Descriptive study of interprofessional collaboration between physicians and osteopaths for the pediatric population in Quebec, Canada

Chantal Morin
Johanne Desrosiers
Isabelle Gaboury
Descriptive study of interprofessional collaboration between physicians and osteopaths for the pediatric population in Quebec, Canada
10.1186/s12913-017-2717-yR E S E A R C H A R T I C L E Open Access
Background: Osteopathy is an increasingly popular healthcare approach that uses a wide variety of therapeutic manual techniques to address pain and somatic dysfunction. In Quebec, Canada, osteopathy is the complementary medicine most often recommended by family physicians. However, factors fostering the development of interprofessional collaboration (IPC) between physicians and osteopaths are unknown. This study aimed to describe the current situation in terms of IPC among practitioners working with pediatric patients. Methods: A self-administered questionnaire was sent to osteopaths, family physicians, and pediatricians involved with pediatric patients in the province of Quebec. The postal questionnaire captured general knowledge about osteopathy and its practice parameters and role, sources of information, communication aspects including having a professional relationship and referrals, and influence of the upcoming government regulation. Quantitative data from the questionnaires were analyzed using descriptive statistics. Logistic regression model for factors associated with osteopathic referrals and multiple linear regression analyses for the number of correct answers about general osteopathic practice parameters were performed. Results: A total of 274 physicians (155 family physicians (response rate 13%) and 119 pediatricians (17%)) and 297 osteopaths (42%) completed the survey. According to physicians, osteopathy was most appropriate for musculoskeletal pain (241; 91%) and plagiocephaly (235; 88%). Osteopathic referral was positively associated with having a professional relationship (odds ratio [OR] 4.10 (95% confidence interval [CI] 2.12; 7.95), p < 0.001), personal consultation (OR 2.58 (95% CI 1.35; 4.93), p = 0.004), community-based practice (OR 1.89 (95% CI 1.03; 3.47), p = 0.040), and belief in the active role of osteopathy for pediatric conditions (OR 1.22 (95% CI 1.01; 1.47), p = 0.042). The majority of physicians (72%) and osteopaths (62%) considered the upcoming government regulation of osteopathy a positive factor for collaboration. Conclusion: Some collaboration already exists among these practitioners, including mutual referrals, but optimizing this collaboration still poses some challenges.

Background

Osteopathy is a complementary and alternative medicine (CAM) that is growing in popularity in many countries including in Canada and especially in the province of Quebec [1,2] where it is the CAM most often recommended by family physicians [3]. Osteopaths use a wide variety of therapeutic manual techniques to address pain and somatic dysfunction in order to restore the person's natural state of physical well-being. Osteopathic manipulative treatments can facilitate the body's normal selfregulation and self-healing mechanisms by addressing areas of tissue strain, stress, restriction, or dysfunction that may impede normal function [4]. In Quebec, osteopathic consultations for the pediatric population are frequent [5].The most common reasons include cranial deformities, torticollis, postnatal preventive healthcare, otolaryngology concerns, gastroesophageal reflux, motor or cognitive development concerns, musculoskeletal, respiratory and digestive problems, headaches, and sleep disturbances [2]. Such functional disorders are known to pose a significant challenge to conventional medicine [6][7][8] and frequently require an interprofessional approach [9,10].

Interprofessional collaboration (IPC) in primary care may be defined as a set of relationships and interactions that allows professionals to share their knowledge, expertise and experience to concomitantly address complex client needs [11]. Many interactional (interpersonal relationships including willingness to collaborate, trust, communication and mutual respect), organizational and systemic factors (social, cultural, professional and education systems) are known to influence IPC [12]. In the context of IPC between conventional medicine and CAM, differences in work culture, paradigms, knowledge and language are factors preventing interactions and limiting collaborations [13]. Systemic factors such as liability concerns [14] and power disparities between physicians and CAM practitioners [13,15,16] also influence the collaboration process. There is no legislation yet controlling the training and the title, or regulating the practice of non-physician osteopaths in Quebec. In order to protect the public, the Office des professions du Québec (provincial regulatory body for all health care professions) is currently working towards a legislative framework to regulate the practice of osteopathy [17]. To date, no studies have been published about IPC between physicians and osteopaths for pediatric patients.

The absence of studies about the factors enabling the development of IPC between physicians and osteopaths for pediatric and other patients, coupled with the increase in osteopathy consultations by parents, points to the need to study this working arrangement, especially given the pending governmental legislation of the osteopathic profession in Quebec.

This study aimed to describe the current general situation in the province of Quebec, Canada, in terms of IPC between physicians and non-physician osteopaths working with pediatric clients. More specifically, three descriptive aspects were examined: 1) physicians' knowledge about osteopathic practice parameters and role; 2) communication including referrals and professional relationships; and 3) influence of the pending government regulation of osteopathy. In addition, factors associated with osteopathic referrals by physicians and factors linked to physicians' knowledge of key osteopathic practice parameters were explored from descriptive data.

Methods

Study design, participants and recruitment procedure

This survey, conducted between September and November 2014, was the first (quantitative) phase of a larger sequential mixed method study aimed at improving understanding of IPC between physicians and non-physician osteopaths working with pediatric patients. Postal questionnaire were sent to all family physicians with a pediatric population and pediatricians without a subspecialty in Quebec, according to Scott's MD Select 2013 directory as well as all members of Ostéopathie Québec (largest professional association in the province). A total of 2802 questionnaires were initially mailed to family physicians (N = 1327), pediatricians (N = 738) and osteopaths (N = 737). Assuming a 20% response rate for physicians/pediatricians and 40% for osteopaths, it is estimated that errors would be limited to 5% and 6% respectively, 19 times out of 20.

Efforts to maximize participation in the study included personalized mailing of questionnaires. The survey was also promoted on the Ostéopathie Québec website, and the first author attended various medical and osteopathic events in fall 2014. A reminder postcard was sent two weeks after the initial mailing and a second questionnaire two weeks later. Study aims were clearly described at the beginning of the questionnaire. Completing and returning the questionnaire anonymously indicate informed consent from participants. The study was approved by the Centre hospitalier universitaire de Sherbrooke ethics committee for health research on humans (#14-115).

Instrument (survey questionnaire)

The physician and osteopath versions of the survey questionnaire were developed using a three-stage iterative process comprising developmental stage, question testing stage, and pilot stage [18].

The initial version of the questionnaire was based on a literature review of existing questionnaires regarding IPC between conventional medicine and CAM practitioners. It was divided into three categories according to the study's conceptual model, the Chiropractor-physician model of collaborative practice of Mior et al., (2010): practice parameters, communication, and care delivery parameters including regulation [14]. We considered existing questionnaires on physicians' [19] or other health professionals' [20] knowledge of osteopathy, continuing education and sources of information about osteopathy [20] or CAM for the pediatric population [21], experiences of collaboration with CAM practitioners [21], referrals [19,20,22], communication and professional relationships [20], and sociodemographic variables related to collaboration [19][20][21][22]. This first version was pretested with two physicians, one pediatrician, three osteopaths, and an expert on IPC and questionnaire development. The modified version was piloted (procedure and duration) in individual face-to-face cognitive interviews with two physicians, two pediatricians and four osteopaths. It took 8 to 10 minutes to complete the survey. The final version of the questionnaires covered: a) knowledge about osteopathic practice parameters including 10 questions regarding general aspects, 10 questions concerning belief in the active role of osteopathy for specific pediatric conditions, and one question regarding sources of information about osteopathy (physician version only); b) communication aspects including interpersonal relationships, referrals, and communication methods (7 questions); c) influence of regulation of osteopathy on IPC (1 question); and d) sociodemographic data: gender, years of experience, type of practice, discipline, personal consultation of an osteopath (physician version), and presence of a physician in the working environment (osteopath version). Qualitative comments could be added by participants at the end of the questionnaire.

Data analysis

The number of correct answers out of 10 was computed for the questions on the general aspects of practice parameters. Similarly, the total number of positive answers for the 10 questions on belief in the role of osteopathy for specific pediatric conditions was calculated. Means and standard deviations were generated. Other quantitative data from the questionnaires regarding communication, influence of regulation, and sociodemographic information were analyzed using descriptive statistics (frequencies and percentages).

