Genetic factors affecting the composition and quality of cow’s milk

This chapter reviews the genetic factors affecting the composition and quality of cow's milk. It starts by discussing how different breeds of cattle can affect the composition of milk. The chapter then goes on to examine milk proteins, genetic variants and post-translational modifications. It then discusses milk coagulation and other functional properties, before highlighting the genetic influence of fatty acids on minor milk components. The chapter also discusses using mid-infrared spectroscopy for genetic parameter estimation, before concluding with a section on the possibilities for genetic improvement in relation to dairy milk.

Influence of model selection and data structure on the estimation of genetic parameters in honeybee populations

Estimating genetic parameters of quantitative traits is a prerequisite for animal breeding. In honeybees, the genetic variance separates into queen and worker effects. However, under data paucity, parameter estimations that account for this peculiarity often yield implausible results. Consequently, simplified models which attribute all genetic contributions to either the queen (queen model) or the workers (worker model) are often used to estimate variance components in honeybees. However, the causes for estimations with the complete model (colony model) to fail and the consequences of simplified models for variance estimates are little understood. We newly developed the necessary theory to compare parameter estimates that were achieved by the colony model with those of the queen and worker models. Furthermore, we performed computer simulations to quantify the influence of model choice, estimation algorithm, true genetic parameters, rates of controlled mating, apiary sizes, and phenotype data completeness on the success of genetic parameter estimations. We found that successful estimations with the colony model were only possible if at least some of the queens mated controlledly on mating stations. In that case, estimates were largely unbiased if more than 20% of the colonies had phenotype records. The simplified queen and worker models proved more stable and yielded plausible parameter estimates for almost all settings. Results obtained from these models were unbiased when mating was uncontrolled, but with controlled mating, the simplified models consistently overestimated heritabilities. This work elucidates the requirements for variance component estimation in honeybees and provides the theoretical groundwork for simplified honeybee models.

Growth and Genetic Parameters of Mindi (Melia azedarach Linn.) Progeny Test in Parungpanjang Forest Research Station, Bogor

Mindi (Melia azedarach Linn., family Meliaceae) is a multipurpose forest tree species such as for furniture, vinier and plywood, otherwise the leaves for pesticide and medicine material. Mindi mostly cultivated in a community forest, especially in West Java. This study was aimed to assess the growth performance and genetic parameter of the progeny test of mindi at Parungpanjang Forest Research Station, Bogor, West Java. The progeny test was established by using a randomized complete block design, consisting of 6 blocks and 73 families and each family consisting of 4 trees row-plot. The survival at 7 years old was 57.48%, height was 7.15 m and diameter is 8.59 cm, in average. The height and diameter growths among families showed a significantly different. The best family growth in height and diameter were Sumedang-31 and Bogor-18 families, respectively.The individual and family heritabilities for both stem diameter and tree heght characters were stil relatively low, with the higher family heritability value than individual heritability. The genetic correlation between total height and stem diameter of 0.57 indicates a strong correlation between the traits.

SSR MARKERS REVEALED GENETIC DIVERGENCE OF RICE BROWN PLANTHOPPER POPULATIONS MAINTAINED ON TWO SETS OF DIFFERENTIAL HOST VARIETIES

<p>Resistance screening of promising rice lines in Indonesia requires the use of brown planthopper (BPH) biotypes 1, 2, and 3. Three BPH populations have been raised as biotypes 1, 2, and 3 on differential rice host of improved varieties Pelita I-1 (no <em>Bph </em>gene), IR26 (<em>Bph1</em>), and IR42 (<em>bph2</em>), respectively. Three alternative populations have also been developed on the respective traditional varieties TN1 (no <em>Bph </em>gene), Mudgo (<em>Bph1</em>), and ASD7 (<em>bph2</em>). Although these populations displayed two virulent patterns other than biotype 1 to 3 phenotypes, they were expected to be discriminated into two virulence groups by SSR analysis. The study aimed to investigate the level of genetic variation among the six BPH populations using SSR markers and to relate it with the observed virulence patterns. Genotyping of 30 females with 29 polymorphic SSR markers revealed higher genetic parameter values in populations reared on improved varieties than those on traditional varieties. This difference was marked as two population clusters in PCoA plots corresponding to the host variety type, in contrast to the previous assumption that clustering would be based on virulence patterns. The presence of individuals with unwanted virulence allele, either resulting from contamination during the long period of rearing or lack of host adaptation period, is suspected. The result of this study indicates that the six populations are not suitable for resistance screening. Virulence selection must be performed until they attain biotype 1 to 3 phenotypes which can be genetically separated by DNA markers.</p>

Elucidating Genetic Diversity in Apricot (Prunus armeniaca L.) Cultivated in the North-Western Himalayan Provinces of India Using SSR Markers

