Review of the direct thermochemical conversion of lignocellulosic biomass for liquid fuels

Abstract

Increased demand for liquid transportation fuels, environmental concerns and depletion of petroleum resources requires the development of efficient conversion technologies for production of second-generation biofuels from non-food resources. Thermochemical approaches hold great potential for conversion of lignocellulosic biomass into liquid fuels. Direct thermochemical processes convert biomass into liquid fuels in one step using heat and catalysts and have many advantages over indirect and biological processes, such as greater feedstock flexibility, integrated conversion of whole biomass, and lower operation costs. Several direct thermochemical processes are employed in the production of liquid biofuels depending on the nature of the feedstock properties: such as fast pyrolysis/liquefaction of lignocellulosic biomass for bio-oil, including upgrading methods, such as catalytic cracking and hydrogenation. Owing to the substantial amount of liquid fuels consumed by vehicular transport, converting biomass into drop-in liquid fuels may reduce the dependence of the fuel market on petroleum-based fuel products. In this review, we also summarize recent progress in technologies for large-scale equipment for direct thermochemical conversion. We focus on the technical aspects critical to commercialization of the technologies for production of liquid fuels from biomass, including feedstock type, cracking catalysts, catalytic cracking mechanisms, catalytic reactors, and biofuel properties. We also discuss future prospects for direct thermochemical conversion in biorefineries for the production of high grade biofuels.

Graphical abstract

Keywords

lignocellulosic biomass / thermochemical / liquid fuels / upgrading / biofuels

Introduction

Sheep (Ovis aries) were one of the first animal species domesticated in the Fertile Crescent region c. 10000 years ago. Following its domestication, a great variety of sheep breeds have been developed during the spread of sheep to other regions^[1^–3^]. As an important part of the global animal industry, sheep provide meat, milk, wools and dietary fat sources for human society^[4^]. In particular, income for farmers in many countries with Islamic traditions comes from the sheep husbandry. However, as the global population is expected to reach over nine billion people by 2050, highly efficient livestock production, including sheep, is needed to meet the expected demand^[5^].

Over the past few decades, established breeding practices making use of the estimated breeding values from phenotypic and pedigree information have significantly improved the production efficiency of the sheep (e.g., the increase of milk yield)^[6^,7^]. However, these approaches have been constrained, due to slow progress in genetic improvement and difficulty in separating desired from non-preferred traits^[6^–8^]. To overcome such limitations, researchers have mapped quantitative trait loci (QTL) based on sparse microsatellite markers and identified candidate genomic regions for the targeted traits in sheep. However, the QTL mapping resolution has been quite low or coarse and the typical confidence interval of a given QTL usually covers a large genomic region of several mega-bases or even a whole chromosome. Therefore, it is unlikely that QTL mapping can detect the causal genes and variants, and reveal genetic mechanisms for the target traits in domestic animals like sheep^[9^].

With the development and application of high-throughput screening technologies, genome-wide analyses of genetic variations has been shown to be an efficient strategy for identifying functional genes and genetic variants associated with economically important traits in sheep^[10^]. Whole-genome scale analyses are mainly used in genome-wide association studies (GWAS), selective sweep tests, RNA-seq, DNA methylation and copy number variations (CNVs) analysis. GWAS and selective sweep tests use mathematical approaches to pinpoint candidate causal genes and variants for relevant traits. RNA-seq has been explored to detect regulatory elements that are essential in expression of mRNA and microRNAs (i.e., miRNAs) related to target traits. DNA methylation analysis has been conducted to quantify the methylation of genes associated with particular traits. To date, these approaches have succeeded in revealing genes, genetic variants, signatures of selective sweeps and pathways associated with traits in sheep.

This review summarizes recent investigations that have revealed genetic mechanisms for economically important traits in sheep (i.e., meat, growth, milk, wool, reproduction, horns and tails) using high-throughput screening technologies (Table 1). Genetic variants, functional genes and pathways as well as the relevant technologies reviewed here will be useful for future genomics studies as well as omics-assisted breeding practices in sheep and other livestock.

