The livestock industry is a critical component of the agricultural sector in the United States (U.S.). In 2022, cattle production was anticipated to represent about 17% of the USD 462 billion in total cash receipts for agricultural commodities [1]. Despite cattle numbers declining since 1975, beef production has increased due to advances in management practices, genetics, and nutrition. The beef industry needs to address global issues such as climate change and the use of natural resources in a sustainable manner while producing more food to feed a growing population [2]. Consequently, the future of a sustainable beef industry relies on improved reproductive performance and growth efficiencies. Fortunately, the cow–calf sector holds immense potential (reviewed by ^[3][4][3,4]) to positively contribute towards addressing those emerging issues ^[5][6][5,6].

Cow–calf production is the foundation of the U.S. beef industry, representing 86% of beef operations and 84% of the beef cattle population in the country [7]. However, reproductive inefficiency has been a significant cause of economic loss and a limiting factor for the sustainability of the beef industry ^[8][9][10][8,9,10]. The annual gross loss (due to pregnancy failure) of beef females is estimated to cost USD 2.8 billion in the United States [10]. Strategies to mitigate such losses include selecting and developing heifers with high genetic merit with respect to fertility traits. In addition to being a source of genetic gain for the herd, replacement heifers also represent a large portion of the capital outlay invested by producers ^[11][12][11,12]. Consequently, the selection of heifers is critical for the overall profitability and sustainability of the cow–calf sector. Cushman et al. [13] reported that heifers that calved earlier in the breeding season remained longer in the herd and weaned more kilograms of calf throughout their productive life.

Despite the consensus that good fertility and longevity are essential for the sustainability of the cattle industry, improving reproductive efficiency has been a major challenge worldwide ^[14][15][14,15] due to the various complex factors underlying fertility and related traits. Indicator traits of fertility tend to be low in terms of heritability, are expressed late in life, and are controlled by many genes of small additive and non-additive effects ^{[16][17][18][19]}[16,17,18,19]. Still, there is enough genetic variation underlying fertility to support the improvement of reproductive efficiency ^[14][17][18][14,17,18]. The complex nature of fertility has necessitated the integration of disciplines such as reproductive biology and genetics to accelerate genetic gain [20]. Likewise, the advent of “omics” technologies has provided new capabilities to analyze the structure and function of an organism at different regulatory levels, including DNA (genomic), RNA (transcriptomic), proteins (proteomic), and metabolites (metabolomic) [21]. These advances have improved theour understanding of the genetic architecture governing fertility and have allowed for the more accurate selection of animals with high genetic values ^[20][22][20,22].

Genomic selection (GS) has contributed to the increased productivity of yield traits and the genetic gain of low-heritability traits such as fertility, especially in dairy cattle ^[23][24][23,24]. Although promising, findings from different research groups around the globe are still limited, as no major genes or genetic variants regulating fertility have been reported. Other omics-based approaches have increasingly been used to dissect the molecular basis of fertility and provide a more comprehensive understanding of the biological pathways associated with reproductive success ^{[25][26][27][28][29]}[25,26,27,28,29]. Advances in metabolomics and proteomics applications in relation to cow fertility were discussed by Aranciaga et al. [30].

2. Female Fertility: From Biology to Economics

Fertility is a general term encompassing a variety of traits that contribute to overall reproductive success [31]. Reproductive or fertility-related traits have been recorded in several ways, and authors have proposed different measurements. Berry et al. [17] classified female reproductive traits into three categories: interval (e.g., calving interval), binary (e.g., pregnant or non-pregnant), and count (e.g., number of services) traits. Additional reproductive measurements and definitions have been reviewed by Berry et al. [17], Cammack et al. [19], and Kgari et al. [32]. Although the relative importance of such measurements will differ between production systems and breeding objectives, fertility is critical for the profitability of cattle operations ^[17][33][17,33]. Herein, thwe researchers will broadly consider fertility to be defined as “the ability to conceive, maintain a pregnancy, and deliver a normal, living calf” ^[20][33][20,33]. Historically, selection has focused on the increased performance and growth of beef animals [15]. Especially in dairy cattle, producers’ priorities were related to milk production due to its direct effects on profitability ^[32][34][32,34]. Nonetheless, over the years, the cattle industry has reported declining reproductive success due to the inverse relationship between production and fertility traits ^[17][18][34][17,18,34]. Thus, due to these unfavorable correlations, several breeding programs are now including fertility traits in their genetic evaluations [31].

