To the best of our knowledge, this is the first genome-wide scan study of putative signatures of selection in local Italian chicken populations. Several factors could have led to the identification of signatures of selection in these populations, such as body weight (heavy vs light) and the geographical area of origin (Northern vs Southern Italy) (Table 1). Most local Italian chickens are Mediterranean-type breeds or populations that are known to produce eggs and meat for the rural family and/or niche products [32]. Some can be regarded as meat-type chicken breeds, including Valdarnese, Robusta Lionata, Robusta Maculata, Millefiori di Lonigo, and Ermellinata di Rovigo [33], although their productive performance is lower than that of commercial broiler lines. These local breeds have been mainly raised as backyard chickens and are, thus, more resistant to diseases and viruses than commercial chickens, for which selective breeding has reduced resistance to infectious diseases [34]. In fact, to adapt to these backyard conditions, selective sweeps might have occurred in genomic regions that are related to immune responses and disease sensitivity [35]. Effects of the geographical area of origin on genomic regions that have been subjected to selection have also been reported in local Italian goats [36] and sheep [37]. Geographical location, coupled with smallholder farm practices, likely imposed multiple environmental stressors on the studied chicken populations that may have affected their fitness and led to their adaptation to these environments over time through changes in allele frequency of beneficial or detrimental alleles.

A number of factors can affect the identification of signatures of selection, including genetic structure, population size, bottlenecks, and migration [38]. Assuming that populations with a similar structure have undergone similar evolutionary processes [39], we used populations with a high degree of within-population genetic homogeneity and shared ancestry components to detect signatures of selection [19], as was also confirmed by the MDS and Admixture results (see Additional file 1: Fig. S1 and Additional file 2: Fig. S2). We also categorized the populations into four groups for comparative analysis. In fact, including more populations in a group may identify a specific history of selection for each production purpose, instead of population-specific selection histories, which can facilitate the interpretation of the identified signatures of selection [39, 40].

To identify signatures of selection, we used different statistical methods based on the decay of haplotype homozygosity (iHS, Rsb, and XP-EHH) and based on regions of homozygosity (ROH). The combination of different approaches is an effective way to identify signatures of selection [38] and, together with the use of high-density SNP panels, can boost the detection accuracy and avoid unknown biases [8, 41, 42]. Moreover, we used LD-based pruning because it can account for the effects of ascertainment bias on the identification of signatures of selection, producing results that are most comparable to those obtained from whole-genome sequence data and therefore it is recommended for practical use [43, 44].

This study detected 15 genomic regions that were potentially under selection using the extended haplotype homozygosity (EHH)-derived statistics. Eight of these regions were detected within a single group (iHS) and seven were identified by combining the results of Rsb and XP-EHH, which revealed divergent selection between groups, thus providing good evidence that these signals are not artifacts. Twenty-one additional genomic regions were identified with the ROH approach.

There were no overlaps between the regions under selection that were identified with ROH and those detected with the three extended haplotype homozygosity (EHH)-derived statistics. This may be because ROH can detect signatures of selection related to any trait, while the heavy vs light or Northern vs Southern Italy comparisons are more likely to detect signals related to the investigated trait. Each of these statistics has its advantages and disadvantages and can capture a specific genomic region under selection [13,14,15]. This is not surprising as there are differences in the statistics underlying each approach for detecting the signatures of different types of selection across different timescales [14]. Moreover, the genomic regions detected by ROH can also result from other evolutionary processes, such as inbreeding, bottlenecks, and genetic drift e.g., [16, 18, 45, 46]. Therefore, considering ROH regions as signatures of selection should be viewed with caution.

Numerous genomic studies of local chicken populations worldwide have allowed the identification of signatures of selection in local breeds, using methods based either on an excess of haplotype homozygosity or deformation of the allele frequency spectrum e.g., [8, 9, 11, 35, 41]. One observation that has emerged from this study is that, in most cases, the signatures of selection detected in local chicken breeds do not overlap across studies and even between lines from the same geographical location within the same study e.g., [35]. This is mainly explained by the fact that, following their expansion through human migrations, founder populations of present-day local chicken breeds have experienced drastic bottlenecks [47]. In addition, being genetically isolated, these populations have independently evolved to adapt to diverse environmental conditions. Given that standing genetic variation is the major contributor to adaptation in chicken [48], it is not surprising that most of the signatures of selection are breed-specific because of differences in genetic background between chicken breeds.

The putative genomic regions under selection identified in our study (Tables 2, 3 and 4) spanned many candidate genes with diverse molecular and cellular functions. Therefore, in our comparison with the literature, we considered mainly the genes in the identified regions that are related to traits involved in livestock breeding. Moreover, the number of identified regions potentially under selection was larger for regions related to differences in live weight than for those related to differences in geographical area of origin (Table 3).

The iHS analysis was performed to detect recent and incomplete selective sweeps [13] within the five groups. This approach exploits information on allele frequencies of both selected and neighboring SNPs, which increases its power to detect signatures of selection [15]. This analysis is also more suited to genotyping data that are generated from SNP chips than to whole-genome sequence data, thus reducing the problems of ascertainment bias [49].