Chi-squared or t tests (depending on the type of dependent variable) were first used to identify statistically significant variables associated with osteopathic referrals by physicians (yes or no) and factors associated with more correct answers out of 10 about general osteopathic practice parameters. Independent variables that were significantly (p < 0.05) associated were introduced in a stepwise backward logistic regression model for factors associated with osteopathic referrals or in multiple linear regression analyses for the number of correct answers about general osteopathic practice parameters.

Normality of the knowledge variable was verified visually with a histogram and a residual analysis was conducted to verify basic assumptions for both regression models. All analyses were performed using SPSS 17 (Chicago, IL).

Results

After excluding undeliverable questionnaires, retired physicians and osteopaths and specialized family physicians (see Fig. 1), a total of 274 physicians (155 family physicians out of 1192 (response rate 13%) and 119 pediatricians out of 708 (response rate 17%)) and 297 osteopaths out of 714 (response rate 42%) completed the survey. All surveys were considered for analysis. The respondents' characteristics are summarized in Table 1. The majority were women, 122 physicians (45%) had consulted an osteopath for themselves, and 38 osteopaths (13%) had a physician working in the same clinic.

Physicians' knowledge about osteopathic practice parameters

Physicians correctly answered 7.2 (SD 1.8) questions out of 10 on average concerning general knowledge about the practice of osteopathy ( Table 2). Level of osteopathic education and regulation status of the profession were the questions with the lowest percentage of correct answers. However, almost all physicians agreed that being a physical therapist is not a prerequisite for being an osteopath in Quebec and that osteopaths evaluate and mobilize all body tissues, not just vertebrae. There were 6.8 (SD 1.91) positive answers for belief in the role of osteopathy for specific pediatric conditions (Table 2). A high percentage of physicians think that osteopathy has a role in addressing musculoskeletal pain as well as torticollis and plagiocephaly. When asked what their sources of information about osteopathy are (multiple answers were allowed), more than half of the physicians mentioned asking information directly to an osteopath (52%), followed by patients (51%), and to other health professionals (21%). Information was also obtained by personal searches (17%), from scientific articles (10%), and during continuing education sessions (8%). Table 3 outlines communication aspects for each type of practitioner. More than one third of respondents had a professional relationship with the other practitioner. Nearly half the physicians referred pediatric patients to osteopaths at least once a month. The large majority of respondents (81% of physicians and 85% of osteopaths) said that communication was required for common patients and that their preferred communication methods were letters or verbal communication through the patient.

Communication

Influence of regulation

The majority of physicians (72%) and osteopaths (62%) said they would be moderately or greatly influenced by the upcoming government regulation of osteopathy and the creation of university-based osteopathic programs in Quebec. Eighteen percent of physicians and 22% of osteopaths would not be very influenced and respectively 10% and 16% said they would not be influenced at all by regulation, either because they already collaborated or because they had no interest in physician-osteopath collaboration (comments on the survey).

Factors associated with osteopathic referrals by physicians

Variables statistically associated with osteopathic referrals by physicians at the bivariate level were: gender (female), profession (family physician), general knowledge about practice parameters, direct sources of information about osteopathy, belief in the active role of osteopathy for pediatric conditions, community practice, personal consultation of an osteopath, and having a professional relationship. These last four variables remained in the final stepwise backward logistic regression model (Table 4).

Factors associated with knowledge about osteopathic general practice parameters

In the final linear regression model F (7247) = 9.117 (p < 0.001), 20.5% of the variance in the number of correct answers (knowledge) concerning general osteopathic practice parameters was explained by having a direct source of information about osteopathy (osteopath or other health professional; p = 0.009) and the number of positive answers for belief in the appropriateness of osteopathy for pediatric conditions (p < 0.001) while considering all the other bivariate associated variables in the model (professional relationship, personal consultation with an osteopath, osteopathic referrals, importance of having an osteopath among collaborators (quite or very important versus not important), and gender).

Discussion

Implication of the findings

The main aim of this study was to describe the current situation in Quebec respecting interprofessional collaboration between physicians and non-physician osteopaths working with pediatric patients. We found that some collaboration already exists, including referrals and professional relationships between these practitioners, but other factors might improve this collaboration. Having a professional relationship or having personally used osteopathic services were the factors most strongly associated with osteopathic referrals. It is known that consulting a CAM practitioner or having used CAM personally influences CAM referrals for the pediatric population [22,23]. Indeed, as noted by Mior and collaborators (2010) in the early stages of implementing collaborative care between conventional and CAM practitioners, informal social meetings or events could foster the personal relationships that help to develop mutual respect and trust and break down barriers to communication.

The majority of the survey respondents acknowledged the importance of communication, including exchanging information about common patients. The preferred communication methods were verbal via the patients or  Respondents could check more than one answer in writing for practitioners. In a previous study, physicians who recommended that parents of young patients consult with a CAM practitioner reported that sustained two-way communication with CAM practitioners was rare [24]. In the present study, one third of respondents provided parents with a written referral, regardless of profession. In their cross-sectional study on communication regarding pediatric patients, Ben-Arye and collaborators (2007) found that, in addition to communicating clinical information and increasing the willingness to respond to the initial referral, the exchange of referral letters positively impacted the development of collaboration between physicians and CAM practitioners [25]. Letters about common patients should include conventional/CAM diagnoses using jargon-free terminology, possible conventional/CAM treatment interactions, and treatment plan and goals [26]. Since disclosure and overall discussion about CAM use during physician/patient encounters is reportedly limited [5,27], especially for the pediatric population, verbal exchanges via the patient may not guarantee efficient communication between practitioners compared to exchanging letters. Communication is also an important catalyst for other determinants of collaboration, such as sharing and developing mutual trust and respect [14]. Mutual respect implies knowledge and recognition of the complementarity of other professionals' contributions [12]. Pediatricians who have a high level of knowledge about CAM are more likely to recommend CAM and discuss CAM with parents [23]. In this study, half the physicians asked  Stepwise backward logistic regression an osteopath directly for information while about 20% asked other professionals such as colleagues about the role of osteopathy for pediatric patients. Having such direct sources of information and believing osteopathy to be appropriate for certain pediatric conditions are associated with physicians' greater knowledge of general osteopathic practice parameters. More knowledge about practice parameters may lead to safer, appropriate twoway referrals. However, very few physicians said they had access to continuing education sessions about osteopathy, suggesting that formal education and information transmission concerning osteopathy are rare and could be improved. Less than half the physicians knew that non-physician osteopaths are not yet regulated in Quebec, suggesting some confusion about the current status of osteopathic practice. However, the majority of respondents, both physicians and osteopaths, expected to be positively influenced by professional regulation and osteopathic university-based education. IPC between conventional and CAM practitioners is known to be affected by systemic determinants such as regulation [16]. For example, practice conditions governed by Colleges of Physicians and Surgeons were found to be a significant barrier to IPC [15,28] and, even when referral to unregulated CAM is allowed, physicians must consider patients' needs in light of medical, legal and ethical issues [29]. A positive view of the upcoming regulation and the creation of osteopathic university-based programs in Quebec might improve interactions between practitioners and increase interprofessional education for future practitioners. More interactions during the training phase might in turn diminish current power disparities observed in practice [30].

Study limitations

This study has some limitations. First, the low response rate, especially for physicians, may limit the generalizability of the results. Although the response rate is typical for this type of survey [3], lack of time, low perceived relevance of the study topic or receiving more surveys a week might explain the physicians' low response rate [31] compared to the osteopaths. Nearly half the physicians had personally used osteopathic services, which may suggest that those respondents already had a positive attitude toward osteopathy. Since the respondents appeared to be open to and in favour of collaboration, the results likely underestimate the challenges facing collaboration between physicians and osteopaths. To deepen our understanding of IPC between these practitioners, some of the results of this survey are being further explored with qualitative data from purposeful sampling of physicians and osteopaths (phase 2 of the study).

