Plasma PUFA were measured in 5571 children 8 months later, and 4380 children had complete dietary and plasma data. The association between dietary and plasma PUFA proportions was estimated using Spearman’s correlation coefficients, quintile cross-classification and Cohen’s κ coefficients. Mean total PUFA intake was 13·2 g/d (sd 4·2), contributing 6·5 % of total energy intake; n-6 PUFA contributed 5·2 % and n-3 PUFA 0·7 %. The n-6:n-3 ratio was 7·9:1. Mean intakes of EPA and DHA were 35·7 mg/d and 49·7 mg/d, respectively. Most n-3 and n-6 PUFA intakes were weakly correlated with their respective plasma lipids . The correlation between dietary and plasma DHA was stronger though , supported by a modest level of agreement between quintiles (k = 0·32). The results indicate that the FFQ was able to reasonably rank the long-chain (LC) PUFA, DHA, in this paediatric population. Public health initiatives need to address the suboptimal ratio of n-6:n-3 PUFA and very low n-3 LC-PUFA intakes in school-age children in the UK.An adequate intake of PUFA plays a vital role in human health. Therefore, it is important to assess PUFA intakes in different populations and validate them with biomarkers, but only a few small studies are in paediatric populations. We calculated the dietary intake of PUFA and their main food sources in children and assessed associations between PUFA intakes and plasma proportions. Dietary intakes of 7-year-old children ( They may also play an important role in modulating the risk of CVD, inflammatory and neurodegenerative diseases,3,4. PUFA consist of two distinct families: n-3 and n-6. The medium-chain parent fatty acids (FA), n-3 α-linolenic acid (ALA) and n-6 linoleic acid (LA) are termed essential because they cannot be synthesised endogenously and so need to be provided by diet. In contrast, the n-3 and n-6 long-chain (LC) PUFA can be derived either from the diet or endogenously synthesised from the parent PUFA. The n-3 and n-6 PUFA have distinct physiological functions,4,5. A low ratio of n-6:n-3 PUFA in the diet is important for health, since high ratios favour a pro-inflammatory state,4. Modern Western diets are generally low in n-3 PUFA, particularly in the marine LC-PUFA (EPA and DHA) while high in n-6 PUFA, resulting in an n-6:n-3 ratio often reaching up to 15–16:1. Therefore, lowering the current ratio is recommended, since an n-6:n-3 ratio of 2–3:1 is associated with reduced risk of many chronic inflammatory-related diseases. A high ratio of n-6:n-3 PUFA and/or inadequate EPA and DHA early in life may also be a potential risk factor for a range of neurodevelopmental cognitive disorders in childhood.PUFA are essential for human growth and development, forming a crucial part in membrane structures and brain and retinal development during infancy,9,10,11. The UK Scientific Advisory Committee on Nutrition recommends that 6·5 % of total energy (TE) intake should be from PUFA. The European Food Safety Authority recommends an intake of 250 mg/d of EPA and DHA. However, many Western populations fall well below this intake,16,17,18. Data from the nationally representative UK National Diet and Nutrition Survey (NDNS) showed that while total and n-6 PUFA intakes were in line with dietary guidelines, most children failed to meet recommended minimum weekly fish intakes. However, direct measures of EPA and DHA were not available. It is particularly relevant to assess adequacy of PUFA intakes in paediatric populations as suboptimal PUFA intakes early in life may modulate disease risk throughout the life course,20. It is also essential to validate the tools used to assess dietary PUFA intakes, which is generally done by studying PUFA concentrations in blood and tissue. Numerous biomarker validation studies in adults have compared PUFA intakes estimated using dietary questionnaires, records or recalls with tissue biomarkers, including FA in plasma, phospholipids, erythrocyte membranes and platelets or in adipose tissue,24,25,26. However, validation studies conducted in children are limited and mostly based on small sample sizes (n 35–404),28,29,30. Estimating dietary intake is particularly challenging in children, and reporting error (notably under-reporting) can vary by age group,32.Many countries, including the UK, have made public health recommendations to replace the consumption of SFA with PUFAn-3 and n-6 PUFA within a paediatric population from the UK (n 8242) and (2) measure the correlations between PUFA intakes estimated through FFQ and PUFA concentrations in plasma (n 4380) in children from the Avon Longitudinal Study of Parents and Children (ALSPAC).Therefore, the objectives of this study were to (1) assess the dietary intake and food sources of ,34,35 and are also available on the ALSPAC website . In addition, the study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool . In 1991–1992, 14 541 eligible pregnant women from the Southwest of England were enrolled into the study, resulting in 13 988 children alive at 1 year and followed since birth. During follow-up, extensive data have been regularly collected on the parents and their children, primarily using questionnaires, medical records, biological samples and clinical visits. The current study uses data from the child cohort when aged 6·8 (sd 0·1) years whose parents completed a child-based FFQ in 1997–1999 (n 8482) and from the children who took part in a research clinic at age 7·5 (sd 0·2) years and had blood samples collected and analysed (n 4380 children had blood samples and FFQ data), see http://www.bristol.ac.uk/alspac/researchers/research-ethics/) and conformed to the Declaration of Helsinki. Consent for biological samples was collected in accordance with the Human Tissue Act.The study participants were the core index children (first generation = G1) from ALSPAC, a transgenerational prospective birth cohort established to investigate the determinants of health and disease across the life course, including childhood development and growth. Full details of the cohort and study design have been described previously, with full details published previously. In summary, the questionnaire contained a series of questions enquiring about the frequency of the child’s habitual consumption of eighty different food and drinks and included questions about school meals and food items often consumed by children. The frequency ranges used were ‘never or rarely’, ‘once every 2 weeks’, ‘1–3 times a week’, ‘4–7 times a week’ and ‘more than once a day’. There were five questions directly relating to fish and seafood intake. These foods are high in n-3 LC-PUFA and thus allowed an estimate of n-3 LC-PUFA intakes particularly from fish sources. Foods normally consumed every day and in a variety of forms, such as bread, milk and fat spreads, were questioned in more detail. Standard portion sizes for children in this age group were used in combination with the reported frequency of consumption of each food/drink to calculate dietary intakes. Energy and nutrients intakes were estimated using the nutrient content of foods based on 5th edition of McCance and Widdowson’s (M&W) food tables. The food items and portion sizes assessed for the school meal section of the FFQ were informed by school menus collected at the time from local schools.The parental-completed FFQ was adapted from the original FFQ used to assess maternal diet in ALSPAC at 32 weeks of pregnancyn-3 and n-6 PUFA and individual PUFA , EPA and DHA). The PUFA composition of food items covered in the 7-year FFQ was primarily determined using the electronic version of M&W food composition tables . When