Luiz Brito's Lab

About the lab

Our Lab focuses on complex traits associated with animal behavior and welfare, environmental efficiency and adaptation to challenging environments. Our long-term research goals are to integrate different data sources (pedigree, phenotypic and comprehensive “-omics”-derived data) to better understand the genetic basis underlying the phenotypic variability observed in these traits. We work towards the development of selection methods and approaches to enable efficient incorporation of these traits into livestock breeding programs, while maintaining enough populational genetic diversity. We work across livestock species, including the development of genomic approaches applied to breeding of minor species (e.g., alpacas, buffaloes, sheep, goats) as well as in small-holder production systems.

Featured research (38)

Selection for resilience indicator (RIND) traits in Holstein cattle is becoming an important breeding objective as the worldwide population is expected to be exposed to increased environmental stressors due to both climate change and changing industry standards. However, genetic correlations between RIND and productivity indicator (PIND) traits, which are already being selected for and have the most economic value, are often unfavorable. As a result, it is necessary to fully understand these genetic relationships when incorporating novel traits into selection indices, so that informed decisions can be made to fully optimize selection for both groups of traits. In the past 2 decades, there have been many estimates of RINDs published in the literature, albeit in small populations. To provide valuable pooled summary estimates, a random-effects meta-analysis was conducted for heritability and genetic correlation estimates for PIND and RIND traits in worldwide Holstein cattle. In total, 926 heritabil-ity estimates for 9 PIND and 27 RIND traits, along with 362 estimates of genetic correlation (PIND x RIND traits) were collected. Resilience indicator traits were grouped into the following subgroups: Metabolic Diseases, Hoof Health, Udder Health, Fertility, Heat Tolerance, and Other. Pooled estimates of heritability for PIND traits ranged from 0.201 ± 0.05 (energy corrected milk) to 0.377 ± 0.06 (protein content) while pooled estimates of heritability for RIND traits ranged from 0.032 ± 0.02 (incidence of lameness, incidence of milk fever) to 0.497 ± 0.05 (measures of body weight). Pooled estimates of genetic correlations ranged from −0.360 ± 0.25 (protein content vs. milk acetone concentration) to 0.535 ± 0.72 (measures of fat-to-protein ratio vs. milk acetone concentration). Additionally, out of 243 potential genetic correlations between PIND and RIND traits that could have been reported, only 40 had enough published estimates to implement the meta-analysis model. Our results confirmed that the interactions between PIND and RIND traits are complex , and all relationships should be evaluated when incorporating novel traits into selection indices. This study provides a valuable reference for breeders looking to incorporate RIND traits for Holstein cattle into selection indices.
Genetic improvement of livestock productivity has resulted in greater production of metabolic heat and potentially greater susceptibility to heat stress. Various studies have demonstrated that there is genetic variability for heat tolerance and genetic selection for more heat tolerant individuals is possible. The rate of genetic progress tends to be greater when genomic information is incorporated into the analyses as more accurate breeding values can be obtained for young individuals. Therefore, this study aimed (1) to evaluate the predictive ability of genomic breeding values for heat tolerance based on routinely recorded traits, and (2) to investigate the genetic background of heat tolerance based on single-step genome-wide association studies for economically important traits related to body composition, growth and reproduction in Large White pigs. Pedigree information was available for 265,943 animals and genotypes for 8686 animals. The studied traits included ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off-test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN) and weaning-to-estrus interval (IWE). The number of phenotypic records ranged from 6059 (WN) to 172,984 (TNB). Single-step genomic reaction norm predictions were used to calculate the genomic estimated breeding values for each individual. Predictions of breeding values for the validation population individuals were compared between datasets containing phenotypic records measured in the whole range of temperatures (WR) and datasets containing only phenotypic records measured when the weather station temperature was above 10°C (10C) or 15°C (15C), to evaluate the usefulness of these datasets that may better reflect the within-barn temperature. The use of homogeneous or heterogeneous residual variance was found to be trait-dependent, where homogeneous variance presented the best fit for MDP, BFT, OTW, TNB, NBA, WN and IBF, while the other traits (WW and IWE) had better fit with heterogeneous variance. The average prediction accu- racy, dispersion and bias values considering all traits for WR were 0.36±0.05, −0.07±0.13 and 0.76±0.10, respectively; for 10C were 0.39±0.02, −0.05±0.07 and 0.81±0.05, respectively; and for 15C were 0.32±0.05, −0.05±0.11 and 0.84±0.10, respectively. Based on the studied traits, using phenotypic records collected when the outside temperature (from public weather stations) was above 10°C provided better predictions for most of the traits. Forty-three and 62 can- didate genomic regions were associated with the intercept (overall performance level) and slope term (specific biological mechanisms related to environmental sensitivity), respectively. Our results contribute to improve genomic predictions using existing datasets and better understand the genetic background of heat tol- erance in pigs. Furthermore, the genomic regions and candidate genes identified will contribute to future genomic studies and breeding applications.
