial markers. The estimation of demographic parameters from mismatch analyses showed that all groups had a recent demographic expansion corresponding roughly to the period when domestication took place. But even with a large data set it remains difficult to give relative dates of expansion for different haplogroups because of large confidence intervals Conclusions/Significance. We propose standard criteria for the definition of the different haplogroups based on the result of mismatch analysis and on the use of sequences of reference. Such a method could be also applied for clarifying the nomenclature of mitochondrial haplogroups in other domestic species.

In lung and mammary gland of sheep, Maedi-Visna virus (MVV) causes lymphoproliferative inflammation often with follicular structures (lymphofollicular inflammation). The aim of this work was to define whether Limphotoxin α and β (LTα, LTβ) play a role in the formation of these peculiar lesions in sheep experimentally infected with MVV.

The aim of this work was to investigate the risk of scrapie of the ARQ/ARQ genotype carrying at least one point mutation at codons 112, 137, 141, 142, 154 and 176 in comparison with the ARQ/ARQ without any point mutations.

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International audience

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