Whole-genome SNP association analysis of reproduction traits in the Finnish Yorkshire pig breed

Hedelmällisyysominaisuuksien alhaisen periytymisasteen vuoksi näiden ominaisuuksien parantaminen perinteisen jalostusvalinnan avulla on osoittautunut haasteelliseksi, eikä niiden perinnöllinen edistyminen ole täysin vastannut odotuksia myöskään Suomessa. Yksi mahdollisuus hedelmällisyysominaisuuksien perinnöllisen edistymisen nopeuttamiseen on käyttää geenimerkkejä. Assosiaatioanalyysin tuloksena löydetään ne geenimerkit, joilla on tilastollisesti merkitsevä vaikutus tutkittavan ominaisuuden suhteen. Tutkimuksen tavoitteena oli löytää yorkshire-sioilla ne kromosomialueet, jotka ovat yhteydessä sian hedelmällisyysominaisuuksiin. Tarkoituksena oli myös selvittää, ovatko yorkshire-sioista löydetyt kromosomialueet yhteydessä samoihin hedelmällisyysominaisuuksiin aiemmin maatiaissioilta löydettyihin alueisiin. Lisäksi tutkimuksessa pyrittiin löytämään tilastollisesti merkitsevien SNP-markkereiden lähellä sijaitsevia geenejä. Tutkimuksessa tarkasteltiin yhdeksää naarashedelmällisyyden ominaisuutta, jotka olivat syntyneiden porsaiden lukumäärä ensimmäisessä ja myöhemmissä pahnueissa, kuolleina syntyneiden porsaiden lukumäärä ensimmäisessä ja myöhemmissä pahnueissa, porsaskuolleisuus syntymästä vieroitukseen ensimmäisessä ja myöhemmissä pahnueissa, emakon ikä ensimmäisellä porsimisella sekä ensimmäinen ja toinen porsimisväli. Ominaisuuksien ja SNP-markkerien välisen assosiaation selvittämiseen käytettiin lineaarista sekamallia, joka sisälsi kiinteän SNP-vaikutuksen ja satunnaisen polygeenisen vaikutuksen. Assosiaatioanalyysi tehtiin AI-REML-menetelmällä, lisäksi tutkimuksessa käytettiin deregressoituja EBV:eitä ja painokertoimia. Tilastollisen merkitsevyystason selvittämisessä käytettiin Bonferronin korjausta. Tutkimuksessa löydettiin 20 sian hedelmällisyysominaisuuksiin vaikuttavaa tilastollisesti merkitsevää (P-arvo ? 2,0E-06) kromosomialuetta. Näistä markkereista yksi (krom. 7) liittyi syntyneiden porsaiden lukumäärään ensimmäisessä pahnueessa (P-arvo = 8,39E-08), kaksi (krom. 7 ja 1) syntyneiden porsaiden lukumäärään myöhemmissä pahnueissa (P-arvot = 2,77E-07 ja 1,91E-06), yksi (krom. 8) ensimmäisessä pahnueessa kuolleina syntyneiden porsaiden lukumäärään (P-arvo = 9,72E-08), kolme (krom. 13) ensimmäisen pahnueen syntymän ja vieroituksen väliseen porsaskuolleisuuteen (P-arvo = 2,12E-07 ja kahdella jälkimmäisellä molemmilla 7,01E-07), 11 (krom. 7) emakon ikään ensimmäisellä porsimisella (P-arvot 1,74E-06 ja 3,89E-08 välillä) ja kaksi (krom. 7) toiseen porsimisväliin (molemmilla P-arvot 4,03E-07). Tutkimuksessa löydettiin myös suuntaa antavia (P-arvo ? 4,0E-06) alueita kromosomeista 1 ja 8. Nämä kromosomialueet vaikuttavat myöhempien pahnueiden pahnuekokoon ja kuolleena syntyneiden porsaiden lukumäärään ensimmäisessä pahnueessa. Jos tässä tutkimuksessa löydetyt kromosomialueet saadaan varmistettua, voidaan näitä SNP-markkereita hyödyntää merkkiavusteisen valinnan avulla kansallisessa jalostusohjelmassa. MAS:n avulla sikoja voidaan alkaa valita hedelmällisyysominaisuuksien kannalta suotuisien SNP-markkereiden suhteen ja pidemmällä tähtäimellä hedelmällisyysominaisuuksien geneettinen edistyminen alkaa nopeutua. Laajempien populaatiotutkimusten avulla voidaan myös