Background Hoof structure and health are essential for the welfare and productivity of beef cattle. Therefore, we assessed the genetic and genomic background of foot score traits in American (US) and Australian (AU) Angus cattle and investigated the feasibility of performing genomic evaluations combining data for foot score traits recorded in US and AU Angus cattle. The traits evaluated were foot angle (FA) and claw set (CS). In total, 109,294 and ~ 1.12 million animals had phenotypic and genomic information, respectively. Four sets of analyses were performed: (1) genomic connectedness between US and AU Angus cattle populations and population structure, (2) estimation of genetic parameters, (3) single-step genomic prediction of breeding values, and (4) single-step genome-wide association studies for FA and CS. Results There was no clear genetic differentiation between US and AU Angus populations. Similar heritability estimates (FA: 0.22–0.24 and CS: 0.22–0.27) and moderate-to-high genetic correlations between US and AU foot scores (FA: 0.61 and CS: 0.76) were obtained. A joint-genomic prediction using data from both populations outperformed within-country genomic evaluations. A genomic prediction model considering US and AU datasets as a single population performed similarly to the scenario accounting for genotype-by-environment interactions (i.e., multiple-trait model considering US and AU records as different traits), even though the genetic correlations between countries were lower than 0.80. Common significant genomic regions were observed between US and AU for FA and CS. Significant single nucleotide polymorphisms were identified on the Bos taurus (BTA) chromosomes BTA1, BTA5, BTA11, BTA13, BTA19, BTA20, and BTA23. The candidate genes identified were primarily from growth factor gene families, including FGF12 and GDF5 , which were previously associated with bone structure and repair. Conclusions This study presents comprehensive population structure and genetic and genomic analyses of foot scores in US and AU Angus cattle populations, which are essential for optimizing the implementation of genomic selection for improved foot scores in Angus cattle breeding programs. We have also identified candidate genes associated with foot scores in the largest Angus cattle populations in the world and made recommendations for genomic evaluations for improved foot score traits in the US and AU.
Hoof diseases is a major welfare and economic issue in the worldwide dairy cattle production industry, which can be minimized through improved management and breeding practices. Optimal genetic improvement of hoof health could benefit from a deep understanding of the genetic background and biological underpinning of indicators of hoof health. Therefore, the primary objectives of this study were to perform genome-wide association studies, using imputed high-density genetic markers data from North American Holstein cattle, for 8 hoof-related traits: digital dermatitis, sole ulcer, sole hemorrhage, white line lesion, heel horn erosion, interdigital dermatitis, interdigital hyperplasia, and toe ulcer, and a hoof health index. De-regressed estimated breeding values (dEBVs) from 25,580 Holstein animals were used as pseudo-phenotypes for the association analyses. The genomic quality control, genotype phas- ing, and genotype imputation were performed using the PLINK, Eagle, and Minimac4 software, respectively. The functional genomic analyses were performed us- ing the GALLO R package and the DAVID platform. Twenty-two, 34, 14, 22, 28, 33, 24, 43, and 15 significant markers were identified for digital dermatitis, heel horn erosion, interdigital dermatitis, interdigital hyperplasia, sole hemorrhage, sole ulcer, toe ulcer, white line lesion disease, and the hoof health index, respectively. The significant markers were located across all autosomes, except BTA10, BTA12, BTA20, BTA26, BTA27, and BTA28. Moreover, the genomic regions identified over- lap with various previously reported quantitative trait loci (QTL) for exterior, health, meat and carcass, milk, production, and reproduction traits. The enrichment analyses identified 44 significant gene ontology terms. These enriched genomic regions harbor various candi- date genes previously associated with bone develop- ment, metabolism, and infectious and immunological diseases. These findings indicate that hoof health traits are highly polygenic and influenced by a wide range of biological processes.
Precision Livestock Farming technologies have increased the availability of on-farm data collected from dairy operations, such as automatic milk feeding machines. We analyzed feeding records from AMF to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North Ameri-can Holstein calves. Data from 10,076 pre-weaned female Holstein calves were collected daily over a period of 6 years (3 years included per-visit data) and daily milk consumption (DMC) and per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit (DDPV), daily number of rewarded visits (DNRV), and total number of visits per day (TNV) were recorded over a 60-d pre-weaning period. Additional traits were derived from these variables, including total consumption and duration variance (TDC and TDV), feeding interval, drinking speed (DS), and pre-weaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step GBLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 single nucleotide polymorphisms after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models [0.006 ± 0.0009 to 0.08 ± 0.004]. However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d pre-weaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full data set (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that random regression models capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of −0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.

Lab head

Luiz Fernando Brito
  • Department of Animal Sciences
About Luiz Fernando Brito
  • Luiz Brito has an Honours BSc in Animal Science (2010) and a Master of Science (2012) in Genetics and Animal Breeding from the Federal University of Viçosa (Brazil) as well as a PhD and a 2-year postdoc in Quantitative Genomics (2016) from the University of Guelph (Canada) with an internship at AgResearch (New Zealand). He joined Purdue University in Sept/2018 and is now an Associate Professor of Quantitative Genetics and Genomics:

Members (12)

Shi-Yi Chen
  • Sichuan Agricultural University
Leonardo Glória
  • Universidade Estadual do Norte Fluminense
Artur Oliveira Rocha
  • Purdue University
Amanda Botelho Alvarenga
  • Corteva Agriscience
André Campêlo Araujo
  • Purdue University
Ali Haider Saleem
  • University of Veterinary and Animal Sciences
Hui Wen
  • Purdue University
Leticia F Oliveira
Leticia F Oliveira
  • Not confirmed yet

Alumni (6)

Laís Grigoletto
  • ABS Global, Inc.
Sirlene Lazaro
  • University of Guelph
Hannah Willson
  • Purdue University
Kristen N Cleaver
Kristen N Cleaver