Tab.1 Summary of high-throughput screening studies on economically important traits in sheep

Trait	Approach	Genes, miRNAs and signaling pathways	References
Meat and carcass	GWAS	Lean meat yield: GDF8 Marbling: ALDOA, STK32B and FAM190A Meat myoglobin content and fatty acid composition: FASN, MLXIPL, EVOLV6, ACACA, SYNRG, APOL6, MB, ACSL1, ISYNA1, SGK2 and AGPAT9 Meat production and quality: GHR Muscling and shear force: MEG3, MEG8 and DLK1	[ 11–16]
	RNA-seq	Fat metabolism: NELL1, FMO3, AACS, ACACA, ACACB, ACOT6, ELOVL6, FASN, HSD17B12 and SCD Muscle growth and development: HSPA6, ZIM3, SLC5A12, FGF1, IGF2BP2, IGF-2, TCF4, MRFs, GXP1 and STAC3; and miRNAs miR-10, miR-let-7 family, oar-miR-3955-5p, oar-miR-410-3p, bta-miR-183 and bta-miR-182	[ 17–22]
	DNA methylation	Muscle development: 399 different methylated regions and LTBP1	[ 23,24]
Growth	GWAS and selective sweeps	Body size: NPR2, HMGA2, BMP2, GPX3, FSTL1, PVR, EXT2, ALT4, SOX6, HAND2, PDGFD, BMPR2, GHR, ANKS1B; and Signal pathways HIF-1, VSMC and GH/IGF-1 Post-weaning gain: GRM1, POL, MBD5, UBR2, RPL7, SMC2, DAAM1 and APIP Bodyweight: GSTRBP, TRAMIL1, PHF15, PRSS12, MAN1A1, SYNE1, WAPAL, DAAM1, OST/SPP1, MEPE, IBSP, NCAPG and LCORL Bone-related traits: SPP1, MEPE, IBSP, LCORL and NCAPG	[ 11,16,25–29]
Wool	GWAS and selective sweeps	Coat pigmentation: KIT, ASIP, KITLG, EDN3, MITF, MC1R, HERC2-like, TYRP1, ASIP and MITF Growing wing hairs: FRY Wool fiber diameter: TSPEAR, PIK3R4, KRTCAP3, YWHAZ and CCNY Fiber diameter coefficient of variation and fineness dispersion: gKIF16B Crimp: PTPN3, TCF9, GPRC5A, DDX47, EPHA5, TPTE2 and NBEA	[ 11,27,30–36]
	RNA-seq	Hair/fleece development and function: KRTs, KRTAPs families, CST3, CSTB, S100A11, PPARD, CSTA, SIVA1, CCDC85B, FOXE1, VAX2, BAK1, ADRA1B, PKIA and HNF4A Coat color regulation: DCT, MATP, TYR and TYRP1 Fiber diameter: GNPAT, LPIN2, CHKA, PLD2, PLA2G3, SMPD2, UGCG, PIP4K2A, ACOT8, SLC25A17, NCOR1, FABP6, HSD11B1, STAR, FABP4 and SLC25A20 Follicle bulb regression and regeneration: Wnt, Jak-STAT, MAPK, Notch, TGF-β, Toll-like receptor and VEGF pathways Telogen hair follicles development and growth: 1910 known miRNAs and 2261 novel mature miRNAs	[ 37–41]
Milk	GWAS and selective sweeps	Milk protein and fat contents: LALBA Milk production performances: PALMD and RFP145 Milk production traits: ABCG2, SPP1, SCD and SOCS2, PKD2, MEPE and IBSP	[ 34,42–44]
	RNA-seq	Higher cheese yield: CSN2, CSN3 and LALBA Lipid metabolism: BTN1A1, XDH, FASN, ADFP, SCD, H-FABP and ACSS2 High protein production: FABP4, SUCNR1, HSP70 and HSPB8 Milk secretion: 2764 genes	[ 45–48]
Reproduction	GWAS and selective sweeps	Mammalian reproduction: PRLP Metabolic regulation and reproduction: TSHR Increased ovulation rate/litter size: GDF9 and BMP15 Oocyte development: CCNB2 and SLC8A3 Pre-weaning gain: POL, RPL7, MSL1 and SHISA9	[ 11,13,49–52]
	RNA-seq	Fecundity: BAX, BAD, NDUFA13, CAV1, LOC101117112, PAX8, IFI6, PRLR, PGR, ESR2, CYP19A1, CYP11A1, HSD17B12, INHBA, BMPR1B and BMPR2 receptors; and miRNAs oar-miR-665-5p, oar-miR-411a-5p, oar-miR-1197-3p and oar-miR-134-3p Fecundity and prolificacy: signaling pathways Wnt, MAPK, G-protein, TGF-b and PPAR	[ 53–57]
	DNA methylation	Reproductive traits: ADIPOQ, ABCG1, BRWD1, GRIN2B, METTL6, SIAH2, SLCO2A1, TNIK and UMODL1	[ 58]
Horn	GWAS and selective sweeps	Horn development: RXFP2, MTX2, HOX clusters, EVX2 and KIAA1715	[ 11,27,32–34,59–68]
Tail	Selective sweeps	Fat deposition or tail morphology: PPP2CA, SKP1, TCF7, RXFP2, PPP1CC, PDGFD, BMP2, VNRT, PPARA, RXRA, KLF11, HOXA11, BMP2, PPP1CC, SP3, SP9, WDR92, PROKR1 and ETAA1	[ 34,69–72]
	RNA-seq	Lipid metabolism: NELL1, FMO3, ACACA, ELOVL6, HSD17B12, CYP11A1, GDE1, ACACB, ACOT6, FASN, SCD and ACOT2; and miRNAs bta-miR-18a, hsa-miR-29b-1-5p, ssc-miR-22-3p, and hsa-miR-4749-5p	[ 17,18,20,73 ]