selvittää, mitkä geenit vaikuttavat hedelmällisyysominaisuuksiin ja millaiset niiden geenivaikutukset ovat.It has been challenging to genetically improve reproduction traits with the resources of the conventional breeding because of the low heritability of these traits. In Finland genetic improvement of the reproduction traits hasn’t either meet the expectations entirely. Gene markers are one possibility to make genetic improvement of the reproduction traits more effective. With an association analysis it is possible to find those gene markers which have statistically significant effect on the trait. The purpose of this study was to identify SNP markers associated with reproduction traits in the Finnish Yorkshire pig breed. Other purpose of this study was to find out if both Finnish pig breeds have certain chromosomal regions associated with same reproduction traits. In this study one goal was also to discover genes which are placed near statistically significant SNP markers. Under this study were nine female reproduction traits which are total number of piglets born in first and later parities, number of stillborn piglets in first and later parities, piglet mortality between birth and weaning in first and later parities, age at first farrowing, first farrowing interval and second farrowing interval. Prior to SNP association analysis, unstandardized EBVs were deregressed and corresponding weights were calculated. The association between SNP markers and deregressed EBV was studied using a mixed linear model, for each SNP separately. The model included a fixed SNP effect and a random polygenic effect. The analyses were performed using the AI-REML method. Statistical significance of the associations was based on Bonferroni-corrected P-values. In this study 20 statistically significant (P-value ? 2,0E-06) associations on the reproduction traits were observed. One of these SNP markers (P-value = 8,39E-08, on chromosome 7) is associated in total number of piglets born in first parity, two markers (P-values = 2,77E-07 and 1,91E-06, on chromosomes 7 and 1) in total number of piglets born in later parities, one marker (P-value = 9,72E-08, on chromosome 8) in number of stillborn piglets in first parity, three markers (P-value = 2,12E-07 and next two P-value was 7,01E-07, on chromosome 13) in piglet mortality between birth and weaning in first parity, 11 markers (P-values between 1,74E-06 and 3,89E-08, on chromosome 7) in age at first farrowing and two markers (P-values = 4,03E-07, on chromosome 7) in second farrowing interval. In this study statistically suggestive (P-value ? 4,0E-06) associations were also observed on chromosomes 1 and 8. These are associated in total number of piglets born in later parities and number of stillborn piglets in first parity. If these chromosomal regions found in this study are confirmed, these SNP markers will be valuable in the national breeding program through their use in marker-assisted selection. With MAS it’s possible to select pigs which have favourable SNP markers as for reproduction traits. In the long run genetic improvement of the reproduction traits is going to accelerate. There is still need of more extensive population analysis in order to discover genes associated reproduction traits and to estimate those gene effects