necessary, this was supplemented with the M&W Fatty Acids Supplement (Ministry of Agriculture Fisheries and Food (MAFF), 1998) and data from the NDNS database. A manual matching process was employed to combine ALSPAC food codes with appropriate M&W code. If no exact match was found, a similar food item close to the original was used, resulting in all foods in the FFQ with any fat content (332 food items) being covered in the food composition database.A food composition database was created in order to calculate the children’s intake of total, . Plasma FA were extracted using transmethylation of lipids with acetyl chloride and methanol,44. Chromatographic separation of the FA methyl esters was achieved via a fast gas chromatography 6890 Plus LAN system (Agilent Technologies) coupled with a fused-silica, narrow bored DB-FFAP capillary column . Assays were carried out during 2009–2010 with the measurement of twenty-two FA, eleven of which were PUFA.Plasma obtained from the non-fasting blood samples was stored at –70°C, thawed once to obtain a 100 µl aliquot that was refrozen and shipped by airfreight to Rockville, MD, USA, and then thawed for final analysesn-3 and n-6 PUFA from eleven food groups was calculated and expressed as median daily intake and percentages of total PUFA intake . These food groups encompassed all the individual food items (except soft drinks) covered in the FFQ and consisted of (1) vegetables, pulses and potatoes; (2) bread, cereals and bakery products; (3) meat and meat products; (4) fish and fish products; (5) milk and milk products; (6) fat spreads and cooking fat; (7) crisps and savoury snacks; (8) nuts and seeds; (9) egg and egg dishes; (10) fruit and (11) sugar, preserves and confectionary. The contribution of dietary DHA and EPA (mg/d) from different categories of fish and seafood was also calculated.Analyses were performed using SPSS (version 19) and STATA 15 (Statacorp.). A total of 240 (2·8 %) of the original 8482 participants with FFQ data were excluded from the statistical analysis due to implausible dietary data, using cut-offs <15 000 and >140 000 kJ/week, based on inspecting the histogram of weekly energy intake. This gave a final study sample of 8242 participants with valid FFQ data and 4380 participants with both valid FFQ and blood plasma FA data. The analyses were carried in all participants and stratified by sex. The dietary and plasma PUFA data were assessed for normality, and since the majority of the data was not normally distributed, non-parametric tests were used (the data were not transformed). The children’s daily PUFA intake was summarised as medians and interquartile ranges, and as a percentage of TE intake. Plasma PUFA concentrations were presented as percentage of total FA. The contribution of dietary r). PUFA intakes were not log-transformed but were energy adjusted using the energy density method, by dividing each individual’s PUFA intake by their TE intake and then multiplying by 7000 (approximately the median energy intake in kJ/d). Cross-classification analysis was used to evaluate the agreement between the two PUFA measures. Energy-adjusted dietary PUFA intakes were classified into quintiles and then cross-tabulated with quintiles of the respective PUFA plasma proportion. Discordance and agreement in quintile rankings were evaluated by calculating the percentage of participants classified in the same quintile, same or adjacent quintile and opposite quintile. In addition, Cohen’s weighted kappa statistics (κw) and 95 % CI were calculated for quintiles of energy-adjusted PUFA intakes and plasma PUFA proportions, since they consider agreements that were due to chance. The strength of the correlations (r) and agreements (κw) was evaluated as poor (<0·2), moderate (0·2–0·59) or good (>0·6).The correlation between crude and energy-adjusted dietary PUFA intakes and plasma PUFA proportions was assessed by Spearman’s correlation coefficients (sd 1859) kJ/d. Fat intake (75·7 g/d) contributed 37·1 % to TE intake, of which 14·7 % of energy was from SFA, 11·8 % from MUFA and 6·5 % from PUFA (13·2 g/d). The sub-sample with both dietary and plasma FA data had a lower daily energy intake, mothers with a higher education, a higher family social class and were less overweight/obese compared with the sample with only FFQ data.The characteristics of the 8242, seven-year-old children with FFQ data and 4380 children with both FFQ and plasma FA data are outlined in n-6 PUFA; 80·3 % of total PUFA and 5·2 % of TE. This was mainly due to intake of LA, which contributed to a mean of 5·1 % of TE. n-3 PUFA accounted for 10·6 % of the total PUFA (0·7 % of TE), with the majority in the form of ALA. The daily intake of the DHA was 49·7 mg/d with 10 % of children having <15 mg/d. The LC n-3 PUFA (DHA and EPA) average intake was 85·4 mg/d. The n-6:n-3 ratio in the diet was 7·9:1.The reported intake of dietary FA and proportions of plasma FA  is shown in n-6 PUFA, particularly LA . AA (a LC n-6 PUFA) contributed 6·4 % of total plasma FA, whereas n-3 PUFA  contributed only 3·2 % of total FA and the contribution of DHA was more than twice that of either ALA or EPA. The PUFA intakes and PUFA plasma proportions are presented separately for females and males in Supplementary Tables 1 and 2, respectively. Statistical comparison of PUFA intakes and plasma proportions by sex indicated differences unlikely to be explained by chance; however, in absolute terms, the differences were minimal.The median concentration of total FA in plasma was 2·26 mg/ml (1·9–2·6 mg/ml for 25th and 75th percentile range). The PUFA plasma proportions were dominated by n-6 and n-3 PUFA and DHA intakes according to food groups are shown in 3 for sex-specific intakes). The highest intake of n-6 PUFA was from cereal-based products and from fat spreads and cooking fat, together contributing to almost half of n-6 PUFA intake. Further important sources were fats used in vegetable and potato dishes and in meat and meat products. The main source of n-3 PUFA was vegetable fat used in vegetable and potato dishes (28·5 %), followed by cereal products, meat and meat products and milk and milk products. The major dietary source of DHA and EPA was fish (contributing to 59·2 % of DHA and 45·9 % of EPA intake). Other dietary sources of the LC-PUFA in these children were meat and meat products, eggs (for DHA), and fats and spreads and milk and milk products (for EPA). Most other food groups provided no DHA or EPA. In terms of the different types of fish and seafood, coated fish contributed most to the children’s DHA and EPA intake, providing a mean of 7·9 mg/d and 6·8 mg/d, respectively (The mean daily intakes and percentage contribution to ectively . Anothern-6 and n-3 FA (LA and ALA) were weakly correlated with their respective plasma lipid concentrations . There were also weak correlations between dietary and plasma AA and between dietary and plasma EPA . The strongest correlation in our study was between dietary and plasma DHA , explaining about 8 % of the variance. The correlations were similar when female and male participants were analysed separately . With regard to correlations between different types of PUFA, the precursor of the n-6 series, dietary LA, was not correlated with plasma AA but it was weakly negatively correlated with plasma concentrations of EPA. For the n-3 PUFA, there were significant but weak positive correlations between dietary ALA, the precursor of the n-3 series, and EPA and DHA, and between dietary EPA and plasma DHA and vice versa.The correlation between energy-adjusted dietary PUFA intakes and PUFA plasma proportions