Conclusions

This study examined the current IPC situation between physicians and non-physician osteopaths working with pediatric patients. Findings suggest that some collaboration already exists, including mutual referrals, but optimizing this collaboration still poses some challenges. Given the pending regulation of the osteopathic profession in Quebec and the need to promote the development of a healthcare delivery model that fosters safe patient-centred care, efforts must be made to reinforce communication skills and opportunities, provide physicians with easily accessible information about osteopathy, and ensure that regulatory bodies establish and maintain relationships after regulation.

Fig. 1
1Response flowchart

Abbreviations CAM: Complementary and alternative medicine; IPC: Interprofessional collaboration

Table 2
2Physicians' general knowledge about osteopathic practice parameters and belief in the active role of osteopathy for specific pediatric conditions (n = 274)Currently in Quebec, osteopaths …

Table 1
1Characteristics of participantsCharacteristics
Physicians (n = 274)
Osteopaths (n = 297)

Freq. (%; 95% CI) a
Freq. (%; 95% CI)

Gender (female)
202 (73.7; 68.0, 78.8)
236 (79.5; 74.3, 83.8)

Working experience (yrs):

0-4
22 (8.1; 5.3, 12.2)
55 (18.5; 14.4, 23.5)

5-9
34 (12.5; 8.9,17.2)
97 (32.7; 27.4, 38.4)

10-14
34 (12.5; 8.9, 17.2)
64 (21.5; 17.1, 26.8)

15-20
45 (16.5; 12.4, 21.6)
43 (14.5; 10.8, 19.1)

+21
137 (50.4; 44.3, 56.5)
38 (12.8; 9.3, 17.3)

Type of practice b :

Group
75 (27.7; 22.5, 33.5)
159 (53.5; 47.7, 59.3)

Solo
25 (9.2; 6.2, 13.5)
159 (53.5; 47.7, 59.3)

Hospital
155 (57.2; 51.1, 63.1)
n/a

Community practice
132 (48.7; 42.6, 54.8)
n/a

Rehabilitation centre
9 (3.3; 1.6, 6.4)
n/a

Other
13 (4.8; 2.7, 8.3)
11 (3.7; 2.0, 6.7)

n/a not applicable

a

Percentages reflect missing data (2 or 3 respondents)

b

Table 3
3Communication aspects including relationship, referrals and communication methodsRelationship and referrals

Table 4
4Factors associated with osteopathic referrals by physiciansOR (95% CI)
P value

Gender (female)
1.97 (0.96, 4.02)
0.064

Direct source of information
1.93 (0.98, 3.81)
0.058

Belief in active role for pediatric conditions
1.22 (1.01, 1.47)
0.042

Community practice
1.89 (1.03, 3.47)
0.040

Personal consultation
2.58 (1.35, 4.93)
0.004

Professional relationship
4.10 (2.12, 7.95)
<0.001

AcknowledgmentsFinancial support for CM and IG (Junior 1 Researcher) from the Fonds de la Recherche du Québec en Santé (FRQ-S) is gratefully acknowledged.FundingThis study was supported in part by the Canadian Interdisciplinary Network for Complementary and Alternative Medicine Research (INCAM). INCAM has no role in the design of the study, the collection, the analysis, and the interpretation of the data or in the writing of the manuscript.Availability of data and materialsThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.Competing interestsThe authors declare that they have no competing interests.• We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal Submit your next manuscript to BioMed Central and we will help you at every step:
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Genome-wide association of milk fatty acids in Dutch dairy cattle
R E S E A R C H A R T I C L E Open Access
Background: Identifying genomic regions, and preferably individual genes, responsible for genetic variation in milk fat composition of bovine milk will enhance the understanding of biological pathways involved in fatty acid synthesis and may point to opportunities for changing milk fat composition via selective breeding. An association study of 50,000 single nucleotide polymorphisms (SNPs) was performed for even-chain saturated fatty acids (C4:0-C18:0), even-chain monounsaturated fatty acids (C10:1-C18:1), and the polyunsaturated C18:2cis9,trans11 (CLA) to identify genomic regions associated with individual fatty acids in bovine milk. Results: The two-step single SNP association analysis found a total of 54 regions on 29 chromosomes that were significantly associated with one or more fatty acids. Bos taurus autosomes (BTA) 14, 19, and 26 showed highly significant associations with seven to ten traits, explaining a relatively large percentage of the total additive genetic variation. Many additional regions were significantly associated with the fatty acids. Some of the regions harbor genes that are known to be involved in fat synthesis or were previously identified as underlying quantitative trait loci for fat yield or content, such as ABCG2 and PPARGC1A on BTA 6; ACSS2 on BTA 13; DGAT1 on BTA 14; ACLY, SREBF1, STAT5A, GH, and FASN on BTA 19; SCD1 on BTA26; and AGPAT6 on BTA 27. Conclusions: Medium chain and unsaturated fatty acids are strongly influenced by polymorphisms in DGAT1 and SCD1. Other regions also showed significant associations with the fatty acids studied. These additional regions explain a relatively small percentage of the total additive genetic variance, but they are relevant to the total genetic merit of an individual and in unraveling the genetic background of milk fat composition. Regions identified in this study can be fine mapped to find causal mutations. The results also create opportunities for changing milk fat composition through breeding by selecting individuals based on their genetic merit for milk fat composition.

Background

The biosynthesis of bovine milk fat is a complicated process regulated by many genes belonging to several pathways [1]. Genetic analyses of bovine milk fatty acids have shown heritable variation. Short and medium chain fatty acids (C4:0 up to and including C16:0), which are synthesized de novo in the mammary gland, have moderate to high heritability [2,3]. Long chain fatty acids (i. e. C16:0 and higher) are derived from blood lipids that originate mainly from the diet and endogenous lipids, nevertheless, they have low to moderate heritability [2][3][4]. Identification of genomic regions, and preferably individual genes, responsible for genetic variation in milk fat composition will enhance the understanding of biological pathways involved in fatty acid synthesis and may point towards opportunities for changing milk fat composition via selective breeding. Candidate gene studies have shown that polymorphisms in diacylglycerol O-acyltransferase 1 (DGAT1 K232A) [5] and stearoyl-CoA desaturase 1 (SCD1 A293V) [6] have important effects on milk fat composition [7][8][9][10][11][12]. Many genes are involved in the biosynthesis of milk fat, and analyzing these candidate genes one by one in a candidate gene approach is not an option; therefore, quantitative trait loci (QTL) studies try to identify regions associated with milk fat composition to identify candidate genes that are worth considering.

In order to identify genomic regions involved in the biosynthesis of milk fat, Schennink et al. [13] and Stoop et al. [14] performed genome-wide linkage analyses of milk fatty acids and detected genome-wise significant QTL and several suggestive QTL. Other linkage studies have been performed for single chromosomes [15] or the fat composition of adipose tissue in beef cattle [15][16][17][18].

Recent developments in molecular genetics have made it possible to perform genome-wide association studies using thousands of single nucleotide polymorphism (SNP) markers to detect QTL. A genome-wide association study has higher power to detect QTL and provides more precise estimates of QTL locations compared to a linkage study. Some genome-wide associations for routinely evaluated traits in dairy cattle, such as milk production and fertility, have been published [19][20][21][22]. To the best of our knowledge, no genome-wide association study of milk fatty acids has been reported.

The aim of this study was to perform a genome-wide association analysis using 50,000 SNP markers to identify QTL for individual fatty acids in bovine milk. Associations were studied for even-chain saturated fatty acids (C4:0-C18:0), even-chain monounsaturated fatty acids (C10:1-C18:1), and the polyunsaturated fatty acid C18:2cis9,trans11 (CLA).

Methods

Phenotypes

The fat composition of winter milk samples from 1,905 first-lactation Dutch Holstein Friesian cows was available for this study. The cows were housed on 398 commercial farms throughout the Netherlands. At least three cows were sampled per farm. The cows were between 63 and 282 days in milk. The period of negative energy balance in early lactation was avoided by choosing cows over 63 days in lactation. The population consisted of five large paternal half-sib families from proven sires (200,199,195,176, 101 daughters per sire) and 50 small paternal half-sib families from test-sires (10-24 daughters per sire), as well as 190 cows descending from 45 other proven sires (1-30 daughters per sire). The pedigree of the cows was supplied by CRV (Cooperative cattle improvement organization, Arnhem, the Netherlands) and consisted of 26,300 animals.