Genetic parameters for cow-specific digestibility predicted by near infrared reflectance spectroscopy

Digestibility traits included in this study were dry matter digestibility (DMD, g/kg), which was calculated based on the indigestible neutral detergent fibre (iNDF, g/kg of dry matter) content in faeces (iNDFf) and in diet (iNDFd), and iNDFf predicted directly from faecal samples by near infrared reflectance spectroscopy (NIRS). The data set was collected at three research herds in Finland and one in Norway including in total 931 records from 328 lactating Nordic Red Cattle and Holstein cows. Observations were associated with different accuracy, due to the differences in sampling protocols used for collecting faecal samples. Heritability estimates varied between different sampling protocols and ranged from 0.14 ± 0.06 to 0.51 ± 0.24 for DMD and from 0.13 ± 0.05 to 0.48 ± 0.18 for iNDFf. Estimated genetic standard deviations were 10.5 g/kg and 6.2 g/kg dry matter for DMD and iNDFf, respectively. Results of our study indicated that recording only the iNDF content in the faeces is sufficient to determine genetic variation in cows’ ability to digest feed. The coefficient of genetic variation for DMD was rather small (1.7%), but could be utilized if it is supported by a positive analysis of benefits over costs.Peer reviewe

Genetisk forbedring av fôreffektiviteten hos melkeku

The main objective of the thesis was to investigate the requirements and possibilities for including feed efficiency (FE) in the breeding goal in dairy cattle and hence enable the genetic improvement of feed efficiency. In addition, possible ways to obtain large scale phenotypic data for the genetic improvement of FE were investigated. The data was provided by Norwegian dairy foods company TINE SA (Ås, Norway), NMBU research farm (Ås, Norway), the Norwegian Dairy Herd Recording System (Ås, Norway) and Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark (Co. Cork, Ireland). The data consisted of records from two research farms, the Norwegian dairy herd recording system and mid-infrared (MIR) spectroscopy of milk. In total, data from 160 lactating Norwegian Red dairy cows and 375 lactating Irish Holstein-Friesian dairy cows were used in the thesis, recorded from 2007 to 2015. Individual feed intake (FI), milk yield (MY), concentration of milk, body weight (BW) and milk spectral recordings were included in the dataset. In Paper I, alternative genomic selection (GS) and traditional Best Linear Unbiased Prediction (BLUP) breeding schemes were compared for the genetic improvement of feed efficiency in simulated Norwegian Red dairy cattle populations. The change in genetic gain over time and achievable selection accuracy were studied for MY and residual feed intake (RFI). When contracted test herds, with genotyped and FE recorded cows as a reference population were used, a reference population size of 4,000 new heifers per year was needed to achieve considerable genetic improvement of feed efficiency. With such a reference population it was possible to reach similar selection accuracies of 0.75 for males than when using progeny testing. It was concluded that the use of contracted test herds with additional recordings (e.g. FE) is a viable option for the genetic improvement of such difficult to record traits. In Paper II, MIR spectra of milk was used to predict dry matter intake (DMI) and net energy intake (NEI) in Norwegian Red dairy cows. Leave-one-out cross-validation and external validation were used to develop and validate prediction equations using five different models. Predictions were performed using either partial least squares regression (PLS) or BLUP. When using the PLS method, the greatest accuracy (R) for predicting DMI (0.54) and NEI (0.65) in the external validation dataset was achieved when using both BW and MY as predictors incombination with the MIR spectra. The Best Linear Unbiased Prediction method gave similar accuracies as PLS but the predictions were biased. This study shows that MIR spectral data can be used to predict NEI as a measure of FI in Norwegian Red dairy cattle, and the prediction accuracy can be increased if BW and/or MY is added to the model. In Paper III, milk MIR data from two countries was combined and used to predict NEI in Norwegian Red dairy cows and effective energy intake (EEI) in Holstein-Friesian dairy cows. Split-sample cross-validation and external validation methods were used to develop and validate prediction equations using four different models. Predictions were performed using either PLS regression, multiple linear regression (MLR) or BLUP methods. Best Linear Unbiased Predictions were implemented either as a single trait or a multi-trait method. Using across country spectra, the R of predicting EEI increased by 0.02 units in both the cross-validation and the external validation compared to the model with spectral information within country only. For NEI, the use of across country MIR decreased the prediction accuracy in the cross-validation by 0.02 units and had no effect on R in the external validation. When NEI was predicted using only the MIR spectral information, single trait BLUP method yielded greater accuracy than PLS. For both NEI and EEI, the greatest accuracy of prediction was achieved using