2.1. Heritability and The Traditional Selection of Fertility Traits

Fertility is a complex, quantitative trait determined by a large number of variants with small effects that are spread throughout the genome ^[31][35][31,35]. In general, indicator traits of fertility are often low in terms of heritability, with values ranging from 0.02 to 0.1 ^[17][19][17,19]. However, the heritability of interval traits tends to be greater than the values from binary or count traits [17]. Heritability provides a measurement of genetic variation and is an important parameter in the prediction of genetic gain, as it expresses the reliability of the phenotypic value as an indicator of the breeding value [36]. Heritability can be defined as the proportion of phenotypic variation due to genetic differences among individuals. Many contributing factors are related to the low heritability of fertility traits, including the environment and the number of records available [18]. Management and nutritional programs affect various aspects of fertility, such as age at puberty, age at breeding, reproductive development, and longevity [11]. Therefore, an increased, accurate number of records and a uniform environment are important to improve the accuracy of heritability estimates and accelerate genetic gain [37]. Additionally, as will be discussed later, genomic selection has improved the accuracy of selecting low-heritability traits [17]. Only recently, fertility traits were included in breeding programs. The selection of fertility traits in beef cattle has its own challenges due to the diversity of breeds, production systems, and breeding goals ^[37][38][37,38]. Additional challenges are related to the records from extensively managed herds and the availability of suitable data for genetic evaluation [37]. The female reproduction traits that have been used for genetic evaluation include heifer pregnancy rate, days to calving, age at first calving, calving interval, and stayability [37]. The Beef Improvement Federation (BIF) has provided guidelines for Uniform Beef Improvement Programs and the traits to be recorded for genetic prediction [39]. Likewise, the National Cattle Evaluation Consortium (NCEC) has measured economically important traits across the most prominent beef cattle breeds in the U.S., although the evaluation of reproductive traits is still limited [40]. Although limited by low accuracy, they allow producers to select sires that can improve reproductive performance [41]. Another opportunity for advances in beef cattle fertility is in the selection of replacement heifers. These animals are the genetic future of the cowherd and represent a large portion of the capital outlay invested in cow–calf production [11]. However, most producers still use phenotype-based strategies to select replacements for the breeding herd. As fertility-related traits have low heritability, phenotypes are poor predictors of an animal’s genetic value, leading to slow genetic progress. Most commonly, body weight, reproductive tract score, pelvic dimensions, body condition score, and structural correctness are the traits considered during the selection process [11]. Although age at breeding, growth, and reproductive maturity are key traits influenced by the timing of puberty, and thereby, fertility, they are not the most accurate predictors of reproductive success ^[8][42][8,42]. Hence, as will be discussed later in this resviearchw, researchers have proposed the development of novel traits that could better predict fertility potential ^[33][37][43][33,37,43].

2.2. Economic Impact of Fertility in Beef Cattle Production

Fertility and reproductive success are directly linked to the economics of cow–calf production operations ^[19][44][45][19,44,45]. Developing a calf into a heifer that calves out, on average, within two years of age requires a significant amount of a producer’s time and money. However, reproductive failure is the primary reason cows are sold in the U.S. According to the USDA, 43.9% of cows sold in 2017 for purposes other than breeding were due to reproductive failure, i.e., reproductive problems and pregnancy status (open or aborted) [46]. Additionally, 16.4% of all beef cows aged less than five years old were culled. Although this is a lower percentage, it represents a large amount of monetary loss for beef cattle producers, as a cow is expected to produce, on average, five to six consecutive calves in order for the producer to recoup development costs and begin to turn a profit ^[8][44][47][8,44,47]. Considering the percentages mentioned above, the selection of replacement heifers is critical to the sustainability and profitability of a cow–calf operation. Heifers are not only the genetic future of the herd but also, if the opportunity cost is factored in, they are the greatest investment beef producers can make ^[11][42][11,42]. Dickinson et al. [48] showed that traditional traits used for heifer selection, such as age at breeding, body condition score, and reproductive tract score, alone were not predictive of reproductive outcomes [48]. On the other hand, Hindman et al. [42] reported that age at AI and pelvic width were associated with heifer pregnancy through AI. These conflicting results reinforce the need for further research to develop accurate selection strategies that allow for differentiation between fertile and infertile heifers at an early stage.

3. New Opportunities for Old Challenges: Novel Traits and Technologies

Efforts have been made to identify the novel traits that need to be recorded for genetic evaluation as well as the identification and use of genetic markers through omics approaches. Novel traits such as age at calving, days to calving, first calving interval, and heifer pregnancy have been proposed for inclusion in breeding programs as they have, on average, higher heritability than previously used fertility-related traits ^[37][49][37,49]. The estimated heritability for the first calving interval was 0.23 in Charolais [50]. Likewise, the estimates of age at first calving, days to calving, and pregnancy from heifers in the Angus Australia database were 0.25, 0.26, and 0.32, respectively [49]. It is important to highlight that advances in omics technologies and analytical methods have provided opportunities to accelerate the genetic progress of low-heritability traits. These technologies can provide a snapshot of a vast amount of molecules in a tissue or cell [51]. The primary goal of such approaches is to provide a deeper understanding of animal biology and the genetic architecture and function that underlie a trait so that we can connect the animal genome to its phenome [52]. DNA-based technologies and the emergence of genomics have led to significant advances in identifying genetic variants affecting fertility [35]. Functional genomics approaches such as transcriptomics and proteomics have shed light on the regulatory mechanisms modulating reproductive processes. Likewise, analytical approaches to integrate different sources of omics data and model the interactions between molecular regulatory layers have provided insights into the interplay among fertility trait variation and the various levels of genome regulation.