Milk fat composition was measured by gas chromatography. Many fatty acids were measured, but only the major fatty acids are reported here: even-chain saturated fatty acids C4:0 to C18:0, even-chain (cis9) monounsaturated fatty acids C10:1 to C18:1, and the polyunsaturated fatty acid CLA. The fatty acids were expressed in terms of weight-proportion of total fat weight (w/w%). In total, these fatty acids made up 89% of the total fat. Table 1 presents the mean, phenotypic standard deviation, and intra-herd heritability for the fatty acids included in this study. More detailed information about the population and phenotypes can be found in Stoop et al. [3].

Genotypes

Blood samples were collected from the cows for DNA isolation. The cows were genotyped using a custom Infinium Array (Illumina, San Diego, CA, USA) designed by CRV. In total, 1,810 cows were successfully (call rate > 90%) genotyped. The cows were genotyped for 50,855 technically successful SNPs. The assumed map positions of the SNPs were based on the bovine genome assembly BTAU 4.0 [23]. From these 50,855 SNPs, a total of 776 SNPs could not be mapped to any of the Bos taurus (BTA) chromosomes and were assigned to BTA 0. In addition, 591 of the SNPs were located on the X chromosome. The SNPs on BTA 0 and the X chromosome were included in the study. The average distance between SNPs was 52,452 bp. Monomorphic SNPs (n = 245), SNPs with a genotyping rate < 80% (n = 383), and SNPs with a genotype frequency < 0.006 (1-9 observations for one of the genotype classes, SNPs with two genotype classes instead of three were included in the final marker set; n = 5,494) were discarded from the original SNP set of 50,855 SNPs, resulting in the final marker set of 44,733 SNPs used for the association analysis. Table 2 provides an overview of the number of SNPs available for the association study per chromosome.

Statistical analysis

For the association study, both phenotype and genotype information was available for 1,706 individuals. A twostep single SNP association analysis was performed. In the first step, the genome was screened for interesting regions using a general linear model. In the second step, the interesting regions were verified using an animal model. In the first step, a genome-wide association study was performed with a general linear model using the R package 'SNPassoc' [24]. In this step, the analyzed phenotypes were pre-adjusted for systematic environmental effects, and the general linear model accounted for the SNP effect and the effect of sire. The general linear model used in the first step was:
y * ij = μ + sire i + SNP j + e ij ,(1)
where y* was the phenotype adjusted for the systematic environmental effects; sire was the fixed effect of sire; SNP was the fixed effect of SNP genotype; and e was the random residual. Sire effect was included in the SNPassoc model to account for paternal half-sib relations. Phenotypes were adjusted for days in milk, age at first calving, calving season, and herd. Adjusted phenotypes were obtained from the phenotypes of 1,905 cows using an animal model in ASReml [25]:
y ijklmn =μ + b 1 * dim i + b 2 * e −0.05 * dimi + b 3 * afc j + b 4 * afc 2 j + season k + scode l + herd m + animal n + e ijklmn ,(2)
where y was the (unadjusted) phenotype; μ was the overall mean; dim was the covariate describing the effect of days in milk; afc was the covariate describing the effect of age at first calving; season was the fixed effect of the class of calving season (June-Aug 2004, Sept-Nov 2004, or Dec 2004-Jan 2005); scode was the fixed effect accounting for differences in genetic level between groups of proven bull daughters, young bull daughters, and other bull daughters; herd was the random effect of herd, distributed as N(0, I σ 2 herd ), with identity matrix I and herd variance σ 2 herd ; animal was the random additive genetic effect of the individual, distributed as N(0, A σ 2 a ), with the additive genetic relationship matrix A and the additive genetic variance σ 2 a ; and e was the random residual, distributed as N(0, I σ 2 e ), with identity matrix I and residual variance σ 2 e . The genome-wide false discovery rate (FDR) was controlled according to the method described by Storey and Tibshirani [26], by separately calculating the genomewide FDR based on the P-values from the general linear model for each trait using the R package 'qvalue'. Associations with a genome-wide FDR < 0.05 for the general linear model were considered significant.

The first step was performed to identify interesting regions, which were then further analyzed with an animal model to account for all relationships among individuals. Including a polygenic effect and accounting for genetic relationships would be more appropriate [27]. The model including a polygenic effect is computationally demanding when analyzing many traits, SNPs, and animals; therefore, in the second step we only analyzed the regions that contained multiple SNPs that were significant in the first step.

A region started at the first significant SNP on a chromosome that was followed by an additional significant SNP within 10 Mbp; the region was extended as long as another significant SNP occurred within 10 Mbp from the previous one and ended at the last significant SNP that was not followed by another significant SNP within the next 10 Mbp. Thus, a region contained at least two significant SNPs. More than one region could be present on the same chromosome when there were groups of significant SNPs located within 10 Mbp from each other but further than 10 Mbp from the other region(s) on the chromosome. The 10 Mbp distance between significant SNPs is rather large, but it was chosen to prevent having many small regions on one chromosome, each containing a small number of significant SNPs.

In the second step, all SNPs in regions with significant effects were analyzed using animal model (2) extended with an SNP effect in ASReml [25]. In this model the phenotypes were simultaneously adjusted for systematic environmental effects, for all genetic relationships among individuals, and for the SNP genotype. Associations with a -log 10 (P-value) ≥ 3 were considered significant.

The genetic variance explained by an SNP was calculated from the estimated genotype effects from animal model (2) extended with an SNP effect and the observed genotype frequencies. The result was expressed as a percentage of the total additive genetic variance. These percentages can be overestimated, especially when the effect of an SNP is small, this is due to the so called Beavis effect [28]. The percentage of the total additive genetic variance explained by the most significant SNP per trait per region is reported. The most significant SNP can differ per trait for a region associated with multiple traits.

Results

In the first step of the single SNP association study, all SNPs were analyzed using a general linear model. The analysis resulted in many significant (FDR < 0.05) associations between SNPs and the studied fatty acids. Figure 1 shows the genome-wide plots of -log 10 (P-values) for all of the studied fatty acids. All analyzed fatty acids showed significant associations with at least one genomic region.

From the results of the general linear model, a total of 64 significant regions were defined, affecting one or more (up to 10) traits. Significant associations with SNPs were detected on all chromosomes; however, on BTA 29 we identified only a single SNP associated with a trait instead of multiple SNPs in a region. Therefore, no regions were defined on BTA 29. These 64 regions were analyzed with an animal model and 54 remained significant at -log 10 (P-value) ≥ 3. Thus, most of the regions identified with the general linear model were confirmed with the animal model. The correlation between P-values in both analyses was 0.90. All results mentioned hereafter originate from the animal model and concern the 54 regions that remained significant at -log 10 (P-value) ≥ 3 with the animal model.

Significant associations were detected with several regions for most of the fatty acids, especially C14:0 (19 regions) and C16:1 (18 regions), whereas C18:0 and C12:0 were associated with only one region ( Table 3). The X chromosome showed significant association with C14:0. Table 3 shows that several regions affect more than one trait. In particular, the medium chain unsaturated fatty acids were often associated with the same region.

Sixteen of the unmapped SNPs showed significant associations with one or more of the studied fatty acids. Comparing the sequence of these significant unmapped SNPs against genome assembly UMD 3.0 [29] identified 14 of them as mapping to regions 14a, 19b, and 26, which were already identified as being associated with the traits. The other two unmapped SNPs (ULGR_BTA-38166 and ULGR_BTA-38169) were significantly associated with C10:0 and located at 24.2 Mbp on BTA 16 according to the UMD 3.0 genome assembly [29].