across country MIR spectra. This study shows that MIR spectral data from two countries can be combined and used to increase accuracies of predictions of energy intake (EI) as a measure of feed intake in dairy cattle. If sufficient quantity of FE phenotypic data is available, genetic improvement of feed efficiency is possible. MIR spectral data can be used to predict NEI as a measure of feed intake in Norwegian Red dairy cattle. Also, across country MIR spectral data can be used to predict different energy intake traits. Before including any measure of FE in the breeding program, genetic correlations between predicted feed intake, actual feed intake and other performance traits, especially health and fertility traits must be estimated, and taken into consideration.Hovedformålet med avhandlingen var å undersøke kravene og mulighetene for å inkludere fôreffektivitet (FE) i avlsmålet til melkekyr og dermed muliggjøre den genetiske forbedringen av fôreffektivitet. I tillegg ble mulige måter å oppnå storskala fenotypiske data for genetisk forbedring av FE undersøkt. Dataene ble levert av det norske meieriet TINE SA (Ås, Norge), NMBU-forsøksgård (Ås, Norge), Kukontrollen (Ås, Norge) og Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark (Co. Cork, Ireland). Dataene besto av målinger fra to forskningsgårder, kukontrollen og mid-infrarød spekteranalyse (MIR) av melk. Totalt ble det brukt data fra 160 lakterende melkekyr av rasen Norsk Rødt Fe og 375 lakterende Irske Holstein-Frieser melkekyr i avhandlingen, registrert fra 2007 til 2015. Datasettet inkluderer individuell opptak av fôr (FI), melkeytelse (MY), konsentrasjon av melk, kroppsvekt (BW) og melkespekter. I artikkel I ble alternativ genomisk seleksjon (GS) og tradisjonelle Best Linear Unbiased Prediction (BLUP) avlssystemer sammenlignet for den genetiske forbedringen av fôreffektivitet i simulerte Norske Røde melkekyrpopulasjoner. Forandringen i genetisk gevinst over tid og oppnåelig seleksjonsnøyaktighet ble studert for MY og restinntak av fôr (RFI). Når det ble brukt begrensede testbesetninger med kyr som har registrert genotype og FE som referansepopulasjon, var det nødvendig med en referansepopulasjonsstørrelse på 4000 nye kviger per år for å oppnå betydelig genetisk forbedring av fôreffektivitet. Med en slik referansepopulasjon var det mulig å oppnå seleksjonsnøyaktigheter på 0,75 for okser, tilsvarende bruk av avkomstgransking. Det ble konkludert med at bruk av testbesetninger med tilleggsregistreringer (for eksempel FE) er et overkommelig alternativ for den genetiske forbedring av egenskaper som er vanskelige å registrere. I artikkel II ble MIR av melk brukt til å forutsi inntak av tørrstoff (DMI) og netto energiinntak (NEI) i Norsk Rødt Fe. "Holde-en-utenfor" -kryss-validering og ekstern validering ble brukt til å utvikle og validere prediksjonsligninger ved bruk av fem forskjellige modeller. Prediksjoner ble utført ved bruk av enten Partial Least Squares Regression (PLS) eller BLUP metoder. Ved bruk av PLS-metoden ble den største nøyaktigheten (R) for å forutsi DMI (0,54) og NEI (0,65) i det eksterne valideringsdatasettet oppnådd ved bruk av både BW og MY som prediktorer i kombinasjon med MIR-spektrene. Best Linear Unbiased Prediction -metoden ga lignendenøyaktigheter som PLS, men prognosene var partisk. Denne studien viser at MIR-spektraldata kan brukes til å forutsi NEI som et mål for FI i Norsk Rødt Fe, og prediksjonsnøyaktigheten kan økes dersom BW og/eller MY er lagt til modellen. I artikkel III ble MIR av melk fra to land kombinert og brukt til å forutsi NEI i Norsk Rødt Fe og effektivt energiinntak (EEI) i Holstein-Frieser melkekyr. "Split-sample" -kryss-validering og eksterne valideringsmetoder ble brukt til å utvikle og validere prediksjonsligninger ved bruk av fire forskjellige modeller. Forutsigelser ble utført ved bruk av enten PLS-regresjon, multiple lineære regresjon (MLR) eller BLUP-metoder. Best Linear Unbiased Predictions ble implementert enten som enkeltegenskap eller en fler-egenskapsmetode. Ved å bruke spekter på tvers av land, økte R for å forutsi EEI med 0,02 enheter både i kryssvalidering og ekstern validering sammenlignet med modellen med spekterinformasjon bare innen land. For NEI reduserte bruken av MIR på tvers av land prediksjonsnøyaktigheten i kryssvalideringen med 0,02 enheter og hadde ingen effekt i R i den eksterne valideringen. Når NEI var forutsatt bare ved bruk av MIR-spekterinformasjonen, enkeltegenskap BLUP-metode ga større nøyaktighet enn PLS. For både NEI og EEI ble den største nøyaktigheten av prediksjon oppnådd ved bruk av MIR på tvers av land. Denne studien viser at MIR-spektraldata fra to land kan kombineres og brukes til å forutsi energiinntak (EI) som et mål for inntak av fôr i melkekyr. Hvis tilstrekkelig mengde fenotypiske data om FE er tilgjengelige, er genetisk forbedring av fôreffektivitet mulig. MIR-spektraldata kan brukes til å forutsi NEI som et mål for fôrinntaket i Norsk Rødt Fe. Også kan på tvers av land MIR spekterdata brukes til å forutsi forskjellige energiinntaks karakteristikker. Før det inngår noen måling av FE i avlsprogrammet, må genetiske korrelasjoner mellom predikert fôrinntak, faktisk inntak av fôr og andre ytelsesegenskaper, spesielt helse- og fruktbarhetsegenskaper, estimeres og tas i betraktning

