Our long-term objective is to develop breeding strategies for improving feed efficiency in dairy cattle. In this study, phenotypic data were pooled across multiple research stations to facilitate investigation of the genetic and nongenetic components of feed efficiency in Holstein cattle. Specifically, the heritability of residual feed intake (RFI) was estimated and heterogeneous relationships between RFI and traits relating to energy utilization were characterized across research stations. Milk, fat, protein, and lactose production converted to megacalories (milk energy; MilkE), dry matter intakes (DMI), and body weights (BW) were collected on 6,824 lactations from 4,893 Holstein cows from research stations in Scotland, the Netherlands, and the United States. Weekly DMI, recorded between 50 to 200 d in milk, was fitted as a linear function of MilkE, BW0.75, and change in BW (ΔBW), along with parity, a fifth-order polynomial on days in milk (DIM), and the interaction between this polynomial and parity in a first-stage model. The residuals from this analysis were considered to be a phenotypic measure of RFI. Estimated partial regression coefficients of DMI on MilkE and on BW0.75 ranged from 0.29 to 0.47 kg/Mcal for MilkE across research stations, whereas estimated partial regression coefficients on BW0.75 ranged from 0.06 to 0.16 kg/kg0.75. Estimated partial regression coefficients on ΔBW ranged from 0.06 to 0.39 across stations. Heritabilities for country-specific RFI were based on fitting second-stage random regression models and ranged from 0.06 to 0.24 depending on DIM. The overall heritability estimate across all research stations and all DIM was 0.15±0.02, whereas an alternative analysis based on combining the first- and second-stage model as 1 model led to an overall heritability estimate of 0.18±0.02. Hence future genomic selection programs on feed efficiency appear to be promising; nevertheless, care should be taken to allow for potentially heterogeneous variance components and partial relationships between DMI and other energy sink traits across environments when determining RFI.

No abstract

The swamp type of the Asian water buffalo is assumed to have been domesticated by about 4000 years BP, following the introduction of rice cultivation. Previous localizations of the domestication site were based on mitochondrial DNA (mtDNA) variation within China, accounting only for the maternal lineage. We carried out a comprehensive sampling of China, Taiwan, Vietnam, Laos, Thailand, Nepal and Bangladesh and sequenced the mtDNA Cytochrome b gene and control region and the Y-chromosomal ZFY, SRY and DBY sequences. Swamp buffalo has a higher diversity of both maternal and paternal lineages than river buffalo, with also a remarkable contrast between a weak phylogeographic structure of river buffalo and a strong geographic differentiation of swamp buffalo. The highest diversity of the swamp buffalo maternal lineages was found in south China and north Indochina on both banks of the Mekong River, while the highest diversity in paternal lineages was in the China/Indochina border region. We propose that domestication in this region was later followed by introgressive capture of wild cows west of the Mekong. Migration to the north followed the Yangtze valley as well as a more eastern route, but also involved translocations of both cows and bulls over large distances with a minor influence of river buffaloes in recent decades. Bayesian analyses of various migration models also supported domestication in the China/Indochina border region. Coalescence analysis yielded consistent estimates for the expansion of the major swamp buffalo haplogroups with a credibility interval of 900 to 3900 years BP. The spatial differentiation of mtDNA and Y-chromosomal haplotype distributions indicates a lack of gene flow between established populations that is unprecedented in livestock.

Genetic improvement of feed efficiency (FE) in dairy cattle requires greater attention given increasingly important resource constraint issues. A widely accepted yet occasionally contested measure of FE in dairy cattle is residual feed intake (RFI). The use of RFI is limiting for several reasons, including interpretation, differences in recording frequencies between the various component traits that define RFI, and potential differences in genetic versus nongenetic relationships between dry matter intake (DMI) and FE component traits. Hence, analyses focusing on DMI as the response are often preferred. We propose an alternative multiple-trait (MT) modeling strategy that exploits the Cholesky decomposition to provide a potentially more robust measure of FE. We demonstrate that our proposed FE measure is identical to RFI provided that genetic and nongenetic relationships between DMI and component traits of FE are identical. We assessed both approaches (MT and RFI) by simulation as well as by application to 26,383 weekly records from 50 to 200 d in milk on 2,470 cows from a dairy FE consortium study involving 7 institutions. Although the proposed MT model fared better than the RFI model when simulated genetic and nongenetic associations between DMI and FE component traits were substantially different from each other, no meaningful differences were found in predictive performance between the 2 models when applied to the consortium data.

Scientists often interpret P-values as measures of the relative strength of statistical findings. This is common practice in large-scale genomic studies where P-values are used to choose which of numerous hypothesis test results should be pursued in subsequent research. In this study, we examine P-value variability to assess the degree of certainty P-values provide. We develop prediction intervals for the P-value in a replication study given the P-value observed in an initial study. The intervals depend on the initial value of P and the ratio of sample sizes between the initial and replication studies, but not on the underlying effect size or initial sample size. The intervals are valid for most large-sample statistical tests in any context, and can be used in the presence of single or multiple tests. While P-values are highly variable, future P-value variability can be explicitly predicted based on a P-value from an initial study. The relative size of the replication and initial study is an important predictor of the P-value in a subsequent replication study. We provide a handy calculator implementing these results and apply them to a study of Alzheimer's disease and recent findings of the Cross-Disorder Group of the Psychiatric Genomics Consortium. This study suggests that overinterpretation of very significant, but highly variable, P-values is an important factor contributing to the unexpectedly high incidence of non-replication. Formal prediction intervals can also provide realistic interpretations and comparisons of P-values associated with different estimated effect sizes and sample sizes.