On the Genetic Interpretation of Disease Data

Bishop, Stephen; Woolliams, John

doi:10.1371/journal.pone.0008940

Cited by 111 publications

(125 citation statements)

References 25 publications

(23 reference statements)

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“…Deterministic predictions indicate a steady, almost linear, increase in GEBV accuracy if up to 20 000 extra records were added to this reference dataset . The rate of increase is conditional on the heritability of the trait and thus artificial challenges for the reference population may still be required to maximize exposure to infection and the potential heritability for WEC (Bishop & Woolliams, 2010), at least under Australian conditions. The next step is to increase the potential LD between markers and polymorphisms by increasing the density of SNP markers.…”

Section: Discussionmentioning

confidence: 99%

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The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections

et al. 2011

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SummaryGenetic resistance to gastrointestinal worms is a complex trait of great importance in both livestock and humans. In order to gain insights into the genetic architecture of this trait, a mixed breed population of sheep was artificially infected with Trichostrongylus colubriformis (n=3326) and then Haemonchus contortus (n=2669) to measure faecal worm egg count (WEC). The population was genotyped with the Illumina OvineSNP50 BeadChip and 48 640 single nucleotide polymorphism (SNP) markers passed the quality controls. An independent population of 316 sires of mixed breeds with accurate estimated breeding values for WEC were genotyped for the same SNP to assess the results obtained from the first population. We used principal components from the genomic relationship matrix among genotyped individuals to account for population stratification, and a novel approach to directly account for the sampling error associated with each SNP marker regression. The largest marker effects were estimated to explain an average of 0 . 48% (T. colubriformis) or 0 . 08 % (H. contortus) of the phenotypic variance in WEC. These effects are small but consistent with results from other complex traits. We also demonstrated that methods which use all markers simultaneously can successfully predict genetic merit for resistance to worms, despite the small effects of individual markers. Correlations of genomic predictions with breeding values of the industry sires reached a maximum of 0 . 32. We estimate that effective across-breed predictions of genetic merit with multi-breed populations will require an average marker spacing of approximately 10 kbp.

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Section: Discussionmentioning

confidence: 99%

“…Livestock provide a suitable model for studying diseases (e.g. Lanzas et al, 2010), particularly when (subclinical) artificial infections are used to maximize the expression of genetic difference between individuals (Bishop & Woolliams, 2010).…”

Section: Introductionmentioning

confidence: 99%

The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections

et al. 2011

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“…Therefore, it may lower the biases due to incomplete exposure on estimable heritabilities (Bishop and Woolliams, 2010). Third, the model provides estimates of the probability of recovery (IMI1 to IMI2 5 a 10 ) and of new infection (IMI2 to IMI1 5 a 01 ) for each animal.…”

Section: Resultsmentioning

confidence: 99%

“…However, the problem of identifying infected cows based on their SCC is still not satisfactorily solved as individual SCC are not very sensitive in diagnosing mammary infection, either at the quarter or cow level (Sargeant et al, 2001;Djabri et al, 2002). This has a relevant impact on animal selection because imperfect accuracy in the diagnosis of infectious diseases results in a reduction of heritability estimates (Bishop and Woolliams, 2010). It is also a source of misclassification as uninfected animals may have high SCC (and vice versa).…”

Section: Introductionmentioning

confidence: 99%

A hidden Markov model to predict early mastitis from test-day somatic cell scores