is presented in n-6 and n-3 PUFA, there was poor agreement between their respective dietary and plasma measures (κ < 0·2). There was a moderate level of agreement between dietary and plasma DHA though .Cross-classification of quintiles of dietary and plasma PUFA subtypes showed that 54–79 % of children were classified into the same or adjacent quintile, with the highest agreement for DHA . In contn-6 and n-3 series of PUFA were assessed by FFQ in 7-year-old children living in South-West England in 1999–2000 and agreement with plasma PUFA measured 8 months later were assessed. On average, PUFA made up 6·5 % of TE intake, with the greatest proportion from n-6 PUFA (5·2 %) and only 0·7 % of energy from n-3 PUFA. This resulted in a n-6:n-3 ratio of 7·9:1. The majority of dietary n-6 PUFA were from fat spreads and cooking fat and from cereals and cereal-based products, whereas fish was the main source of LC-PUFA. In general, there were weak correlations between dietary PUFA and their corresponding plasma concentrations in blood. However, dietary DHA and plasma DHA concentrations had a moderate correlation and a reasonable level of agreement.Dietary intakes of the n-6 and n-3 PUFA, as well as total fat, MUFA and SFA, were very similar to NDNS (1997) intake data on 4–10-year-old children. The amount in g/d or percentage of energy from main PUFA subtypes  were also comparable with those reported in other studies of PUFA intakes in paediatric populations in Westernised countries,14,15. However, the n-6:n-3 ratio (7·9:1) was generally lower than reported in these studies which could be due to the higher estimated n-3 PUFA intakes we observed . The low intakes of DHA and EPA observed in our study are also consistent with research in paediatric populations from other countries,14,15,18.In this study, the intakes of n-3 and n-6 PUFA intakes were very similar between the current study and the NDNS study of 4–10-year-olds. However, we found that fat spreads and cooking oils, and cereal products contributed most to n-6 PUFA intake, while in the NDNS study, fats used in vegetable and potato dishes were the main source. As expected, fish and seafood dishes were the most important sources of LC-PUFA, contributing to 59 % of total DHA intake, which was comparable with previous findings,14. According to the NDNS 2008–2012 rolling programme, white fish (including coated white fish) is the most common type of fish consumed in UK 6–11-year-olds (average intake is four times that of oily fish). Therefore, although white fish have much lower concentrations of EPA and DHA than oily fish because of its more frequent consumption, it formed the major source of LC-PUFA in these children . Oily/fatty fish were an important source of dietary LC-PUFA though, consistent with findings from other studies in children,16.The main food groups contributing to ,47. LA, the principal source of n-6, provided 5·1 % of TE in this cohort, also within the guidelines of ≥4 % of TE set by European Food Safety Authority. In terms of n-3 PUFA dietary recommendations, the UK advocates that it forms a minimum of 0·2 % of food energy, while the FAO/WHO set an acceptable distribution range of 0·5–2·0 % of TE. European Food Safety Authority recommends that ≥0·5 % of TE should come from the n-3 PUFA ALA. Therefore, the mean intakes of total n-3 PUFA (0·7 % of TE) and ALA (0·6 % of TE) in our study were within these dietary recommendations. However, the dietary intakes of the LC-PUFA in our study (85·4 mg/d) fell far below recommendations of 200–250 mg/d set by internationally recognised organisations,18,47. In fact, none of the children in our cohort reached this level of intake and most children consumed less than half. This is not surprising considering the recent findings from the NDNS, which reported that only 4·7 % of UK children met the minimum recommendations for fish intake and only 4·5 % met minimum recommendations for oily fish. Encouragingly, previous studies in children have shown that even eating a small amount of fish can significantly improve LC-PUFA levels compared with non-consumers.The mean daily intake of dietary PUFA in these 7-year-old children was in line with the Scientific Advisory Committee on Nutrition UK recommendation of 6·5 % of TEn-6:n-3 PUFA in our study (7·9:1) is higher than what is considered for optimal growth and long-term health, particularly cardiovascular health,4,49. This ratio is a reflection of the abundance of food sources of LA in modern Western diets,14, with regular use of fat spreads (margarines) and vegetable oils rich in LA (i.e. sunflower and maize oil) and their wide use in processed cereal-based products (baked goods and savoury and sweet snacks). In contrast, there are relatively fewer food sources high in n-3 PUFA. To improve the PUFA balance, a change in dietary habits is necessary, by increasing consumption of n-3 PUFA and/or decreasing consumption of n-6 PUFA. The advantage of decreasing n-6 PUFA intakes is that it potentiates the use of essential n-3 PUFA, since LA and AA compete for the same elongase and desaturase enzymes. A higher intake of n-3 and LC-PUFA can be achieved by increasing the consumption of foods containing DHA and EPA (mainly fish and seafood) and/or foods containing their precursor, ALA. Although findings from the NDNS rolling programme comparing intake data from 1997 to 2008–2009 in 4–10-year-olds indicate that there was an overall shift towards recommended dietary guidelines for fat intakes, including an increase in consumption of n-3 PUFA, these related to relatively small increases in absolute terms.The ratio of ,24,26,51 and paediatric populations,29,30,52. A study of 0–11-year-olds from the USA compared FFQ estimates with the PUFA content of erythrocyte membranes and reported a correlation of 0·16 (P < 0·001) for n-6 PUFA, 0·25 (P = 0·001) for n-3 PUFA and 0·38 (P < 0·001) for total marine PUFA, which is comparable to the correlations of these PUFA subtypes observed in our study. An Australian study in forty-seven healthy-weight children found moderate correlations between total dietary n-3 PUFA (r = 0·22) and EPA (r = 0·24) and their respective concentrations in erythrocyte membranes but no correlation with DHA. Two studies in children observed higher correlations than in this study between dietary and tissue PUFA for total n-6 and LA (r ranging from 0·3 to 0·4),28. The different correlations reported between studies could be partly due to variations in the type of biomarker medium, dietary assessment method, period between obtaining dietary intake and biomarker tissue, health status of study population and genetic and lifestyle factors.Our results showed weak-to-moderate correlations between dietary and plasma PUFA, consistent with results from previous studies comparing dietary PUFA intakes with tissue biomarkers in adultsThe overall weak-to-moderate correlations between dietary intakes of PUFA and their respective biomarkers observed in many studies, including ours, could be explained by the fact that tissue PUFA represent the interplay between dietary intakes, individual variation in absorption rates and metabolism. Metabolic processes and the complex interrelationships between different PUFA along the biosynthetic pathway of elongation and desaturation are a key reason why dietary intakes may not map directly onto plasma concentrations.. An experimental study using tracers in rats supports this and found that in addition to oxidation, eighteen-chain PUFA move out of circulating blood lipids quickly and are stored in adipose tissue. This could explain why blood eighteen-chain PUFA are