Genes and genetic variants associated with economically important traits in sheep

Meat and carcass traits

Meat and carcass traits, including meat quality (e.g., muscle components, taste, tenderness and intramuscular fat content) and carcass weight are economically important in sheep. In recent years, several studies, particularly those using high-throughput screening technologies, have revealed a number of candidate genes and genetic variants associated with meat and carcass traits.

Although there have been only a small number of GWAS in sheep, many candidate genes related to meat and carcass traits have been identified, including lean meat yield (e.g.,GDF8), marbling (e.g., ALDOA, FAM190A and STK32B), meat production and quality (e.g., GHR), fatty acid formation (e.g., ACACA, EVOLV6, FASN, MLXIPL and SYNRG), (glycero) lipid biosynthesis (e.g., ACSL1, AGPAT9, ISYNA1 and SGK2) etc.^[11^–14^]. Bolormaa et al., for example, performed a multi-trait GWAS using the Illumina Ovine SNP50 BeadChip in 10613 sheep, and found several candidate genes (e.g.,APOL6, DLK1, MEG3, MEG8 and MB) on chromosomes 3, 6, 14, 18 and 21 for muscling, shear force, meat myoglobin content and fatty acid composition^[15^]. Also, Matika et al. identified multiple QTLs on chromosomes 1, 3, 6 and 24 using GWAS and the Illumina Ovine SNP50 BeadChip^[16^]. The QTLs contain several candidate genes (e.g., SPP1, MEPE, IBSP, LCORL and NCAPG) for bone-related traits and meat quality traits in sheep^[16^].

Based on RNA-seq data at the transcriptome level, a large number of genes and miRNAs were revealed for meat and carcass traits, such as fat metabolism (e.g.,NELL1, FMO3, AACS, ACACA, ACACB, ACOT6, ELOVL6, FASN, HSD17B12 and SCD), muscle growth and development (e.g., HSPA6, ZIM3, SLC5A12, oar-miR-3955-5p, oar-miR-410-3p, bta-miR-183 and bta-miR-182) etc.^[17^–20^]. In an integrated analysis of miRNAs and mRNA expression profiles from a high-throughput screening study, 36 novel and six known miRNAs (e.g.,miR-10 and miR-let-7 families) and a few candidate genes (e.g., FGF1, IGF2BP2, IGF-2 and TCF4) relevant to cell development and cell differentiation were detected^[21^]. In addition, 463 differentially expressed genes including MRFs, GXP1 and STAC3 related to muscle growth and development were identified by an RNA-seq study^[22^].