Detilleux

2011

Animal

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In many countries, high somatic cell scores (SCS) in milk are used as an indicator for mastitis because they are collected on a routine basis. However, individual test-day SCS are not very accurate in identifying infected cows. Mathematical models may improve the accuracy of the biological marker by making better use of the information contained in the available data. Here, a simple hidden Markov model (HMM) is described mathematically and applied to SCS recorded monthly on cows with or without clinical mastitis to evaluate its accuracy in estimating parameters (mean, variance and transition probabilities) under healthy or diseased states. The SCS means were estimated at 1.96 (s.d. 5 0.16) and 4.73 (s.d. 5 0.71) for the hidden healthy and infected states, and the common variance at 0.83 (s.d. 5 0.11). The probability of remaining uninfected, recovering from infection, getting newly infected and remaining infected between consecutive test days was estimated at 78.84%, 60.49%, 11.70% and 15%, respectively. Three different health-related states were compared: clinical stages observed by farmers, subclinical cases defined for somatic cell counts below or above 250 000 cells/ml and infected stages obtained from the HMM. The results showed that HMM identifies infected cows before the appearance of clinical and subclinical signs, which may critically improve the power of the studies on the genetic determinants of SCS and reduce biases in predicting breeding values for SCS.

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“…production and/or resistance to diseases in dairy cattle) (Lillehammer et al, 2011). This dual consequence facilitates the incorporation of disease-resistant loci information into livestock breeding (Bishop and Woolliams, 2010).Infectious diseases in livestock are likely to increase considerably production costs, as is the case for mastitis which may cost up to 2 billion €/year in Europe (http://www. sabre-eu.eu/).…”

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confidence: 99%

An alternative experimental case–control design for genetic association studies on bovine mastitis

Biffani

Corvo

Capoferri

et al. 2017

Animal

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The possibility of using genetic control strategies to increase disease resistance to infectious diseases relies on the identification of markers to include in the breeding plans. Possible incomplete exposure of mastitis-free (control) animals, however, is a major issue to find relevant markers in genetic association studies for infectious diseases. Usually, designs based on elite dairy sires are used in association studies, but an epidemiological case-control strategy, based on cows repeatedly field-tested could be an alternative for disease traits. To test this hypothesis, genetic association results obtained in the present work from a cohort of Italian Holstein cows tested for mastitis over time were compared with those from a previous genome-wide scan on Italian Holstein sires genotyped with 50k single nucleotide polymorphisms for de-regressed estimated breeding values for somatic cell counts (SCCs) on Bos taurus autosome (BTA6) and BTA14. A total of 1121 cows were selected for the case-control approach (cases = 550, controls = 571), on a combination of herd level of SCC incidence and of within herd individual level of SCC. The association study was conducted on nine previously identified markers, six on BTA6 and four on BTA14, using the R statistical environment with the 'qtscore' function of the GenABEL package, on high/low adjusted linear score as a binomial trait. The results obtained in the cow cohort selected on epidemiological information were in agreement with those obtained from the previous sire genome-wide association study (GWAS). Six out of the nine markers showed significant association, four on BTA14 (rs109146371, rs109234250, rs109421300, rs109162116) and two on BTA6 (rs110527224 and rs42766480). Most importantly, using mastitis as a case study, the current work further validated the alternative use of historical field disease data in case-control designs for genetic analysis of infectious diseases in livestock.Keywords: experimental design, association study, mastitis, cattle, genetic marker ImplicationsRecent developments in livestock genomic research have shown that host genetics has a significant role in the spread of an infectious disease. Experimental plans powerful enough to quantify the genetic variation in disease resistance in most livestock species require field data that, however, is very noisy due to incomplete exposure or imperfect diagnosis. The current work has demonstrated the potential use of individual historical field disease data to set an alternative experimental case-control designs for the study of genetic associations with infectious diseases in livestock aiming to reduce noise in field data, using mastitis in cattle as case study. IntroductionControl of major infectious disease in livestock remains challenging despite the detailed characterisation of the infectious agents associated with common diseases and the deciphering of their genome sequences (Tomley and Shirley, 2009). Furthermore, recent developments in livestock genetic and genomic research have shown that h...

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On the Genetic Interpretation of Disease Data

Cited by 111 publications

References 25 publications

The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections

The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections

A hidden Markov model to predict early mastitis from test-day somatic cell scores

An alternative experimental case–control design for genetic association studies on bovine mastitis

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