Genetic improvement of feed efficiency in dairy cattle

The main objective of the thesis was to investigate the requirements and possibilities for including feed efficiency (FE) in the breeding goal in dairy cattle and hence enable the genetic improvement of feed efficiency. In addition, possible ways to obtain large scale phenotypic data for the genetic improvement of FE were investigated. The data was provided by Norwegian dairy foods company TINE SA (Ås, Norway), NMBU research farm (Ås, Norway), the Norwegian Dairy Herd Recording System (Ås, Norway) and Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark (Co. Cork, Ireland). The data consisted of records from two research farms, the Norwegian dairy herd recording system and mid-infrared (MIR) spectroscopy of milk. In total, data from 160 lactating Norwegian Red dairy cows and 375 lactating Irish Holstein-Friesian dairy cows were used in the thesis, recorded from 2007 to 2015. Individual feed intake (FI), milk yield (MY), concentration of milk, body weight (BW) and milk spectral recordings were included in the dataset. In Paper I, alternative genomic selection (GS) and traditional Best Linear Unbiased Prediction (BLUP) breeding schemes were compared for the genetic improvement of feed efficiency in simulated Norwegian Red dairy cattle populations. The change in genetic gain over time and achievable selection accuracy were studied for MY and residual feed intake (RFI). When contracted test herds, with genotyped and FE recorded cows as a reference population were used, a reference population size of 4,000 new heifers per year was needed to achieve considerable genetic improvement of feed efficiency. With such a reference population it was possible to reach similar selection accuracies of 0.75 for males than when using progeny testing. It was concluded that the use of contracted test herds with additional recordings (e.g. FE) is a viable option for the genetic improvement of such difficult to record traits. In Paper II, MIR spectra of milk was used to predict dry matter intake (DMI) and net energy intake (NEI) in Norwegian Red dairy cows. Leave-one-out cross-validation and external validation were used to develop and validate prediction equations using five different models. Predictions were performed using either partial least squares regression (PLS) or BLUP. When using the PLS method, the greatest accuracy (R) for predicting DMI (0.54) and NEI (0.65) in the external validation dataset was achieved when using both BW and MY as predictors incombination with the MIR spectra. The Best Linear Unbiased Prediction method gave similar accuracies as PLS but the predictions were biased. This study shows that MIR spectral data can be used to predict NEI as a measure of FI in Norwegian Red dairy cattle, and the prediction accuracy can be increased if BW and/or MY is added to the model. In Paper III, milk MIR data from two countries was combined and used to predict NEI in Norwegian Red dairy cows and effective energy intake (EEI) in Holstein-Friesian dairy cows. Split-sample cross-validation and external validation methods were used to develop and validate prediction equations using four different models. Predictions were performed using either PLS regression, multiple linear regression (MLR) or BLUP methods. Best Linear Unbiased Predictions were implemented either as a single trait or a multi-trait method. Using across country spectra, the R of predicting EEI increased by 0.02 units in both the cross-validation and the external validation compared to the model with spectral information within country only. For NEI, the use of across country MIR decreased the prediction accuracy in the cross-validation by 0.02 units and had no effect on R in the external validation. When NEI was predicted using only the MIR spectral information, single trait BLUP method yielded greater accuracy than PLS. For both NEI and EEI, the greatest accuracy of prediction was achieved using across country MIR spectra. This study shows that MIR spectral data from two countries can be combined and used to increase accuracies of predictions of energy intake (EI) as a measure of feed intake in dairy cattle. If sufficient quantity of FE phenotypic data is available, genetic improvement of feed efficiency is possible. MIR spectral data can be used to predict NEI as a measure of feed intake in Norwegian Red dairy cattle. Also, across country MIR spectral data can be used to predict different energy intake traits. Before including any measure of FE in the breeding program, genetic correlations between predicted feed intake, actual feed intake and other performance traits, especially health and fertility traits must be estimated, and taken into consideration