With the advent of next generation sequencing technology, studies using whole genome DNA methylation from various developmental stages (e.g., embryogenesis, genomic imprinting, X-inactivation and tumorigenesis) of livestock, have been performed^[74^]. However, currently there are only two such studies for meat and carcass traits in sheep. In longissimus dorsi muscle of sheep, Couldrey et al. found a number of annotated genes with different levels of DNA methylation^[23^]. Cao et al. identified 399 different methylated regions and found two DNA methylation CpG sites in the LTBP1 gene, whose RNA expression was significantly involved in skeletal muscle development^[24^].

Growth traits

Growth traits of sheep include weight at various ages, average daily gain and bone traits (e.g., weight and area). These traits have been under long-term directional selection with moderate genetic control^[75^]. Over past decades, a number of QTLs have been detected for growth traits in sheep. Recently, GWAS and selective sweep tests have been widely used to detect candidate genes for growth traits in sheep, which could increase the efficiency of artificial breeding and selection. These genes includeNPR2, HMGA2 and BMP2 associated with skeletal morphology and body size, and those (e.g., GRM1, POL, MBD5, UBR2, RPL7 and SMC2) affecting post-weaning gain and accounting for body growth (e.g., GHR and ANKS1B)^[11^,25^–27^]. Meanwhile, a number of genetic variants and signaling pathways have also been identified in these studies. Gholizadeh et al. performed a GWAS for growth traits in Baluchi sheep using the Illumina Ovine SNP50 BeadChip and detected candidate genes associated with weight at various ages^[28^]. For example, STRBP and TRAMIL1 affected bodyweight at the 53 and 83 week stages; DAAM1 and APIP had a central role in controlling weaning weight; PHF15, PRSS12 and MAN1A1 genes were involved in the six-month-weight; SYNE1, WAPAL and DAAM1 genes were associated with the yearling weight^[28^]. Also, Matika et al. conducted a GWAS using the Illumina Ovine SNP50 BeadChip, and identified a QTL on chromosome 6, surrounded by a number of genes (e.g.,OST/SPP1, MEPE, IBSP, NCAPG and LCORL) affecting bone traits^[16^]. By comparative genomic analyses of 77 native sheep using whole-genome sequences, Yang et al. detected several genes (e.g.,GPX3, FSTL1, PVR, EXT2, ALT4, SOX6, HAND2, PDGFD and BMPR2) and signaling pathways (HIF-1, VSMC and GH/IGF-1) associated with body size in various environments^[29^].

Wool traits

As one of the most important animals used to provide fibers to the textile industry, sheep breeding for wool traits has focused mainly on fiber diameter, fiber diameter coefficient of variation, fineness dispersion, staple length and crimp, shedding, color and hair follicles. Compared to other farm animals, the genetic basis underlying wool traits in sheep has been given greater attention for many years^[37^]. Understanding the genetic mechanisms of wool traits would be helpful for sheep breeding as well as for elucidating the genetic basis of hair development in humans. In recent years, many candidate genes and genetic variants affecting wool traits have been reported.

GWAS provides a powerful approach for identifying candidate genes for wool traits. However, to date, only a small number of GWAS have been conducted for wool traits in sheep. A number of CNVs associated with wool fiber were identified using the Ovine Infinium HD SNP BeadChip^[76^]. Based on GWAS in sheep, it is concluded that KIT, ASIP, KITLG, EDN3, MITF, MC1R and HERC2-like genes are related to coat pigmentation, and the FRY gene is involved in the growth of wing hairs^[11^,27^,30^–34^]. By using GWAS coupled with genome-wide scan for selective signatures, Li et al. identified three pigmentation candidate genes (i.e., TYRP1, ASIP and MITF) and ASIP gene, which might explain the color difference between pigmented and unpigmented wool in Finnsheep^[35^]. Wang et al. also performed a GWAS in Chinese Merino sheep populations using the Illumina Ovine SNP50 BeadChip, and found thatTSPEAR, PIK3R4, KRTCAP3, YWHAZ and CCNY genes are associated with wool fiber diameter^[36^]. In addition, the KIF16B gene could be associated with fiber diameter coefficient of variation and fineness dispersion, andPTPN3, TCF9, GPRC5A, DDX47, EPHA5, TPTE2 and NBEA genes were related to crimp^[36^].