not good indicators of dietary intake. In addition, the association between PUFA intakes and their biomarkers may differ for shorter- v. longer-chain PUFA. A systematic review of adult studies comparing FFQ estimated LC n-3 PUFA intake with plasma concentrations reported correlations in the range of 0·30–0·50 for DHA but only 0–0·28 for ALA. Several studies in children have also found that correlations between PUFA in erythrocyte membranes, serum or plasma were generally higher for the marine-origin n-3 PUFA,28,29,30. In our study, the correlations between the shorter-chain PUFA (LA and ALA) were generally weaker than the LC-PUFA. Shorter-chain PUFA may be less correlated with their tissue biomarkers because they are also converted into longer-chain PUFA, although this may only happen when concurrent intake of LC-PUFA is low. ALA was not associated with plasma EPA and DHA in our study though, which is consistent with the poor endogenous conversion rate of ALA to DHA and EPA . Consequently, tissue and circulating LC-PUFA are mainly a reflection of their direct consumption from foods. This could explain why we observed a moderate correlation and level of agreement (according to Cohen’s κ) between dietary and plasma DHA. Indeed, in adult populations with high fish intakes, such as Japan, correlations of up to 0·60–0·70 for EPA and DHA have been observed,57.The weak correlations between eighteen-carbon chain PUFA intakes and plasma levels are in line with research in humans showing that the eighteen-carbon chain PUFA are largely oxidised. Intervention studies have also demonstrated that high intakes of LA were associated with lower conversion of ALA to EPA in subjects on diets without fish,59.Our data showed some, although weak, evidence that dietary intakes of LA were associated with lower plasma concentrations of EPA. This is in line with the evidence indicating that higher concentration of LA inhibits the conversion of ALA to EPA. Inhibition occurs because the metabolic pathway involved in converting the PUFA precursors ALA and LA to their respective metabolites uses the same rate-limiting enzyme, delta-6 desaturase. The use of a parental-completed FFQ specially designed for this age group enabled us to capture habitual dietary intakes, which is particularly advantageous when collecting information on foods such as fish and seafood, which are typically eaten less frequently in this population. We also had a complete database on quantities of EPA and DHA in the foods consumed, and data on intakes of these nutrients are limited in paediatric populations from the UK. However, we did not calculate intake of docosapentaenoic acid or its concentration in plasma: recent findings suggest that docosapentaenoic acid could be just as important as EPA and DHA in terms of health benefits linked to LC-PUFA. Finally, the FFQ included five specific questions covering fish and seafood consumption that enabled us to assess the types of fish contributing to the LC-PUFA intake in these children.The strengths and weaknesses of the study should be considered when interpreting these results. The strengths include the large number of children with both dietary and biomarker data, making this one of the largest correlation studies of this type in children. The majority of studies validating dietary assessment tools in children in the UK have a sample size of <50. However, sample attrition during the 7-year follow-up is likely to have produced loss to follow-up bias, and it is probable that children with less healthy dietary patterns were under-represented which may in turn have influenced average PUFA intakes. However, the average PUFA intakes  and their main food sources reported in our study were very similar to the NDNS data on nationally representative UK 4–10-year-olds. Further attrition and subsequent bias occurred when obtaining a blood sample from these children as only 67·6 % of attendees at the research clinic agreed to this and these children had a lower BMI and energy intake and had a higher socio-economic status. Nevertheless, this should not have affected the correlation results, as these analyses were within subject. The use of parental-reported FFQ to assess children’s dietary intake would be subject to issues of reporting error and bias, as with all dietary survey methods to different extents. To minimise this, the analysis excluded children with implausible dietary intakes.In terms of study limitations, at birth, these children were relatively representative of the population in the area. The choice of medium for FA biomarker measurement is relevant because they reflect FA intakes over different time periods and so should ideally be time integrated with the dietary intake period being measured. Erythrocyte membranes reflect intake aggregated over approximately 4 months. However, two studies in paediatric populations that compared FFQ-estimated PUFA intakes with PUFA levels in erythrocyte membranes reported similar ranges of correlation coefficient to our study,30. In addition, eating habits have been shown to be reasonably stable during childhood, with moderate tracking levels. NDNS data on the time trends in n-6 and n-3 FA in UK 7–9-year-old children show that there are minimal differences in intakes over this period in childhood.A further limitation is that the FFQ, which was designed to assess habitual dietary intake, was completed approximately 8 months prior to the blood sample being obtained. However, plasma FA are an immediate biomarker which reflect intake over the past few days or meals. The storage time of the samples is also a potential limitation, due to oxidation of PUFA and deterioration of lipid classes over time. In our study, the samples were stored at −70°C for approximately 10 years before the plasma FA composition was analysed. However, plasma FA are considered to be relatively stable for up to 10 years with such ultracold storage,65. This also supports our choice of pool sample (plasma) in place of erythrocytes; although erythrocytes are less influenced by recent dietary intake, the FA composition is not completely stable during their 4-month lifespan, since the FA in the membranes can remodel with recent diet intake and the haem content of erythrocytes can cause PUFA oxidation.The difference in reference period between the FFQ and biomarker assessment could mean that the observed correlations were an underestimation of the true correlationsn-3-enriched foods are now more readily available). Food composition databases are limited in both the number of foods they contain and the frequency that they update food composition data. However, we supplemented the M&W food composition tables with up-to-date data from other sources in order to maximise the completeness of the PUFA composition of the foods covered in our FFQ.A final limitation is the food composition database used to estimate intakes from the FFQ data. The composition of foods and types of food available change with time  and seafood, but also from lean (red) meat, eggs and products nutritionally enriched with LC-PUFA. For children unable or reluctant to eat fish or seafood, then dietary changes that reduce foods high in LA  while increasing foods rich in ALA  can improve their n-3 FA status.In conclusion, the weak to moderate correlations between dietary and plasma LC-PUFA intakes and good level of agreement in cross-classiﬁcation analysis reflect the ability of the parental-completed FFQ to relatively rank the LC-PUFA intakes in this paediatric population, particularly for DHA. Our results highlight the need for public health initiatives to address the suboptimal ratio of


