J.B.C.H.M. van Kaam

Genomic imputation and evaluation using high-density Holstein genotypes

Genomic evaluations for 161,341 Holsteins were computed by using 311,725 of 777,962 markers on the Illumina BovineHD Genotyping BeadChip (HD). Initial edits with 1,741 HD genotypes from 5 breeds revealed that 636,967 markers were usable but that half were redundant. Holstein genotypes were from 1,510 animals with HD markers, 82,358 animals with 45,187 (50K) markers, 1,797 animals with 8,031 (8K) markers, 20,177 animals with 6,836 (6K) markers, 52,270 animals with 2,683 (3K) markers, and 3,229 nongenotyped dams (0K) with >90% of haplotypes imputable because they had 4 or more genotyped progeny. The Holstein HD genotypes were from 1,142 US, Canadian, British, and Italian sires, 196 other sires, 138 cows in a US Department of Agriculture research herd (Beltsville, MD), and 34 other females. Percentages of correctly imputed genotypes were tested by applying the programs findhap and FImpute to a simulated chromosome for an earlier population that had only 1,112 animals with HD genotypes and none with 8K genotypes. For each chip, 1% of the genotypes were missing and 0.02% were incorrect initially. After imputation of missing markers with findhap, percentages of genotypes correct were 99.9% from HD, 99.0% from 50K, 94.6% from 6K, 90.5% from 3K, and 93.5% from 0K. With FImpute, 99.96% were correct from HD, 99.3% from 50K, 94.7% from 6K, 91.1% from 3K, and 95.1% from 0K genotypes. Accuracy for the 3K and 6K genotypes further improved by approximately 2 percentage points if imputed first to 50K and then to HD instead of imputing all genotypes directly to HD. Evaluations were tested by using imputed actual genotypes and August 2008 phenotypes to predict deregressed evaluations of US bulls proven after August 2008. For 28 traits tested, the estimated genomic reliability averaged 61.1% when using 311,725 markers vs. 60.7% when using 45,187 markers vs. 29.6% from the traditional parent average. Squared correlations with future data were slightly greater for 16 traits and slightly less for 12 with HD than with 50K evaluations. The observed 0.4 percentage point average increase in reliability was less favorable than the 0.9 expected from simulation but was similar to actual gains from other HD studies. The largest HD and 50K marker effects were often located at very similar positions. The single-breed evaluation tested here and previous single-breed or multibreed evaluations have not produced large gains. Increasing the number of HD genotypes used for imputation above 1,074 did not improve the reliability of Holstein genomic evaluations.

Whole genome scan for quantitative trait loci affecting body weight in chickens using a three generation design

Abstract An experimental population containing 10 full sib families of a cross between two broiler lines was created. In this population blood samples from 20 full sib animals in generation 1 and 451 full sib animals in generation 2 were used for marker genotyping. Data on body weight at slaughter age (48 days) collected in a feed conversion experiment with 2049 individually housed grandoffspring was analysed. Large differences in mean and variance between male and female body weight were found. To account for these differences, a bivariate analysis treating body weight of males and females as separate traits was used to estimate (co)variance components and breeding values. The model accounted for systematic environmental effects and maternal effects. The estimated heritability of body weight was 0.28 in the males and 0.33 in the females and the genetic correlation between male and female body weight did not significantly deviate from unity. Estimated breeding values, fixed and maternal genetic effects were used to calculate average adjusted progeny trait values for all generation 2 animals adjusted for fixed and maternal genetic effects and for the additive genetic contribution of the other parent. Male and female progeny trait values were combined in one trait value adjusting for sex differences by standardisation for mean and variance. This average adjusted progeny trait value was used for QTL detection. To study presence of QTLs, an across family weighted regression interval mapping approach was used both in half sib as well as a full sib QTL analysis. Genotypes from 368 markers mapped on 24 autosomal linkage groups were available. The most likely position for a QTL affecting body weight was found on chromosome 1 at 240 cM with a test statistic of 2.32. Significance levels were obtained using the permutation test. The chromosomewise significance level of this QTL was 10%, whereas the genomewise significance level was 41%. New aspects of this study are: Genomewide QTL analysis in poultry, full sib analysis in an outbred population structure and correction for heterogeneous variances between sexes.

QTL Mapping in chicken using a three generation full sib family structure of an extreme broiler X broiler cross

Abstract A three generation population has been created for mapping both production and health traits in chicken. The Fl and F2 population were genotyped while phenotypes were collected on the F3 animals. The population consisted of 10 full‐sib families with a total of 476 individuals (Fl and F2), and an F3 generation consisting of over 18,000 animals. In total, 264 microsatellites were analyzed on all Fl and F2 animals, and an additional 120 microsatellites were analyzed on only 4 of the 10 families (196 animals). A linkage map of the chicken genome containing 384 microsatellite markers has been constructed by analyzing the segregation of these markers in this population. Preliminary analysis indicate a QTL for body weight at 48 days on chromosome 1. Body weight was measured on 2100 F3 animals housed in cages, and the data was analyzed by a regression interval mapping approach. Higher F‐values were obtained by using a bivariate approach showing that differences in mean and variance of a trait measured on...