Apricot (Prunus armeniaca L.) is an important temperate fruit crop worldwide. The availability of wild apricot germplasm and its characterization through genomic studies can guide us towards its conservation, increasing productivity and nutritional composition. Therefore, in this study, we carried out the genomic characterization of 50 phenotypically variable accessions by using SSR markers in the erstwhile States of Jammu and Kashmir to reveal genetic variability among accessions and their genetic associations. The genetic parameter results revealed that the number of alleles per locus (Na) ranged from 1 to 6 with a mean Na value of 3.89 and the mean effective number of alleles (Ne) per locus 1.882 with a range of 1.22 to 2. Similarly, the polymorphic information content (PIC) values ranged from 0.464 to 0.104. The observed heterozygosity (Ho) (0.547) was found to have higher than expected heterozygosity (He) (0.453) with average heterozygosity of 0.4483. The dendrogram clustered genotypes into three main clades based on their pedigree. The population structure revealed IV sub-populations with all admixtures except the III sub-population, which was mainly formed of exotic cultivars. The average expected heterozygosity (He) and population differentiation within four sub-populations was 1.78 and 0.04, respectively, and explained 95.0% of the total genetic variance in the population. The results revealed that the SSR marker studies could easily decrypt the genetic variability present within the germplasm, which may form the base for the establishment of good gene banks by reducing redundancy of germplasm, selection of parents for any breeding program.

Novel Tools for Adjusting Spatial Variability in the Early Sugarcane Breeding Stage

The detection of spatial variability in field trials has great potential for accelerating plant breeding progress due to the possibility of better controlling non-genetic variation. Therefore, we aimed to evaluate a digital soil mapping approach and a high-density soil sampling procedure for identifying and adjusting spatial dependence in the early sugarcane breeding stage. Two experiments were conducted in regions with different soil classifications. High-density sampling of soil physical and chemical properties was performed in a regular grid to investigate the structure of spatial variability. Soil apparent electrical conductivity (ECa) was measured in both experimental areas with an EM38-MK2® sensor. In addition, principal component analysis (PCA) was employed to reduce the dimensionality of the physical and chemical soil data sets. After conducting the PCA and obtaining different thematic maps, we determined each experimental plot’s exact position within the field. Tons of cane per hectare (TCH) data for each experiment were obtained and analyzed using mixed linear models. When environmental covariates were considered, a previous forward model selection step was applied to incorporate the variables. The PCA based on high-density soil sampling data captured part of the total variability in the data for Experimental Area 1 and was suggested to be an efficient index to be incorporated as a covariate in the statistical model, reducing the experimental error (residual variation coefficient, CVe). When incorporated into the different statistical models, the ECa information increased the selection accuracy of the experimental genotypes. Therefore, we demonstrate that the genetic parameter increased when both approaches (spatial analysis and environmental covariates) were employed.

Quantitative Genetics of Smoltification Status at the Time of Seawater Transfer in Atlantic Salmon (Salmo Salar)

High mortality during grow out in the sea is a challenge for farmed Atlantic salmon production in Norway and globally, which is partly attributed to suboptimal smolt quality. In this study, two groups of pre-smolts were put on a standard light smoltification regime with alternating 12L:12D per day for 6 weeks (Phase I), followed by 24L:0D per day for 6 weeks (Phase II); one group was 0 + smolt (EXP1) and the other 1 + smolt (EXP2). To monitor the smoltification status of the fish, 100 (EXP1) and 60 (EXP2) fish were randomly sampled per week during Phase II. The following phenotypes for smoltification status were studied: RT-qPCR relative mRNA expression of values of two alpha catalytic subunits of the variants of the Na+K+ATPase (NKA) expressed in the sampled gill tissues of each fish. The first variant, alpha1a with increased expression in freshwater (FW) and the second variant alpha1b with increased expression in seawater variant (SW), as well as their ratio SW/FW. At the optimal time for seawater transfer based on the SW/FW trait, 1,000 (at sixth sampling of EXP1) and 1,500 (at fifth sampling of EXP2) fish were sampled for genetic parameter estimation. The individual variation in FW, SW, and SW/FW was very large at each of the seven samplings indicating a large variation among individuals in the optimum time of transfer to seawater. SW/FW showed significant genetic variation in both 0+ and 1+ smolts, which indicates the possibility for selection for improved synchronization of smoltification status of Atlantic salmon at the time where the largest proportion of the fish is considered to be smolt. However, the genetic correlation between SW/FW of 0+ and 1+ was not significantly different from zero indicating very little shared genetic variation in SW/FW in 0+ and 1+ fish. Smoltification phenotypes showed temporal progression over the smoltification period, and this progression varied between 0+ and 1+ smolt highlighting the importance of correctly timing the major sampling point, and when cohorts are transferred to seawater. This also highlighted the need for further research into noninvasive methods of objectively measuring individual smoltification through time and subsequent smolt survival and growth rate at sea.