In the Northern breeds, the genes within the signature of selection on GGA1 were recently reported as putative positively selected genes related to cold adaptability in chickens [50]. In particular, the PRCP and FAM181B genes may participate in the adaptation to cold conditions by regulating angiogenesis and nervous system development [51, 52]. These genes could have a role in the adaptation of the Northern breeds to the cold conditions of their habitat region. Also, we identified the FZD4 as a candidate gene, which is associated with the pattern of phenotypic variation of plumage color (white, mixed and brown) in chicken. Plumage color is an important qualitative trait that can serve as marker for breed identification and can be considered indirectly as an economically important trait that is under the influence of multiple genes, gene–gene interactions, and environmental factors [53]. Several local Northern breeds show a white (Bianca di Saluzzo, Polverara Bianca, Ermellinata di Rovigo) or brown plumage color (Bionda Piemontese, Robusta Lionata, Padovana Camosciata). The detected genomic regions on GGA1 for the Southern populations included candidate genes involved in thermo-tolerance and local adaptation, as for example ST7, which may be involved in the differences in thermo-tolerance and heat stress response mechanisms in indigenous chickens [54].

The role of the WDR37 gene on GGA2 for the heavy group is also interesting as it encodes a member of the WD-repeat protein family that is involved in growth-related processes, including cell cycle progression and gene regulation. A previous study [55] reported that WDR37 was differentially expressed between broilers selected for fast and slow growth. This gene has also recently been reported as a candidate for body weight in Korean native chickens [56].

Finally, in the local group that includes all populations, signatures of selection were observed in genomic regions that included genes related to meat fatty acid composition in Korean native chicken (ATP11C) [57], and to immune traits in chicken (PCDH19) [58]. Within this region, the GPR155 gene is another candidate that is associated with high feed efficiency [59]. In a previous study [19], the identification of ROH islands within these local chickens considered as a meta-population, identified candidate genes involved in body weight and feed conversion ratio. However, there were no overlaps between the regions under selection identified here with the haplotype homozygosity approaches and those detected based on ROH analysis. The two studies agreed only on the chromosomes (GGA7 and GGA8) that hosted the selective sweeps.

The Rsb and XP-EHH tests were applied to detect potential selective sweeps that were fixed (or nearly fixed) in one group but still segregated in the other groups. Climate and farming systems vary between chicken populations from Northern and Southern Italy and between the heavy and light groups. These aspects have an impact on genome shaping in terms of regions under selection and result in differences among populations and groups [19].

The genomic region on GGA4 that was identified in the comparison between Northern and Southern Italian populations included nine candidate genes, such as NOX1, which plays an important role in the process of heat stress [60]. In fact, exposure of farm animals to high summer environmental temperatures, as for example in the south of Italy, negatively affects animal husbandry. Other candidate genes are involved in reproduction traits in livestock species, such as FGF13 in chicken [61] and MCF2 in cattle [62].

Among the candidate genes in the comparison between the heavy vs light breeds, several genes were identified on GGA2: SLC6A19, which is related to growth and metabolism in the Muscovy Duck [63]; EPB41L3, which has been reported as a promising gene for growth and meat production traits in sheep [64]; and ZBTB14, which is listed as a candidate gene for carcass and growth traits in chicken based on haplotype-based genome-wide association studies [10].

The largest overlap between genomic regions showing evidence of signatures of selection that was identified by the three approaches was located on GGA18 (iHS of heavy breeds, Table 2; Rsb and XP-EHH between heavy vs light breeds). Several genes belonging to the Noggin family were detected in this genomic region, such as the NOG gene, which has been suggested to be critical for normal bone and joint development [65]. Other interesting genes were also mapped to this region, such as DGKE, a candidate gene involved in abdominal fat deposition in chickens [66], SCPEP1, which has an important role in the regulation of the body and intramuscular fat content in pig [67], and RAB11FIP4, which is a candidate gene for body weight in American mink [68].

In chicken, several studies have reported that ROH regions can harbour candidate genes associated with production traits, immune response, and environmental adaptation [41, 46, 69, 70]. For the group of heavy chickens, several genes in three regions of GGA3 have been reported as candidates related to muscle growth and overlap with ROH islands detected in Italian autochthonous turkey breeds [71]: BEND6, which was identified as a candidate gene for intramuscular fat content in chicken [72]; COL21A1, which is regulated by growth factors and is involved in muscle growth [73]; and BMP5, which is a strong candidate gene for body size in livestock [74]. In the group of light chickens, we identified the AFF1 gene within a ROH island on GGA4, which is known to have a lower expression level in mallards (wild ancestors with a low weight) than in Pekin ducks (large body size), and thus is related with body weight [75]. Within the ROH islands detected for the Northern group, we identified genes that are known to influence different phenotypic traits in chicken, but that are not directly linked with local adaptation, such as SNX25, a key gene in the regulation of TGF-β signaling and therefore, contributes to the immune system [76], or ACSL1, a candidate gene for fat deposition in chickens [77]. Finally, for the group of Southern chickens, the detected ROH islands hosted several interesting genes, such as: PFKBB3, which together with other genes belongs to the heat shock protein gene family, as a heat responsive gene [78]; genes related with pigmentation, a complex trait that is influenced by the genetic background and other factors, including the environment and endocrine factors, e.g. the RAB32 gene, which has a crucial role in the pigmentation process, i.e. in the melanosome biogenesis, degradation, and transport, and that acts in a functionally redundant way by regulating skin melanocyte pigmentation and controlling the post-Golgi trafficking of tyrosinase (TYR) and tyrosinase-related protein 1 (TYRP1) [79]; and the IRF8 gene, which is a critical transcriptional regulator of the innate and adaptive immune system and has been shown to have a role in the hyperpigmentation and immune development in chicken [80].