RNA-seq analyses have also been performed to characterize candidate genes and genetic variants that affect wool traits in sheep. Moreover, miRNAs and signaling pathways have also been identified for these traits. For example, several miRNAs (e.g.,oar-miR-103-3p, oar-miR-148b-3p, oar-miR-320-3p, oar-miR-31-5p, oar-novel-1-5p, oar-novel-2-3p, miR-let-7, miR-199 and miR-200 families) were found to affect wool follicle development, and a large number of genes (e.g., KRTs, KRTAPs families, CST3, CSTB, S100A11, PPARD, CSTA, SIVA1, CCDC85B, FOXE1, VAX2, BAK1, ADRA1B, PKIA and HNF4A) were shown to be related to hair/fleece development. In addition, earlier investigations indicated that several genes (e.g.,DCT, MATP, TYR and TYRP1) played a central roles in coat color regulation and a few signaling pathways (Wnt, Jak-STAT, MAPK, Notch, TGF-β, Toll-like receptor and VEGF) were involved in the regulation of wool follicle bulb regression and regeneration^[38^–41^]. Using Solexa sequencing (Illumina Genome Analyzer), Li et al. identified 1910 known and 2261 novel mature miRNAs, and these miRNAs could have essential roles in telogen hair follicle development and growth, which are important for improving wool quality^[77^]. Furthermore, Fu et al. sequenced the skin transcriptome and identified genes associated with wool diameter^[37^]. Some early reported genes (e.g., GNPAT, LPIN2, CHKA, PLD2, PLA2G3, SMPD2, UGCG, PIP4K2A, ACOT8, SLC25A17, NCOR1, FABP6, HSD11B1, STAR, FABP4 and SLC25A20) in lipids and lipoproteins metabolic pathways are significantly related to the variation of fiber diameter^[37^].

Milk traits

Sheep milk is an important source of revenue, accounting for a large portion of global milk production and is commonly used to produce many cultured dairy products, such as cheese. Milk traits can be determined by measuring yield, protein, fat and lactose percentage. Since sheep milk is mainly produced by multi-purpose local breeds with low-to-medium milk yields, globally few sheep breeds have been developed specifically for milk production^[78^]. Recently, functional genes affecting milk production have been identified using high-throughput screening technologies.

To data only a few investigations involving GWAS and selective sweep tests have been conducted to identify genomic regions influencing milk traits in sheep. García-Gámez et al. reported the first GWAS for milk traits using a high-throughput SNP array and identified the most likely candidate gene (i.e.,LALBA) affecting milk protein and fat contents in dairy sheep^[79^]. Moioli et al. presented the first genome-wide characterization of selective sweeps in dairy sheep and detected nine candidate regions harboring a couple of genes (e.g.,PALMD and RFP145) related to the milk production performance^[42^]. Moreover, other strong candidate genes (e.g., ABCG2, SPP1, SCD, SOCS2, PKD2, MEPE and IBSP) associated with milk production traits have been proposed using selective sweep and GWAS^[34^,43^,44^].

Recently, RNA-seq has also been applied in a genomic study on milk traits in sheep. A set of candidate genes related to milk traits were identified, includingCSN2, CSN3 and LALBA genes associated with higher cheese yield; BTN1A1, XDH, FASN, ADFP, SCD, H-FABP, ACSS2 genes involved in lipid metabolism; and FABP4, SUCNR1, HSP70, HSPB8 genes with a potential crucial role in high protein production^[45^–47^]. By comparing gene expression in milk somatic cells by RNA-seq in the same sheep before and after linseed feeding, Giordani et al. found 2764 genes potentially regulating milk secretion^[48^].

Reproductive traits

Reproductive traits, especially those related to number of births, litter size and pre-weaning viability, are critical for sheep production^[80^]. Reproductive traits typically have low heritability, thus genetic improvement of these trait has been limited to the long-lasting established breeding approaches^[81^,82^] and marker-assisted selection provides an excellent opportunity for improving these traits^[83^].