In follow-up analyses, 26 of the 33 novel regions replicate in East Asians, Europeans, or Africans, and one mouse homologous region influences craniofacial morphology in mice. The novel region in 1q32.3 shows introgression from Neanderthals and we find that the introgressed tract increases nasal height . Novel regions include candidate genes and genome regulatory elements previously implicated in craniofacial development, and show preferential transcription in cranial neural crest cells. The automated approach used here should simplify the collection of large study samples from across the world, facilitating a cosmopolitan characterization of the genetics of facial features.We report a genome-wide association study of facial features in >6000 Latin Americans based on automatic landmarking of 2D portraits and testing for association with inter-landmark distances. We detected significant associations (P-value <5 × 10 A genome-wide association study and fully automatic landmarking approach on frontal 2D face photographs of >6000 Latin Americans identifies novel genomic regions influencing facial features and implicates Neanderthal introgression in nasal shape. The genomic regions identified often overlap developmental genes, and have been shown to be enriched in regulatory elements active during craniofacial development17. These studies were initially performed in individuals of European descent11. Face GWASs are being gradually extended to the characterization populations of non-European ancestry16. The increasing characterization of non-Europeans is helping to draw a fuller picture of the genetic architecture of facial variation in humans and further our understanding of the evolution of human facial features.Genome-wide association studies (GWAS) of human facial features are contributing importantly to elucidating the genetic basis of variation in facial features in the general population1, to measurements based on manual landmarking of 2D photographs2, to semi-automatic analyses of 3D facial images15. These approaches vary greatly in cost, informativity, and ease of application. Although 3D images fully represent facial morphology, acquisition of such data requires specialized equipment, complicating their widespread application across the world. Although less informative than 3D imaging, standard 2D photographs have the potential to facilitate the collection of large, diverse study samples. However, manual landmarking of 2D photographs is a slow, labor-intensive task. This has fostered an interest in the application of fully automatic landmarking approaches. However, so far these have enjoyed limited success20. Most studies based on 2D photographs have therefore been based on entirely manual1 or, at times, semi-automatic landmarking9 .GWASs of facial variation have used a range of phenotyping approaches, from qualitative assessment of morphological features on 2D photographsHere we report a GWAS of facial features derived from a fully automatic landmarking of 2D frontal photographs from Latin Americans of mixed European, Native American and African ancestry. The association signals detected overlap with previous GWAS findings. In addition, we identify 33 novel signals. For most of the novel signals identified, we find evidence of statistical replication in European, East Asian, or African GWAS data, and one mouse homologous region influences craniofacial morphology in mice. One of the novel regions identified includes a tract of introgression from Neanderthals, which we associate with an increase in nasal height, consistent with the morphological differentiation between Neanderthals and modern humans.21. This cohort has been previously studied in GWASs of various physical appearance traits24. This includes two previous facial morphology GWASs based on 2D photographs: one mainly based on categorical (i.e. morphoscopic) phenotyping, and one based on manual landmarking of lateral (profile) photographs24. Individuals included in these studies were genotyped on Illumina’s OmniExpress chip  and characterized for a set of standard covariates 24.The 6486 individuals examined here are part of the CANDELA cohort, collected in five Latin American countrieshttps://www.faceplusplus.com) to automatically place 106 landmarks on frontal 2D photographs (i.e. portraits) from the CANDELA individuals  between 34 Face++ landmarks , landmarks 6-25 , and landmarks 8-12  (Supplementary Table p < 10−5), and for measures of lip thickness . Strongest correlation with European ancestry was seen for distances sensitive to nasion position , lip thickness , nasal root breadth  and nose wing breadth  (Supplementary Table 2. We estimated narrow-sense heritability (h2) based on the kinship matrix derived from SNP data30 and observe moderate values for most traits  correlation was detected for various ILDs with covariates  multiple testing correction procedure of Benjamini–Hochberg . Altogether, 42 genomic regions were significantly associated with at least one ILD and 148 distances were associated with at least one of these 42 genomic regions  filters (see Methods for details), we evaluated association for ILDs with up to 11,532,785 SNPs on up to 5988 individuals. We considered a 32. These data were processed as for the CANDELA sample. For Europeans and Africans, we extracted association P-values from published studies: a GWAS meta-analysis including data for 10,115 Europeans and 78 ILDs17 and a GWAS performed in 3631 Africans for 34 size and shape-related facial traits )33. When data for the index SNP of a region identified in the CANDELA sample was not available in the other study samples, we examined as proxies SNPs in LD with the index SNP in a region (r2 > =0.1). For six of the novel regions detected, no polymorphic SNPs across datasets were available, preventing evaluation of replication for these regions. We calculated a significance threshold for replication of 0.029 using Benjamini–Hochberg’s FDR procedure (accounting for 27 regions tested in four replication datasets). Altogether, 26/33 regions had association P-values <0.029 for at least one distance, in at least one of the replication datasets  , impacting on multiple aspects of mouse skull and mandible shape . The three associated distances are mainly sensitive to midface height. Interestingly, previous studies have reported Neanderthal introgression in 1q32.340. To evaluate the relationship between the association signal in 1q32.3 and Neanderthal introgression in the region we screened a 1 Mb window around the association signal for evidence of introgression in the CANDELA data41. Considering only introgression tracts >10 Kb long called at >99% confidence, we observe that Neanderthal introgression in 1q32.3 peaks in the region of strongest association seen in the GWAS  which is available in Neanderthal is Subspinale-Nasion . The equivalent distance, between Subnasal (landmark 18) and Nasion (landmark 12), was also measured in the CANDELA individuals (ILD D117). We thus tested for association between Neanderthal introgression in 1q32.3 and D117 and found it to be significant  having highly differentiated allele frequencies between Europeans and Native Americans . These 8 distances are sensitive to philtrum height , GO:0019827: stem cell population maintenance , GO:0051960: regulation of nervous system development , GO:0021700: developmental maturation , GO:0048562: embryonic organ morphogenesis , GO:0007162: negative regulation of cell adhesion , and GO:0032940: secretion by cell  , transcripts closest to the index SNPs are preferentially expressed in