Comparison between different selection criteria in the genetic evaluation of Valle del Belice sheep

Lactation length in dairy sheep affects milk yield like other genetic and environmental factors. The length of the production period is affected by management decisions such as culling, mating and particularly ranking of animals with different parity and lambing in different months or seasons. Moreover the low heritability of lactation length (Barillet and Boichard, 1987; Dahlin et al., 1998) does not allow its use as a selection criterion. For this reason to achieve a good reliability in phenotypic and genetic evaluation of dairy species, production variability caused by systematic environmental effects must be removed. This is of particular interest for dairy sheep and goats reared in Sicily, where the typical production system is based on pasture, and related food availability is strongly affected by seasonal and annual climatic variations, which results in considerable variations in daily yields........

Effect of weather conditions on somatic cell score in Sicilian Valle del Belice ewes

Abstract Mastitis susceptibility of Valle del Belice ewes from the south of Sicily to temperature, humidity, precipitation, solar radiation, sun hours, air pressure, wind-speed and wind-direction measured by a public weather station was investigated using 65,848 test-day somatic cell score (SCS) records of 5,237 ewes. All weather parameters had an effect on SCS in a regression approach. Extreme values of maximum and minimum temperature-humidity indices resulted in increased SCS. Higher precipitation, solar radiation and sun hours resulted in increased SCS, whereas higher air pressure and wind speed resulted in reduced SCS.

Effect of heat stress on production in Mediterranean dairy sheep

Riassunto Effetto dello stress termico sulle produzioni quanti-qualitative di ovini da latte allevati nel Mediterraneo. Il data-set comprende 59.661 test-days appartenenti a 6.624 lattazioni. Le variabili dipendenti sono le produzioni giornaliere di latte (g) e di grasso+proteina (g). Per la stima dei parametri genetici è stato utilizzato un repeatability test-day model. Il pedigree comprende 5.306 animali. La produzione di latte giornaliera mostra un decremento quando l’indice di temperatura-umidità (THI) raggiunge 23. Le correlazioni fenotipiche di THI≥ 23 con la produzione di latte sono state per entrambi i caratteri e i giorni considerati (stesso giorno e giorno prima del controllo) sempre negative (circa -0.3). Le correlazioni genetiche tra la produzione di latte generale e la tolleranza al caldo sono negative (circa -0.8) per entrambi i giorni e i caratteri (latte e gras-so+proteina) considerati. Pertanto la produzione di latte è antagonista della tolleranza al caldo e la selezione diretta solo alla produzione di latte ridurrà la tolleranza al caldo.

Pedigree verification with multiple candidate parents using Pedverif software

Abstract Species like sheep and beef cattle are commonly raised in large herds and often on pasture with multiple sires joining the females for unrecorded natural insemination. This leads to offspring with multiple candidate parents and therefore uncertain parentage. Twins or triplets can be from multiple sires as well. Pedigrees from such populations are often problematic and need proper verification. The Pedverif computer program is able to verify normal pedigrees as well as pedigrees with multiple candidate parents.

Evaluation of in-farm versus weather station data for use as heat stress indicator in dairy sheep

Abstract Heat stress is a limiting factor in dairy production in hot climates impairing growth, milk production and reproduction. The most widely investigated climatic factors related with heat stress are: air temperature and relative humidity. Previously dairy sheep studies of heat tolerance depended on measurements of physiological functions on individual animals such as rectal temperatures, respiration rates or volumes of air inhaled; unfortunately, such measurements are costly and not feasible on a large scale. This study aims to evaluate in-farm (IF) versus weather station (WS) data to be used as heat stress indicator in dairy sheep. Data were collected in three farms in November 2002 till July 2003. Maximum temperature (T) and relative humidity (RH) were monitored by means of thermo-hygrographs placed in the farms at a height of 1.5m above the ground. Both IF and WS data were taken 24 h before milk recording. The data contained 1,059 test-day records belonging to 275 Valle del Belice ewes. The correlation of WS-T with IF-T was 0.83 and with IF-RH was -0.70. The correlation of WS-RH with IF-T was -0.77 and with IF-RH was 0.78. The correlation of milk production with WS-T was -0.49, with WS-RH 0.46, with IF-T -0.50 and with IF-RH 0.30. GLM analyses undertaken were based on models that included fixed effects of flock, DIM, and T RH or Temperature-humidity index (THI). This resulted in a decrease of milk production of -49.7 g per unit increase of T RH if IF data were used versus a decrease of -36.6 g per unit increase T RH if WS information were used. However when using the THI with IF climatic information there was a decrease of -35.0 g per unit increase of THI versus -44.8 g using WS data. By comparing the models, using the R2 and root MSE, these were always slightly better when using WS rather than IF information, especially with THI. Therefore it seems that the use of weather stations might replace the IF collection