To date, a set of genes and QTLs affecting these traits in sheep were reported by GWAS. For example, the prolactin receptor gene was reported as a key regulator of mammalian reproduction, thyroid stimulating hormone receptor as having a pivotal role in metabolic regulation and control of reproduction, and growth differentiation factor 9 (GDF9) as a strong candidate gene for increased ovulation rate/litter size^[11^,49^–51^]. Moreover, an X-chromosomal locus close to the BMP15 gene was identified in highly prolific and normal ewes by GWAS, and two novel mutations in theBMP15 gene were found to be associated with increased litter size and ovulation rate, resulting in an atypically high prolificacy^[52^]. It is also found that two genes (CCNB2 and SLC8A3) are associated with oocyte development and four genes (POL, RPL7, MSL1 and SHISA9) could affect pre-weaning gain via GWAS^[13^].

It is well known that miRNAs have important gene-regulatory roles in reproductive traits. Using genome-wide sequencing of mRNAs and miRNAs, many candidate genes, miRNAs and signaling pathways regulating sheep fecundity have been obtained. In a genome-wide transcriptome analysis of different breeds with high- and low-fecundity sheep, multiple candidate genes, such asBAX, BAD, NDUFA13, CAV1, LOC101117112, PAX8 and IFI6; and miRNAs, such as oar-miR-665-5p, oar-miR-411a-5p, oar-miR-1197-3p and oar-miR-134-3p, were shown to be critical for sheep fecundity, and signaling pathways Wnt, MAPK and G-protein were shown to be potentially critical for regulating sheep fecundity and prolificacy^[53^–55^]. Furthermore, an RNA-seq study with multiple sheep breeds revealed that PRLR, PGR, ESR2, CYP19A1, CYP11A1, HSD17B12, INHBA, and BMP receptors (BMPR1B and BMPR2) genes could be involved in follicular development or control of ovulation, and TGF-band PPAR pathways might regulate female reproduction^[56^,57^].

Methylated-DNA immunoprecipitation sequencing has also been employed to identify genes for phenotypic traits in domestic animals. To data, there has only been one study reported using this method^[58^]. In that study, 19 single nucleotide variations and nine candidate genes (ADIPOQ, ABCG1, BRWD1, GRIN2B, METTL6, SIAH2, SLCO2A1, TNIK and UMODL1) were identified as being related to sheep reproductive traits.

Horn traits

As a cranial appendage in sheep, horns are important in accessing resources and mates, although they might not be desired in livestock farming and breeding. For example, horned males are inclined to attack other animals or people^[84^,85^]. Thus, hornless (or polled) individuals are bred for animal welfare and economic reasons. It has been shown that horn traits of sheep, such as size, number and shape, are controlled by several autosomal loci^[59^].

From 2011 to 2015, several genes (e.g., RXFP2, MTX2 and HOX gene clusters) and QTLs affecting horn development on chromosome 10 were identified in multiple sheep breeds^[11^,27^,32^–34^,59^–65^]. On chromosome 2, a genomic region of 132.0–133.1 Mb that contains the HOXD gene cluster and EVX2 and KIAA1715 genes was identified using GWAS^[66^]. These genes are related to the formation of limbs and genital buds^[66^]. A similar genomic region (131.9–132.6 Mb) on chromosome 2 is associated with the four-horn phenotype, and MTX2 and HOXD cluster within this region were revealed to be involved in horn ontogenesis^[67^]. In Damara sheep, HOXD was located also on chromosome 2 by a GWAS study on horn number, and HOXD was shown to be critical in embryonic development of appendages^[68^].

Tail traits

Domestic sheep can be classified into fat- and thin-tailed breeds. Nowadays a variety of feed sources can be provided in hostile environments, thus fat tails are less important as an energy reserve^[86^]. Moreover, fat-tailed sheep require more feed, which increases the cost of production. Therefore, thin-tailed sheep are preferred for sheep farming. Revealing the molecular mechanisms of fat deposition in sheep tails could provide valuable information for sheep breeding practices.