CNCCs, compared to other cell types, similar to what has been observed in previous analyses17  we identify here share features with findings from previous GWAS of facial morphology.GWAS of facial features have identified dozens of associated genome regionsATF3 gene  gene, which studies in mice have implicated in palate fusion during development51. (iii) The newly associated 8p11.21 region includes a cluster of disintegrin and metalloproteinase (ADAM) domain genes , has been suggestively associated with non-syndromic cleft lip/palate43. (iv) Associated SNP on 10p11.1 overlap a cluster of Zinc Finger proteins genes  The mouse analyses performed here are consistent with the novel association we detect on human 22q12.1. In humans, maximum association is seen for SNPs intronic to the SEZ6L gene  are the genes closest to the index SNPs and, in our RNAseq data analysis, we observe that two of these genes (MYLK and SEZ6L) are preferentially transcribed in CNCC cells . Analysis of skulls has long shown that facial morphology differs markedly between Neanderthals and modern humans with the mid-face, particularly the nasal cavity, showing major differences60. This includes markedly taller noses in Neanderthals than in modern humans. Furthermore, it has long been speculated that nose morphology  has been the subject of natural selection, particularly as an adaptation to environmental temperature and humidity63. Further genetic work, including future analyses of additional ancient DNA samples, could help shed light on this question.Genetic determinants of variation in facial features in contemporary human populations are also likely to have played a role during the evolution of facial morphology. We previously identified a region in 1p12 in which a tract introgressed from archaic humans (Denisovans) impacts on lip thickness. That chromosomal region had previously been shown to be associated with body fat distribution10, recent efforts have increasingly sought to examine non-Europeans32. Populations with admixed continental ancestry, such as Latin Americans, offer challenges and opportunities for such studies. In these populations, optimal correction for population stratification, considering both global and local genomic ancestry, is a challenging analytical problem for which an all-round solution is yet to be developed66. Use of genetic PCs and local ancestry correction approaches to deal with population stratification should therefore be undertaken with caution. Nevertheless, the extensive genetic and phenotypic diversity of Latin Americans is enabling GWASs that have led to important insights into the genetics of physical appearance24. This is illustrated here by the novel instance of archaic introgression detected in 1q32.3: the introgressed tract has a high frequency in Native Americans but is essentially absent in Europeans (Table Although the earliest (and largest) studies on the genetics of facial variation have been carried out in people of Europeans ancestryN = 1407; Brazil, N = 674; Chile, N = 2003; Mexico, N = 1203 and Peru, N = 1199) individuals from the Consortium for the Analysis of the Diversity and Evolution of Latin America (CANDELA consortium) were included in frontal photographs collection. CANDELA consortium (https://www.ucl.ac.uk/biosciences/gee/candela/) has been used to study physical appearance in Latin American for multiple studies, and details could be seen in Ruiz-Linares et al.21. Ethical approval was obtained from the Universidad Nacional Autónoma de México (México), Universidad de Antioquia (Colombia), Universidad Perúana Cayetano Heredia (Perú), Universidad de Tarapacá (Chile), Universidade Federal do Rio Grande do Sul (Brazil) and University College London (UK). All participants provided written informed consent. The participate included in Supplementary Table Discovery sample: 6486 .32. This sample stems from the National Survey of Physical Traits (NSPT) cohort (n = 2628) and the Taizhou Longitudinal Study (TZL) cohort (n = 2670)67. The Taizhou Longitudinal Study (TZL) was approved by the Ethics Committee of Human Genetic Resources at the Shanghai Institute of Life Sciences, Chinese Academy of Sciences (ER-SIBS-261410). The National Survey of Physical Traits (NSPT) is the sub-project of The National Science & Technology Basic Research Project which was approved by the Ethics Committee of Human Genetic Resources of School of Life Sciences, Fudan University, Shanghai (14117). All participants provided written informed consent.The Chinese sample includes 5298 individuals17. This sample includes 10,115 individuals of European ancestry recruited in three countries . The summary statistics are publicly available at 10.6084/m9.figshare.1029839668.The European replication sample is the discovery cohort examined in the GWAS of Xiong et al.33, and a SNP GWAS of Cole et al.5. This sample contains 3631 Bantu African individuals aged from 3 to 21 from the Mwanza region of Tanzania. The summary statistics are available at https://github.com/meganmichelle/CNV_FaceShape and https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000622.v1.p1.The African cohort is the discovery cohort examined in a CNV GWAS of Null et al.24. Briefly, a blood sample was collected from each volunteer and DNA extracted following standard laboratory procedures. DNA samples were genotyped on the Illumina HumanOmniExpress chip including 730,525 SNPs. PLINK v1.90 was used for QC. Individuals and SNPs with >5% missing genotypes, SNPs with <1% minor allele frequency, and individuals who failed the X- or Y- chromosome sex checks were excluded. After these QC filters, ~650,000 SNPs and 5500 individuals were retained for further analyses. Human genome reference assembly GrCh37/hg19 was used. SHAPEIT269 was used for pre-phasing the chip genotype data, and IMPUTE270 was then used to impute variants using the 1000 Genomes Phase 3 reference panel. Imputation led to 11,532,785 SNPs being available for association testing. Markers that are monomorphic in 1000 Genomes Latin American samples were excluded from imputation. Chip genotyped SNPs having a low concordance value (<0.7) or a large gap between info and concordance values (info_type0 – concord_type0 > 0.1), which might be indicators of poor genotyping, were also removed, both from the imputed and chip dataset. Imputed SNPs with imputation quality scores <0.4 were excluded. The IMPUTE2 genotype probabilities at each locus were converted into best-guess genotypes using PLINK (at the default setting of <0.1 uncertainty). SNPs with >5% uncalled genotypes or minor allele frequency <1% were excluded. On the basis of genome-wide SNP data, we estimated European, Native American and sub-Saharan African ancestry proportions for each CANDELA individual (European and Native American ancestries being strongly negatively correlated21).The genotype data examined here are those analyzed in previous GWAS of the CANDELA sample21. The photographs were anonymized for confidentiality, and stored on a secure cluster, where an API script available from Face++ (https://www.faceplusplus.com), implementing a pre-trained deep learning model was run. Face++ placed 106 landmarks on each photograph  are on important locations such as the end of a contour, which would allow us to capture facial features that we are interested in, including face width and eyebrow size  fitted with a Nikkor 50 mm fixed-focal-length lens1, or included in another commonly used automatic face landmarks detection protocol Dlib26 on the same 1610 photographs  to test the correlation of ILDs with gender and Spearman’s correlation coefficient (ρ) to test the correlation of ILDs with age, BMI, genetic ancestry and head angles.We used point biserial correlation coefficient  was retained. To estimate the narrow-sense heritability (h2) for each trait, we computed a genomic relationship matrix (GRM) combining genotype data for of all individuals for which data for at least one trait was available. The GRM was calculated using LDAK530 with default parameters. For each trait, h2 was estimated by fitting an additive linear model with a random effect term whose variance was obtained from the GRM, and added age, sex, BMI, 6 genetic PCs and head angles as covariates.Relatedness between samples was estimated using KING-robust75. Reference parental populations included in the ADMIXTURE analyses consisted of Africans  and Europeans (107 Iberian Population in Spain) from 1000 Genomes Phase 3 and 125 selected Native Americans76.An LD-pruned set of 93,328 autosomal SNPs was used to estimate European, African and Native American ancestry proportions using supervised runs of ADMIXTURE77 of all 301 ILDs are available via figshare 10.6084/m9.figshare.1972891678.GWAS was conducted on the 301 ILD phenotypes using PLINK v1.9. Sex, age, BMI, three head angles  and the first 6 genetic PCs were included as covariates. The Q-Q plots for all traits showed no sign of inflation, and the genomic inflation factor (lambda) of all traits was close to 1 with the maximum value of 1.074 and median value of 1.048, which indicate that appropriately controls for population stratification had been taken care of. Q-Q plots and Manhattan plots79 to correct for M = 1,342,638,980 tests, the adjusted genome-wide significance threshold was 1.823 × 10−6. An alternative FDR approach, used in Xiong et al.17 and developed in Li et al.79, is to calculate the effective number of independent tests (Meff). For the ILD phenotypes, an eigenvalue decomposition of their correlation matrix was used to calculate the effective number of independent phenotypes, 31.53. For the SNPs, LD pruning was used on the imputed genotypes to calculate the number of effective number of independent SNPs, 1,062,091. Therefore the effective number of independent statistical tests was their product, Meff = 33,484,596. With this approach, the adjusted genome-wide significance threshold was very similar, 1.825 × 10−6. Both are more lenient than the commonly used GWAS genome-wide significance threshold (5 × 10−8). Therefore, we continued to use the conventional GWAS threshold 5 × 10−8 as the genome-wide significance threshold, as this will satisfy the conventional threshold as well as the FDR criteria. However, the genomic regions whose P-value are in between the FDR threshold (1.823 × 10−6) and the commonly used GWAS threshold (5 × 10−8) were presented in Supplementary Table Multiple testing in the primary GWASs was corrected by estimating the FDR threshold with the Benjamini–Hochberg procedure. The FDR significance threshold was calculated to adjust for the total number of tests (M), which is a product of the total number of SNPs and the total number of phenotypes. Using the classical BH-FDR method−8 were merged with their neighboring signals.To group SNP-based GWA results across all analyses based on linkage disequilibrium (LD) between SNPs, we conducted clumping in PLINK v1.9 on the combined output file of all GWA analyses. We used 0.1 for LD threshold, and 1000 Kb for the physical distance threshold, which in total resulted in 62 clumps. To further determine if each clump is independent, we conducted conditioned analyses on the signals physically close to each other. All covariates used in the original GWA analysis were also added in the conditional GWAS. All signals with a conditioned P-value greater than 5 × 10P-value obtained was above the suggestive significant threshold (1 × 10−5), this signal would be regarded as a reported signal, and conversely, it would be regarded as a new signal. Details of the results from conditional analyses could be seen in Supplementary Table Conditional GWAS was also carried out to test if a signal detected here had been reported previously. We firstly picked out signals that fall on the chromosome bands that have been reported. Amongst 42 regions we detected, 16 fell on an entirely new chromosome band that was not reported to be associated with facial features. We then have conducted the conditional analysis on totally 93 SNPs across 26 regions. We gathered all reported SNPs in each chromosome band and added those reported SNPs into the regression models of corresponding SNPs of the same chromosome band in our results. If In the replication analysis, 76 P-values were available for 27 novel associated regions across 4 separate datasets. After correction of multiple testing with BH-FDR, the combined significance threshold in the replication cohorts was 0.0293.i.The primary GWAS model described above, implemented using PLINK and including the SNP genotype and genetic PCs (representing whole-genome ancestry);ii.The same GWAS model using PLINK as in (i) but without genetic PCs, in order to assess the extent of inflation when no adjustment for population structure is included in the model;iii.80, which uses a GRM  instead of genetic PCs;A mixed-effect regression model implemented in GCTAiv.82, using both a relatedness matrix (estimated using KING-robust to model recent kinship), and genetic PCs to model population substructure;An extension of the GCTA mixed effects model, implemented in GENESISv.83 (TRACTOR) which instead of using the SNP genotypes directly, uses the SNP coding on three different local ancestry backgrounds. We examined three models: one using only genetic PCs , one using only local ancestry (obtained here by RFMix), and one with both local ancestry and genetic PCs as covariates.A model proposed by Atkinson et al.vi.64), which for each SNP genotype uses the local ancestry estimates at that location as covariates. We tested two models: one using both local ancestry and genetic PCs as covariates, and one only using local ancestry but not PCs;An alternative to TRACTOR called SNP1 . Supplementary Fig. We first contrasted GWAS results for the 148 facial distances showing significant association in the primary PLINK analyses. We ran GCTA and GENESIS on the exact same imputed genome-wide dataset used in the PLINK analyses. For SNP1 and TRACTOR analyses were performed only on the chip data, as local ancestry estimates are only available for genotyped (not imputed) data. To compare results across all analysis approaches and models, the genomic inflation factor (lambda) was calculated for each GWAS using the chip SNP data  using GCTA, GENESIS, SNP1, and TRACTOR . In all six cases, the global ancestry-corrected models  have the highest power, followed by SNP1, while TRACTOR has the lowest power. This is particularly noticeable for EDAR and SLC24A5, two instances in which the index SNPs have fully differentiated allele frequencies between populations participating in the admixture . In these two cases, the local Native American ancestry component is nearly identical to the SNP genotype, leading to SNP1 and TRACTOR suffering from high collinearity and resulting in a nearly total loss of power for these two approaches  for 42 index SNPs identified in the primary PLINK analyses (across the 148 associated distances), with the values obtained for these SNPs using GCTA and GENESIS. These three approaches produce very similar results, a scatterplot of -log showing points that lie close to the diagonal  produce identical results to using a mixed-effect regression model, which incorporate a genetic relatedness matrix