For each region, we estimated the variance explained by the most significant SNP for each trait. Within a region, this most significant SNP can be a different SNP per trait. The percentage of total additive genetic variance explained by the most significant SNP per region ranged from 0.9 for C10:0 on BTA 1 to 61.9 for C18:1 on BTA 14 (Table 3). These percentages can be overestimated, especially when the effect of an SNP is small, this is due to the so called Beavis effect [28]. The QTL with large effects, such as region 14a and 26, are less likely overestimated.

Three chromosomes showed highly significant regions, which were associated with multiple fatty acids: region 14a showed significant associations with 10 traits, region  Table 3 Regions significantly (-log 10 (P-value ≥ 3)) associated with fatty acids and the percentage of the total additive genetic variance explained  [7][8][9][10][11][12]. These regions explained a large portion of the total additive genetic variation. The results also showed that additional regions had an effect on single fatty acids or a certain group of fatty acids. In this section the SNP showing the strongest association of the whole genome for each trait is reported. Strongest associations for C14:0 (-log 10 (P-value) = 36.6), C16:0 (-log 10 (P-value) = 51.3), and C16:1 (-log 10 (P-value) = 54.1) were found with two SNPs (ULGR_SNP_AJ318490_1b and ULGR_SN-P_AJ318490_1c), located at 0.4 Mbp on BTA 14, that are responsible for the DGAT1 K232A polymorphism. For C6:0 (-log 10 (P-value) = 8.4), C18:1 (-log 10 (P-value) = 48.6), and CLA (-log 10 (P-value) = 15.8) the DGAT1 K232A SNPs were the most significant SNPs immediately after an unmapped SNP (ULGN_SN-P_AJ318490_2). After comparing this unmapped SNP sequence against the sequence of DGAT1, we identified it as an SNP located in DGAT1 that is in high LD (r 2 = 0.99) with the DGAT1 K232A SNPs. The strongest associations for C10:1 (-log 10 (P-value) = 41.1), C12:1 (-log 10 (P-value) = 22.8), and C14:1 (-log 10 (Pvalue) = 80.0) were found with an unmapped SNP (ULGR_SNP_SCD). Also, C16:1 showed strong association (-log 10 (P-value) = 34.8) with this unmapped SNP on BTA 26, but C16:1 showed the strongest association with the DGAT1 K232A SNPs on BTA 14 as mentioned above. After comparing this unmapped SNP sequence against the sequence of SCD1, we identified it as the SNP underlying the SCD1 A293V polymorphism. Although SCD1 was not mapped on genome assembly BTAU 4.0, it was mapped on genome assembly UMD 3.0 at 21.1 Mbp on BTA 26 [29]. The strongest associations for C10:0 (-log 10 (P-value) = 11.0) and C12:0 (-log 10 (P-value) = 9.7) were found with an SNP (ULGR_MARC_10099_486) located at 52.5 Mbp on BTA 19. The strongest association for C4:0 (-log 10 (Pvalue) = 6.6) was found with an SNP (ULGR_BTA-45866) located at 54.6 Mbp on BTA 19, and the strongest association for C8:0 (-log 10 (P-value) = 6.2) was found with an SNP (ULGR_BTA-45847) located at 55.1 Mbp on BTA 19. Also, C14:0 showed strong association (-log 10 (P-value) = 22.0) with an SNP (ULGR_BTA-45758) on BTA 19 located at 52.1 Mbp, but C14:0 showed the strongest association with the DGAT1 K232A SNPs on BTA 14 as mentioned above. The strongest association for C18:0 (-log 10 (P-value) = 5.8) was found with an SNP (ULGR_BTA-28678) located at 64.2 Mbp on BTA 7.

Discussion

This study is the first to report a genome-wide association study of bovine milk fatty acids. A two-step single SNP association analysis was performed. In the first step, the genome was screened for interesting regions using a general linear model. In the second step, the interesting regions were verified using an animal model. The animal model accounted for all relationships between individuals in the pedigree, whereas the general linear model accounted only for the paternal half-sib structure of the phenotyped cows. Ignoring relationships between individuals can cause false positive associations [30]; therefore, the animal model was applied to verify the results from the general linear model.

The results showed that DGAT1 and SCD1 are highly associated with several of the fatty acids. The results also showed that, for some traits, other regions were more significantly associated, such as BTA 19 for some short and medium chain saturated fatty acids. In addition, many other regions were associated with fatty acids, but with smaller effects. Table 3 Regions significantly (-log 10 (P-value ≥ 3)) associated with fatty acids and the percentage of the total additive genetic variance explained (Continued) 26 2  Schennink et al. [13] and Stoop et al. [14] reported a linkage study of milk fat composition using some of the same data used in the present study. In the linkage study, 1,341 SNPs were genotyped in 849 cows and their seven sires. Schennink et al. [13] and Stoop et al. [14] detected genome-wide significant QTL on BTA 6, 14, 19, and 26 for fatty acids included in the present study. The QTL on BTA 14, 19, and 26 were confirmed in our study. Our results suggest a QTL on BTA 6 for C6:0 and C8:0 (Figure 1A), but for C6:0 only one SNP exceeded the FDR threshold, and for C8:0 none of the SNPs exceeded the FDR threshold. Therefore, this region was not included in the animal model analysis in our second step. Given the quite stringent threshold, this region is still likely to harbor a QTL for C6:0 and C8:0.

The suggestive QTL found by Schennink et al. [13] and Stoop et al. [14] on BTA 2, 13, 14, 17, 19, and 26 was also confirmed in our study. Other suggestive QTL reported in the studies could not be confirmed, though in some cases a QTL was indicated, but this did not pass the threshold. In addition, several novel regions significantly associated with fatty acids were detected in our study but not reported by Schennink et al. [13] and Stoop et al. [14].

Some studies of the fat composition of adipose tissue in beef cattle were confirmed by our findings regarding milk fat composition. This result indicates that these regions are not unique for milk fat composition. Our findings confirmed the QTL detected by Morris et al. [18] in subcutaneous fat from beef cattle: C16:1 on BTA 1; C14:0, C16:1, and C18:1 on BTA 19; C14:0 and C14:1 on BTA 26; and C14:0 on BTA 27. Our findings confirmed the QTL detected by Abe et al. [17] in back fat, intermuscular fat, or intramuscular fat from beef cattle: C14:0 and C18:1 on BTA 19. Our findings confirmed the QTL detected by Alexander et al. [16] in the longissimus dorsi of beef cattle: CLA on BTA 7. Our findings confirmed the QTL detected by Uemoto et al. [31] in intramuscular adipose tissue from beef cattle: C18:1 on BTA 19.

Major regions

Three major regions were detected in this genome-wide association study (regions 14a, 19b, and 26), with significant effects on milk fat composition. These regions showed highly significant associations with several fatty acids. The regions on BTA 14 and 26 are regions that have been studied previously, and our results confirm the earlier findings [5][6][7][8][9][10][11][12]. The region on BTA 19 has not been studied extensively in relation to milk fat composition, but it harbors a number of candidate genes involved in fatty acid synthesis.

The region on BTA 14 showing an association with C6:0, C8:0, C14:0, C16:0, C10:1, C12:1, C14:1, C16:1, C18:1, and CLA is the region harboring DGAT1, which is known to influence milk production traits [5] and milk fat composition [9,10,12]. For all of these traits, except C10:1, C12:1, and C14:1, the two most significant SNPs on BTA 14 (located at 0.4 Mbp) were the two SNPs corresponding to the dinucleotide substitution of DGAT1 resulting in a K to A amino acid substitution (DGAT1 K232A). The K allele is associated with larger fractions of C6:0, C8:0, C16:0, and C16:1, and with smaller fractions of C14:0, C18:1, and CLA.

For C10:1 and C14:1, the most significant SNP (ULGR_BTC-068225) was located at 3.0 Mbp, and for C12:1 the most significant SNP (ULGR_BTC-067423) was located at 3.7 Mbp. However, for C10:1 and C12:1, the DGAT1 K232A SNPs were also significant. After correcting the phenotypes for the effect of the DGAT1 K232A polymorphism, the -log 10 (P-values) of these most significant SNPs decreased from 6.50 to 2.30 for C10:1, from 5.44 to 2.52 for C12:1, and from 4.63 to 2.40 for C14:1. The LD between these most significant SNPs and the DGAT1 K232A SNPs was moderate (r 2 = 0.26 and 0.31). Although the associations of C10:1, C12:1, and C14:1 were just below the significance threshold after correcting for the DGAT1 K232A genotype, the findings still suggest that an additional QTL may be present for the medium chain unsaturated fatty acids on BTA 14.