However, only a handful of studies on sheep tail traits have been conducted by selective sweep test. Moradi et al. presented the first genome-wide characterization of selective sweeps for thin- and fat-tailed sheep, and revealed QTLs containing candidate genes (e.g.,PPP2CA, SKP1 and TCF7) on chromosomes 5, 7 and X^[69^]. Through population structure and selection tests using whole-genome SNPs, Wei et al. detected three strong selective genomic windows containing two functional genes,PPP1CC and PDGFD, which could be associated with the phenotypic differences of tail types, i.e., thin versus fat tails^[34^]. Moioli et al. performed a genome-wide scan using approximately 50 000 SNP, and detected BMP2 and VNRT genes related to fat deposition in sheep tails^[70^]. Through a genome-wide high-density SNP study of sheep with three tail types, Zhu et al. detected several regions with CNVs harboring functional genes associated with fat deposition, such asPPARA, RXRA and KLF11^[71^]. Furthermore, Yuan et al. applied selection tests based on 50K SNP genotype data, and showed thatHOXA11, BMP2, PPP1CC, SP3, SP9, WDR92, PROKR1 and ETAA1 genes may be involved in the formation of fat tails in sheep^[72^].

RNA-seq studies have also been carried out to reveal the genetic basis of fat deposition in sheep tails. By comparing transcriptome data of fat-tailed (Kazakh sheep) and short-tailed sheep (Tibetan sheep), Wang et al. demonstrated thatNELL1 and FMO3 genes are involved in fat metabolism and deposition of adipose tissues^[17^]. Multiple genes (e.g., ACACA, ELOVL6, HSD17B12, CYP11A1, GDE1, ACACB, ACOT6, FASN, SCD and ACOT2) and miRNAs (e.g., bta-miR-18a, hsa-miR-29b-1-5p, ssc-miR-22-3p and hsa-miR-4749-5p) were also shown to be essential in regulation of lipid metabolism in sheep with different tail morphologies^[18^,20^]. By comparing transcriptomes of liver tissues between Mongolian and Lanzhou fat-tailed sheep with different tail types, Cheng et al. detected seven differentially expressed genes (four upregulated and three downregulated) using RNA-seq^[73^]

Box 1: Workflow of identification, validation and application of candidate function genes or variantsƒ

For example, bone morphogenetic protein receptor 1B (BMPR1B) encodes a member of the bone morphogenetic protein (BMP) receptor family of transmembrane serine/threonine kinases that belongs to the transforming growth factor b (TGF-b) receptor family. Through QTL fine mapping and deep-sequencing approaches, BMPR1B has been demonstrated to be a strong candidate gene for the prolificacy traits in sheep^[57^,87^,88^]. Also, BMPR1B knockout mice showed irregular estrous cycles and an impaired pseudo-pregnancy response; meanwhile, the level of mRNA for cyclooxygenase 2, an enzyme required for cumulus expansion, was increased^[89^]. In addition, a non-conservative substitution in the coding sequence of theBMPR1B was found by expression level difference between genotypes to be closely associated with the hyper-prolific phenotype of ewes^[90^]. Therefore, BMPR1B has a promising application in marker-assisted in breeding programs for prolific sheep (Fig. 1).

Fig.1 Workflow of identification, validation and application of candidate function genes or variants

Full size|PPT slide

Conclusions and perspectives

Recently data on whole-genome genetic variations and genome sequences have contributed greatly to the understanding of genetic mechanisms underlying a variety of phenotypic traits in sheep. Genomic resources also provide a basis for genetic improvements of economically important traits in sheep. Identification of functional genes and variants associated with these traits helps facilitate traditional breeding techniques using quantitative approaches and molecular breeding techniques are cost-effective and time-saving compared to conventional crossbreeding schemes.

It should be mentioned that a large number of candidate genes and miRNAs associated with the target traits require further functional verification (Fig. 1). Currently, the use of large genomic and epi-genomic data sets from high-throughput sequencing is to some extent limited by current computational and statistical approaches. Therefore, novel tools that integrate multiple levels of omics data, and system biology analysis, will be required in the post-genomic era. We believe that integrated analyses of genomes, epi-genomes, transcriptomes and proteomics on a genome-wide scale will provide novel insights into genetic mechanisms underlying these important traits in sheep.

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Acknowledgements

The authors would like to thank the National Natural Science Foundation of China (31422013) and the Research Institute of New Technology, Special Fund for Fundamental Research (CAFYBB2014ZD003) for financial support during this investigation.

Compliance with ethics guidelines

Jianchun Jiang, Junming Xu and Zhanqian Song declare that they have no conflict of interest or financial conflicts to disclose.

This article is a review and does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg