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Fertility traits, such as heifer pregnancy, are economically important in cattle production systems, and are therefore, used in genetic selection programs. The aim of this study was to identify single nucleotide polymorphisms (SNPs) using RNA-sequencing (RNA-Seq) data from ovary, uterus, endometrium, pituitary gland, hypothalamus, liver, longissimus dorsi muscle, and adipose tissue in 62 candidate genes associated with heifer puberty in cattle. RNA-Seq reads were assembled to the bovine reference genome (UMD 3.1.1) and analyzed in five cattle breeds; Brangus, Brahman, Nellore, Angus, and Holstein. Two approaches used the Brangus data for SNP discovery 1) pooling all samples, and 2) within each individual sample. These approaches revealed 1157 SNPs. These were compared with those identified in the pooled samples of the other breeds. Overall, 172 SNPs within 13 genes (CPNE5, FAM19A4, FOXN4, KLF1, LOC777593, MGC157266, NEBL, NRXN3, PEPT-1, PPP3CA, SCG5, TSG101, and TSHR) were concordant in the five breeds. Using Ensembl's Variant Effector Predictor, we determined that 12% of SNPs were in exons (71% synonymous, 29% nonsynonymous), 1% were in untranslated regions (UTRs), 86% were in introns, and 1% were in intergenic regions. Since these SNPs were discovered in RNA, the variants were predicted to be within exons or UTRs. Overall, 160 novel transcripts in 42 candidate genes and five novel genes overlapping five candidate genes were observed. In conclusion, 1157 SNPs were identified in 62 candidate genes associated with puberty in Brangus cattle, of which, 172 were concordant in the five cattle breeds. Novel transcripts and genes were also identified.

Currently, many different data types are collected by beef cattle breed associations for the purpose of genetic evaluation. These data points are all biological characteristics of individual animals that can be measured multiple times over an animal's lifetime. Some traits can only be measured once on an individual animal, whereas others, such as the body weight of an animal as it grows, can be measured many times. Data such as growth has been often referred to as "longitudinal" or "infinite-dimensional" since it is theoretically possible to observe the trait an infinite number of times over the life span of a given individual. Analysis of such data is not without its challenges, and as a result many different methods have been or are beginning to be implemented in the genetic analysis of beef cattle data, each an improvement over its predecessor. These methods of analysis range from the classic repeated measures to the more contemporary suite of random regressions that use covariance functions or even splines as their base function. Each of the approaches has both strengths and weaknesses in the analysis of longitudinal data. Here we summarize past and current genetic evaluation technology for analyzing this type of data and review some emerging technologies beginning to be implemented in national cattle evaluation schemes, along with their potential implications for the beef industry.