The region on BTA 26 that showed an association with C10:0, C14:0, C16:0, C10:1, C12:1, C14:1, and C16:1 is the region harboring SCD1, which is known to be associated with the desaturation of fatty acids [7,8,10,11]. For all of these traits, except C16:0, the most significant SNP on BTA 26 corresponded to the nucleotide substitution in SCD1 that causes an A to V amino acid substitution (SCD1 A293V) at 21.1 Mbp. The A allele was associated with larger fractions of C10:1, C12:1, and C14:1, and with smaller fractions of C10:0, C14:0, and C16:1. Thus, the A allele resulted in more C10:1 and C14:1 at the cost of C10:0 and C14:0. A similar effect was found for C12:0 and C12:1, though C12:0 was not significantly associated with the SNP. The opposite effect was found for C16:0 and C16:1; the A allele resulted in less C16:1 and more C16:0, though C16:0 was not significantly associated with the SNP. The SCD1 gene codes for the SCD enzyme, which desaturates saturated fatty acids to Δ9 unsaturated fatty acids in the mammary gland [32]. The association of this SNP with, and its effects on, the medium chain unsaturated fatty acids and their equivalent saturated fatty acids is, therefore, in agreement with the function of the enzyme. The associations we identified for the medium chain unsaturated fatty acids confirm previous studies on the effect of the SCD1 A293V polymorphism on milk fatty acids [8,10,11]. The associations we identified for the medium chain saturated fatty acids confirm only the results of Schennink et al. [10], who used the same population as the present study.

The SCD1 A293V SNP was not significant for C16:0 (-log 10 (P-value) = 1.09), which also confirms previous studies [7,10,11]. The most significant SNP for C16:0 on BTA 26 was located at 28.8 Mbp. This SNP was not in LD with the SCD1 A293V SNP (r 2 = 0.08), and correcting for SCD1 A293V had little effect on the significance of the SNP associated with C16:0 (-log 10 (P-value) decreased from 5.52 to 4.46). Also, one allele of this SNP is associated with larger fractions of C10:1, C12:1, C14:1, and C16:0, and with smaller fractions of C10:0, C12:0, C14:0, and C16:1, suggesting that it has something to do with desaturation, but it was only significantly associated with C16:0. We did not identify obvious candidate genes in this region.

The region on BTA 19, at 32.7-61.8 Mbp, showed associations with C4:0, C8:0, C10:0, C12:0, C14:0, C16:1, C18:1, and CLA, i.e. mainly with the short and medium chain saturated fatty acids and with the long chain unsaturated fatty acids. No significant effects were found for C6:0, but a QTL was indicated below the threshold in the region on BTA 19 ( Figure 1A). Morris et al. [15] performed a linkage analysis of milk fatty acids on BTA 19, detecting QTL for C8:0, C10:0, C12:0, C14:0, C18:1, and C18:2, which was confirmed by our findings and suggested fatty acid synthase (FASN, at 52.2 Mbp) as a candidate gene responsible for the observed effect. In addition to FASN, several other genes located within the region on BTA 19 are involved in the biosynthesis of milk fat, including sterol regulatory element binding transcription factor 1 (SREBF1, at 35.7 Mbp), ATP citrate lyase (ACLY, at 43.4 Mbp), signal transducer and activator of transcription 5A (STAT5A, at 43.7 Mbp), and growth hormone (GH, at 49.7 Mbp). These genes are all candidate genes because SNPs in the whole region showed an association with the traits, perhaps in LD with mutations in genes not captured by our marker set. The strongest association was found near FASN, but also near some other candidate genes as discussed below.

The region on BTA 19 was strongly associated with C14:0 and explained a large portion of the total additive genetic variation of C14:0. The SNP most significant for C14:0 (-log 10 (P-value) = 22.04) was also the most significant SNP for C18:1 (-log 10 (P-value) = 4.52) and located at 52.1 Mbp on BTA 19 (ULGR_BTA-45758 in LOC518878), which is 71,862 bp from FASN. This SNP also showed significant effects on C4:0 (-log 10 (P-value) = 3.57), C10:0 (-log 10 (P-value) = 7.01), C12:0 (-log 10 (Pvalue) = 5.96), and CLA (-log 10 (P-value) = 3.67), whereas the association with C8:0 (-log 10 (P-value) = 2.96) was just below the threshold. The effects of SNPs on C8:0, C10:0, C12:0, and C14:0 were in opposite direction of the effects on C4:0, C16:1, C18:1, and CLA. Fatty acid synthase (encoded by FASN) is a multienzyme system involved in de novo fatty acid synthesis. The SNP effects suggest that less C4:0 and more C8:0, C10:0, C12:0, and C14:0 are produced by fatty acid synthesis, or vice versa, which is in line with the function of FASN. Three SNPs in FASN were included in the marker set used in our study; however, two of them (FASN 16009 and FASN 763 ) were monomorphic for our population. The third SNP, FASN 17924 , showed association with C14:0 (-log 10 (P-value) = 3.05). Schennink et al. [33] also studied FASN 16024 , finding a significant association with C14:0 for the same population as in our study. Morris et al. [15] did not find significant associations between FASN 17924 and C14:0, but did find a significant association between C14:0 and FASN 15531 and FASN 15603 .

A haplotype of five FASN SNPs has been shown to be significantly associated with C6:0, C8:0, C10:0, C12:0, C14:0, and C18:1 in Friesian-sired cows [15], which are almost the same traits for which we found an association in the region, though not specifically with SNPs in FASN. This finding suggests a QTL in this region with an effect on short and medium chain fatty acids and long chain unsaturated fatty acids, but whether it is actually FASN that causes the association remains unclear. Our genome-wide scan showed that an SNP outside of FASN is the most significant SNP for C14:0 and was also associated with some of the other traits. This SNP showed very strong association with C14:0, whereas the association of C14:0 with the SNP in FASN was just barely significant. Candidate gene studies have shown that FASN is mainly associated with C14:0, but we found associations with additional traits, similar to the haplotype findings of Morris et al. [15]. Perhaps the causal mutation is located outside of FASN and is mainly the effect on C14:0 strong enough to be detected by SNPs in LD with this mutation.

This region on BTA 19 also harbors GH, and SNPs in this gene have been associated with milk production traits, including fat yield [34]. One SNP in our marker set was located in exon 5 of GH (GH-D30713-299) and showed significant association with C18:1 (-log 10 (Pvalue) = 3.81). The neighboring SNP showed even more associations: with C8:0 (-log 10 (P-value) = 3.48), C10:0 (-log 10 (P-value) = 5.13), C12:0 (-log 10 (P-value) = 3.86), and C14:0 (-log 10 (P-value) = 7.41).

No SNPs were located in the other candidate genes on BTA 19. The SNP showing the strongest association with C16:1 was located at 43.8 Mbp on BTA 19 (BFGL-NGS-111365), which is 13,873 bp from STAT5A. The SNPs neighboring ACLY and STAT5A showed significant associations with several of these traits, especially C14:0. All SNPs in the regions seem to be detecting the same effect, which is strongest with C14:0. Although previous studies suggested FASN as the candidate gene for association, which of the candidate genes causes the effect remains debatable. The causal mutation might even be in a gene not considered here.

Additional regions

In addition to the three major regions mentioned above, many additional regions showed associations with fatty acids (see Figure 1). We will not discuss all of the regions in this paper, but what follows are some select regions that showed association with three or more traits.

On BTA 6, region 6a was associated with C12:1, C14:1, and C16:1 ( Table 3). The SNP effects were in the same direction for all three fatty acids. This region harbors the genes ATP binding cassette, subfamily G, member 2 (ABCG2, 37.4 Mbp) and peroxysome proliferatoractivated receptor-gamma coactivator-1alpha (PPARGC1A, 44.8 Mbp). The SNPs most significantly associated with C12:1 (-log 10 (P-value) = 5.03, at 44.3 Mbp), C14:1 (-log 10 (P-value) = 4.39, at 41.2 Mbp), and C16:1 (-log 10 (P-value) = 4.63, at 40.2 Mbp) were located between these candidate genes. ABCG2 has been associated with milk fat yield and percentage [35,36]. One SNP in our marker set was located in ABCG2 and showed no significant associations with the studied fatty acids. PPARGC1A has been associated with milk fat yield in German Holsteins [37], but this was not confirmed by Khatib et al. [38] in two larger American Holstein populations. Up to 10 SNPs in our marker set were located in PPARGC1A, but none of these were significantly associated with C12:1, C14:1, or C16:1. However, one of the 10 SNPs in PPARGC1A showed an association with C16:1, which was just below the significance threshold (-log 10 (P-value) = 2.86). In a candidate gene study, Schennink et al. [33] found a significant association in the same population as in the present study for two SNPs in PPARGC1A that were not included in our marker set: PPARGC1A 1790+514 with C16:1 and PPARGC1A 1892+19 with C14:1. Our genomewide scan indicates that not PPARGC1A, but another region has the strongest effect on the unsaturated medium chain fatty acids.

On BTA 7, two regions showed an association with several fatty acids: region 7a associated with C10:1, C12:1, C14:1, and C18:1; and region 7b associated with C14:0, C12:1, C14:1, and C18:0 (Table 3).

Region 7a showed an association with almost all unsaturated fatty acids. In general, the SNP effects on C18:1 were in the opposite direction of the SNP effects on C10:1, C12:1, and C14:1. SNPs in this region were either associated with C10:1, C12:1, and C14:1, or with C18:1, suggesting one QTL for the medium chain unsaturated fatty acids and another for C18:1. The SNP effects on C10:0, C12:0, and C14:0 were in the same direction as the effects on C10:1, C12:1, and C14:1, but they were not significant. The SNP effects on C18:0 were in the same direction as the effects on C18:1, but they were not significant. Although only unsaturated fatty acids were significantly associated with this region, the SNP effects suggested that this QTL has nothing to do with desaturation because the SNP effects on unsaturated fatty acids were in the same direction as the effects on their saturated equivalents.

Region 7b showed associations with C14:0, C18:0, C12:1, and C14:1. The most significant SNP for each trait was different, but they were located in the same neighborhood, around 64.0-64.1 Mbp, and were in high LD with one another (r 2 = 0.53-0.97). This finding indicates the likelihood of one QTL in this region with an effect on these four traits. The effects of these most significant SNPs were in the opposite direction for C18:0 compared to C14:0, C12:1, and C14:1. The SNP effects on C10:0 and C12:0 were in the same direction as the effects on C14:0, C12:1, and C14:1, but they were not significant. The SNP effects on C16:0, C18:1, and CLA were in the same direction as the effects on C18:0, but they were not significant. These SNP effects suggest that this QTL has something to do with a trade-off between long chain fatty acids and de novo synthesis of medium chain fatty acids. No candidate genes were located in this region.

On BTA 13, region 13 was associated with C6:0, C8:0, C10:0, C14:1, and C16:1 (Table 3). This region confirms the QTL for C6:0, C14:1, and C16:1 detected by Stoop et al. [14], who also found that C8:0 and C10:0 showed a QTL around the same position as C6:0, but these QTL were just below the threshold and, therefore, not reported. For C6:0, C8:0, and C10:0, the same SNP, located at 64.8 Mbp (ULGR_SNP_BES11_Con-tig346_1209), was the most significant SNP in the region, and the SNP effect was in the same direction for all three traits. These traits have a high genetic correlation [3], which supports one QTL affecting these three short chain fatty acids. This SNP is located in acyl-CoA synthetase short-chain family member 2 (ACSS2), which activates acetate for de novo fatty acid synthesis [1]; thus, ACSS2 is a good candidate gene for a QTL with an effect on C6:0, C8:0, and C10:0. Given that this particular significant SNP is located in an intron (between exon 16 and 17) of ACSS2, this SNP is not likely the causal mutation, but it can be in high LD with the causal mutation. In addition, the region also had an effect on C14:1 and C16:1. The SNP effects for C14:1 and C16:1 were in the same direction, but these effects were in opposite direction of SNP effects on C6:0-C10:0. The SNP in ACSS2 had no significant association with C14:1 or C16:1, which indicates an additional QTL with an effect on these unsaturated fatty acids.

On BTA 27, region 27 was associated with C14:0, C16:0, C10:1, C12:1, C14:1, and C16:1 ( Table 3). The region on BTA 27 includes 1-acylglycerol-3-phosphate O-acyltransferase 6 (AGPAT6), which is the most abundant isoform of all AGPAT mRNA (~60%) in the mammary gland and involved in positioning fatty acids on the second position of the triglyceride backbone [1]. Given that 62.2% of C14:0 and 43.1% of C16:0 is located at the second position of the triglyceride backbone [39], this gene might be a candidate for this association. The SNP effects on C14:0 and C16:0 were in opposite directions, which suggests competition between C14:0 and C16:0 for the second position of the triglyceride backbone as an explanation for this association. In AGPAT6 knock-out mice, the composition of the triacylglycerol is altered and contains proportionally more polyunsaturated fatty acids at the expense of monounsaturated fatty acids [40], which might explain the effect of this region on the monounsaturated fatty acids. In general, the SNP effects on C10:1, C12:1, C14:1, and C16:1 were in the same direction. Table 3 shows that regions 14a and 26 explained a large portion of the total additive genetic variation of C10:1, C12:1, C14:0, C14:1, C16:0, C16:1, C18:1, and CLA. This variation is caused by DGAT1 and SCD1. For other traits, however, no regions had such large effects. The sum of the total additive genetic variance explained by the most significant SNP per region for C4:0-C12:0 and C18:0 was less than 20% of the total additive genetic variance. This finding suggests that these traits are influenced by many genes with small effects. Regions explaining roughly 1% of the total additive genetic variation or more were detected by the studied design (e.g., C10:0 on BTA1), but additional regions with either undetectable small effects or that were not dense enough in our marker set to detect the effect is likely.

Variance explained

Even though short chain fatty acids are produced by de novo synthesis, less than 20% of the total additive genetic variance was explained by the analyzed regions. FASN plays an important role in de novo synthesis, but less than 6.5% of the total additive genetic variance of short chain fatty acids is explained by the region harboring FASN. De novo synthesis of fatty acids requires several compounds in addition to FASN enzymes to elongate fatty acids. One of the compounds is acetate, which is activated by ACSS2 for de novo synthesis, ACSS2 was indicated as a candidate gene associated with C6:0-C10:0 in this study. More genes like this that assist FASN in de novo synthesis and, therefore, explain a portion of the total additive genetic variation is likely.

Also, genes involved in transport and triacylglyceride production might explain some of the total additive genetic variation.

Conclusions

A genome-wide association study of 50,000 SNPs was performed for milk fatty acids, resulting in many QTL. All over the genome regions were associated with milk fatty acids, some regions with just one fatty acid and other regions with multiple fatty acids. Milk fat composition is strongly influenced by polymorphisms in DGAT1 and SCD1, genes that have large effects on medium chain fatty acids and unsaturated fatty acids. Several regions showed associations with these milk fatty acids, but with smaller effects. The short chain fatty acids, C12:0 and C18:0, are not strongly affected by genes with large effects, but are influenced by regions with small effects. Some regions included candidate genes involved in milk fat synthesis pathways. On BTA 19, there were several genes involved in fat synthesis underlying the region associated with multiple fatty acids. Only in a few cases was an SNP associated with fatty acids actually located in a candidate gene. Regions identified in this study can be fine mapped to find causal mutations. The results also create opportunities for changing milk fat composition through breeding by selecting individuals based on their genetic merit for milk fat composition, which can be retrieved from the estimated SNP effects and the individual's genotype.