Strategies for implementing genomic selection for feed efficiency in dairy cattle breeding schemes

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Sianlihan laatuominaisuuksien genominen analyysi SNP-markkereiden avulla

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Sianlihan laatuominaisuuksien genominen analyysi SNP-markkereiden avulla

Sianlihan laadulla on tärkeä merkitys teollisten lihatuotteiden eri prosessointivaiheissa ja kuluttajien käyttäessä tuorelihaa ruuan valmistukseen. Sianlihasta valmistetun ruuan tulee olla sekä maukasta, terveellistä että rakenteeltaan hyvää. Lihan laatuun vaikuttavat ominaisuudet mm. lihan pH ja väripisteet ovat olleet kansallisesti jalostettavia ominaisuuksista jo noin 20 vuoden ajan. Nykyinen lihan laatuominaisuuksien jalostaminen perustuu koeasemalla kasvatettujen eläinten ruhoista mitattuihin sisäpaistin ja kyljyksen pH-arvoon (noin 20h teurastuksesta), lihan vaaleuteen (L*) ja lihan punaisuuteen (a*). Lihan laatuominaisuuksiin vaikuttaa oletettavasti suuri joukko eri geenejä, tosin joitain suurivaikutteisia geenimuotoja esim. halotaanigeenissä ja RN-geenissä on myös olemassa eri osuuksilla eri roduissa. Tämän tutkimuksen tarkoituksena oli löytää kromosomialueita ja geenejä, jotka vaikuttavat lihan laatuominaisuuksiin. Tutkimuksessa määritettiin 328 Suomalaisen maatiaisrotuisen keinosiemennyskarjun ja 295 yorkshire-rotuisen keinosiemennyskarjun genotyypit noin 50 000 SNP (single nucleotide polymorhism) suhteen käyttämällä kaupallista Illumina PorcineSNP60 sirua. Tutkittavina muuttujina käytettiin kansallisesta jalostusarvostelusta saatuja jalostusarvon ennusteita eri lihanlaatuominaisuuksien suhteen. SNP assosiaatio lihanlaatuominaisuuksien suhteen testattiin painotetun lineaarisen mallin avulla, joka sisälsi SNP-vaikutuksen lisäksi eläinten sukulaisuudet huomioivan polygeenisen tekijän. Mallin painokerroin perustui jalostusarvojen ennusteiden luotettavuudelle. Tilastollisesti merkitseviksi SNP:ksi katsottiin SNP, jonka P-arvo oli alle 2,0E-06 (merkitsevyysraja määritetty Bonferroni-korjauksen avulla). Maatiaisrodulla kaksi SNP kromosomissa 7 (ASGA0037025, 130470366bp, P-arvo=8,6E-07 ja MARC0045334, 133942365bp, P-arvo=1,8E-06) olivat merkitseviä kyljyslihan a*-värin suhteen ja yksi SNP kromosomissa 15 (INRA0050276, 114169040 bp, P-arvo=7,1E-07) oli tilastollisesti merkitsevä sisäpaistin pH-arvon suhteen. Sama kromosomin 15 alue oli myös tilastollisesti merkitsevä yorkshirerodulla (15 eri SNP:ä, joista paras SNP. ALGA0087116, 113451557bp, P-arvo 4,7E-09) sekä kyljyksestä että sisäpaistista mitatun pH:n suhteen. Tilastollisesti merkitsevä alue on 2Mb pitkä ja se sisältää mm. tunnetun RN-geenin (PRKAG3-geeni). Yorkshirerodulla oli myös tilastollisesti merkitsevä SNP kromosomissa 6 (M1GA0008574, 46347641pb, P-arvo= 7,5E-07) kyljyksen a*-värin sekä kuuden SNP määrittämä alue kromosomissa 2 (paras SNP, MARC0005485, 26930095bp, P-arvo= 7,0E-08) kyljyksen L*-värin suhteen. Tulokset osoittavat, että kummassakin kotimaisessa sikarodussa on mahdollista muuttaa lihan laatuominaisuuksia haluttuun suuntaan myös SNP-markkeritiedon avulla

Sianlihan laatuominaisuuksien genominen analyysi SNP-markkereiden avulla

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Genetic analyses of cow-specific diet digestibility based on spectral analysis of faecal samples

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