Figure 1
1Genome-wide association plots for milk fatty acids. Genome-wide plots of -log 10 (P-values) (y-axis) for association of loci with saturated fatty acids (A) and unsaturated fatty acids (B) analyzed with a general linear model. The genomic position is represented along the xaxis and chromosome numbers are given on the x-axis. The dashed horizontal lines represent the 0.05 false discovery rate thresholds. The y-axis are cut off at -log 10 (P-value) of 9.

at the first significant SNP and proceeds if the next significant SNP is positioned within the next 10 Mbp on the same chromosome, ending at the position of the last significant SNP matching this requirement, with a minimum of 2 significant SNP per trait in a region. The number of the region stands for the BTA number plus an a, b, c or d indicating different regions within a BTA.2 Percentage of the total additive genetic variance explained by all the most significant SNPs per region together. This was analyzed with the animal model and all the most significant SNPs per region simultaneous in the model. Percentage of total additive genetic variance explained by the most significant SNP associated with the trait per region, analyzed with an animal model.

Table 1
1Descriptive statistics of milk fatty acidsTrait
Mean
s P

1

h 2

IH

C4:0
3.50
0.24
0.44

C6:0
2.22
0.14
0.47

C8:0
1.37
0.12
0.61

C10:0
3.03
0.35
0.72

C12:0
4.11
0.46
0.64

C14:0
11.61
0.78
0.62

C16:0
32.59
2.15
0.43

C18:0
8.72
1.18
0.24
C10:1
0.37
0.06
0.34

C12:1
0.12
0.02
0.38

C14:1
1.36
0.23
0.34

C16:1
1.44
0.30
0.44

C18:1
18.18
1.57
0.26

CLA
0.39
0.07
0.42

1 Phenotypic standard deviation after adjusting for systematic environmental
effects: days in milk, age at first calving, season of calving, and herd
Mean (w/w%), phenotypic standard deviation 1 (σ P ), and intra-herd heritability

( h 2

IH
) for the fatty acids of winter milk samples

Table 2
2SNP information per Bos Taurus chromosomeBTA
SNP
Length (Mbp)
SNP interval (bp)
Monomorph
Genorate < 80%
Freq < 0.006

0 1
776
-
-
1
6
84

1
3,011
160.91
53,371
4
25
355

2
2,451
140.64
57,333
20
19
269

3
2,342
127.13
54,212
12
11
260

4
2,300
124.09
53,930
11
20
239

5
2,215
125.78
56,788
5
18
214

6
2,844
122.39
43,050
12
25
334

7
2,017
111.67
55,392
8
14
209
8
2,131
116.93
54,818
15
15
243

9
1,860
108.05
58,090
14
20
206

10
1,911
106.10
55,406
14
11
202

11
2,193
110.01
50,187
18
12
239

12
1,512
85.22
56,324
6
14
147

13
1,689
84.00
49,732
11
12
155

14
2,122
81.29
38,272
9
17
222

15
1,446
84.23
58,130
5
7
169
16
1,455
77.83
53,454
8
12
183

17
1,561
76.40
48,942
5
14
159

18
1,282
66.04
51,429
3
8
131

19
1,452
65.13
44,826
5
7
147

20
1,479
75.41
50,985
3
9
156

21
1,246
69.08
55,440
5
15
130

22
1,256
61.75
49,161
4
10
139

23
1,169
53.27
45,570
7
10
107
24
1,296
64.93
50,141
10
13
159

25
1,256
43.44
34,617
6
9
109

26
1,131
51.00
45,097
7
6
152

27
933
48.73
52,280
3
13
117

28
899
46.01
51,184
6
5
94

29
1,029
51.78
50,371
6
4
109

X
591
88.46
149,940
2
2
55

Total
50,855
2,628
245
383
5,494

1 unmapped SNPs

Total number of SNPs, map length, average SNP interval, number of monomorphic SNPs, number of SNPs with a genotyping rate (genorate) < 80% and number
of SNPs with a genotype frequency (freq) < 0.006 for all Bos Taurus (BTA) chromosomes.

19b with 8 traits, and region 26 with 7 traits. The region 14a harbors DGAT1 and region 26 harbors SCD1; these genes are known to affect fat compositionRegion Start
(Mbp)

End
(Mbp)

# SNP in
region

Trait
#Traits/
region

C4:0 C6:0 C8:0 C10:0 C12:0 C14:0 C16:0 C18:0 C10:1 C12:1 C14:1 C16:1 C18:1 CLA

1a
24.8
24.8
2
3.3
1

1b
54.1
62.8
148
0.9
2.0
2

1c
121.5 129.9
120
3.0
1
1d
146.4 161.0
304
2.1
1

2a
27.6
27.7
3
1.8
1

2b
44.3
69.5
351
2.7
1

2c
126.1 139.5
257
4.0 3.3
2

3a
22.2
25.1
55
4.1
1

3b
125.2 126.9
39
2.9
5.3
2

4
121.8 123.7
30
2.2
1

5a
65.9
65.9
3
1.5
1
5b
81.9
99.9
275
2.7
4.8
2

5c
107.5 113.6
104
1.8
2.7
2

6a
31.4
45.2
630
4.3
1.3
3.1
3

6b
111.2 115.6
81
4.2
1

7a
10.7
23.8
206
4.1
3.9
3.3
4.3
4

7b
62.4
64.2
46
2.0
7.4
3.4
3.0
4

7c
100.3 110.7
166
3.1
1

8a
20.5
21.9
28
2.7
1
8b
52.7
58.4
104
4.5
4.2
2

8c
76.9 101.5
406
1.3
2.4
2

9a
21.4
21.6
2
1.1
1

9b
36.7
36.7
3
3.9
1

9c
48.8
50.4
25
3.9
1

9d
66.6
67.0
4
4.8
1

10a
8.3
8.3
3
1.2
1

10b
98.8 100.2
34
2.8
1
11
35.2
48.0
218
2.3
1

12
52.4
60.1
116
2.2
2.8
2

13
49.5
71.5
377
3.0 2.2
1.9
4.4
3.3
5

14a
0.0
26.3
1121
4.9 2.2
17.7
39.6
6.2
4.8
4.5
34.8
61.9 12.2
10

14b
40.0
40.8
7
3.7
1

15a
20.5
27.0
121
1.2
1.8
2

15b
60.7
65.1
63
3.4
1

15c
72.3
77.7
103
3.8
1
16
53.2
53.5
9
2.3
1

17a
31.4
34.3
43
2.9
1.9
2

17b
68.7
68.7
3
3.5
3.0
2

18a
14.1
29.5
305
1.8
3.1
3.5
3

18b
53.6
54.9
15
1.9
1

19a
6.1
6.1
3
2.3
1

19b
32.7
61.8
529
6.5
3.0
4.4
5.3
13.8
3.0
7.1 3.9
8

20
72.9
73.6
18
1.6
1
21
5.8
5.8
4
3.0
1

22
8.9
42.4
593
3.9
3.5
2

23
26.3
31.7
141
2.9
1

24a
40.4
49.1
142
1.3
1

24b
56.4
58.6
27
2.2
1

25
42.9
42.9
2
1.5
1

AcknowledgementsThis study is part of the Dutch Milk Genomics Initiative and the project 'Melk op Maat', funded by Wageningen University (the Netherlands), the Dutch Dairy Association (NZO, Zoetermeer, the Netherlands), the cooperative cattle improvement organization CRV (Arnhem, the Netherlands), the Dutch Technology Foundation (STW, Utrecht, the Netherlands), the Dutch Ministry of Economic Affairs (The Hague, the Netherlands) and the Provinces of Gelderland and Overijssel (Arnhem, the Netherlands). The authors thank the herd owners for their help in collecting the data.Authors' contributions ACB carried out the analysis, prepared and drafted the manuscript. HB participated in the design of the study, the coordination and helped to draft the manuscript. MHPWV helped to draft and edit the manuscript. JAMA initiated and established the overall project design. All authors read and approved the final manuscript.
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