instead of genetic PCs (as implemented in EMMAX85 or FastLMM24). Regarding local ancestry correction approaches, other than collinearity, the drop of power probably stems from effect sizes generally not being sufficiently different between ancestry components to reach genome-wide significance in each ancestry component65. Since TRACTOR has three degrees of freedom, with three ancestry-specific SNP components, this approach can be more powerful only when there is substantial heterogeneity in the effect size of SNP across ancestry components66. In our case, the trade-off between the scarcity of variants with ancestry-specific effect sizes and that of variants with effect size shared across ancestries appears to be a handicap for TRACTOR.Altogether, the analyses above agree with theoretical and simulation studies34. Coordinates for 44 landmarks (17 pairs of symmetric landmarks and 10 landmarks on the median plane), along with genotypes at 70k SNPs for 692 mice were kindly provided by Luisa Pallares. We performed a full generalized Procrustes analysis with object symmetry91, and the phenotypic variation was modeled on the basis of the 67 non-null PC. We applied a multivariate mixed model not used in the original analysis of these data34. The original mouse GWAS was done on each shape PC34. However, this approach has maximum power when an allele effect is sufficiently strong to structure the overall shape variation. With geometric morphometrics on skull shape, this is unlikely and a multivariate GWAS is preferable. Such an approach is nevertheless computationally challenging when a linear mixed model (mvLMM) based on the genomic relatedness matrix is used on a very high dimensional trait such as skull shape (here 67 non-null dimensions). We therefore approximated this mvLMM by modeling the covariance matrices of this linear mixed model with two blocks (including skull centroid size as covariate). The first block models the genetic and environmental covariances of the first 10 PCs  altogether, while only the variances for the next 57 traits were modeled as the second block (i.e. the covariances among these PCs as well as with the other block were set to 0). This approach gains from the modeling of the genetic correlations between the main PCs while maintaining a lower dimensionality cost than in the full multivariate model. Association between a SNP and craniofacial shape was tested based on Pillai trace statistics obtained from the multivariate regression between the corrected allele dosage and corrected PC scores. A FDR was computed based on 100 permutations of corrected PC scores following the approach of Nicod et al.92 and used to identify SNPs exceeding a FDR threshold of 5%.We reanalyzed genome-wide SNP and craniofacial data obtained for a published GWAS in outbred miceATF3 gene in 1q32.3. Imputed genotypes of all samples for 4311 SNPs in this region were first phased using SHAPEIT4 (with default parameters). The haplotypes obtained were re-phased using low-density chip-genotype data previously phased using RFMix (v1)93. This two-step-phasing is expected to be more accurate and also aligns phases with the local ancestry estimates obtained by RFMix, hence allowing to determine on which ancestral background the archaic tracts are found. The rephased data was merged with data for “Altai” Neanderthal and the 108 YRI samples from 1000GP3  and then filtered. In brief, variants from the 1 Mb window were retained if they: (i) had a read depth ≥20 in “Altai” Neanderthal, (ii) survived the PASS filter in both the “Altai” Neanderthal and 1000GP3 VCFs, (iii) the same ancestral and derived alleles were reported in the two VCFs, and (iv) the ancestral allele is present in our data. This filtering resulted in 3231 SNPs being retained. The introgression scan, on the filtered data, was carried out using the hidden-Markov model implemented in admixtureHMM41, considering only tracts called with >99% confidence and that were >10 Kb. This identified 798 introgression tracts, with an average length of 127 Kb.These analyses focused on a 1 Mb window around the −4.We performed association testing through (archaic) admixture mapping, that is, we first recoded genotypes based on the number of archaic alleles  and merged consecutive SNPs with a similar distribution of genotypes across individuals, allowing a maximum of 1% genotype change across individuals from one SNP to the next. Filtering for a minimum archaic tract frequency of 1%, led to a total of 103 introgressed segments being retained. We then tested for phenotypic association using the same linear model as for the GWAS. The Benjamini–Hochberg’s FDR significance threshold equals 4.9 × 1094). We extracted nasal height from the measurements obtained by Weaver and Stringer95 on 10 Neanderthal specimens . For comparison with modern humans, we extracted nasal height measured in skulls from 484 Africans, 317 Europeans and 389 Native Americans , from Howells’ online database (http://web.utk.edu/~auerbach/HOWL.htm)94. To illustrate the nasal height difference between modern human and Neanderthal skulls, we compared a Native American and the Amud 1 Neanderthal (41Kya). The 3D image of the Native American skull was obtained from the collection of the División de Antropología, Museo de La Plata, Argentina . The Amud 1 3D image was obtained from the MorphoSource repository (www.morphosource.org): Darwin Core triplet: du:ea:CCC08 Homo neanderthalensis; ID Media 000005749: Cranium [Mesh] [Etc]. MorphoSource Archival Resource Key (ARK) identifier: ark:/87602/m4/M5749.Distance D117  measured in the CANDELA individuals corresponds to the cranial distance measured between nasion and subspinale landmarks  and a pre-defined lead SNP list obtained after clumping in Plink v1.9 were loaded to FUMA44. SNP2GENE was processed to identify independent SNPs (r2 < 0.6) and candidate SNPs. Candidate SNPs are the SNPs in LD of one of the independent significant SNPs, which includes non-GWAS tagged SNPs extracted from 1000 genomes reference panel. Implemented tool ANNOVAR was used to annotate the functional consequences on gene function on the independent SNPs and candidate SNPs. The website indicates that ANNOVAR uses all annotated transcripts in Gencode collection lifted up to hg19, and has its own prioritization criteria to report the most deleterious function. Only prioritized annotations are used for those SNPs.A subset of GWAS summary statistics including only significant SNPs (http://metascape.org/) to carry out a GO analysis96 of genes nearest to the index SNPs of the novel associated regions  .We used Metascape (Further information on research design is available in the Peer Review FileSupplementary MaterialDescription of Additional Supplementary FilesSuppementary DataSupplementary MovieReporting Summary


Indeed, it is still difficult to infer the inheritance of the maternal allele from cell-free DNA (cfDNA) analysis. Based on the study of maternal haplotype imbalance in cfDNA, relative haplotype dosage (RHDO) was developed to address this challenge. Although RHDO has been shown to be reliable, robust control of statistical error and explicit delineation of critical parameters for assessing the quality of the analysis have not been fully addressed. We present here a universal and adaptable enhanced-RHDO (eRHDO) procedure through an automated bioinformatics pipeline with a didactic visualization of the results, aiming to be applied for any SGD-NIPD in routine care. A training cohort of 43 families carrying CFTR, NF1, DMD, or F8 mutations allowed the characterization and optimal setting of several adjustable data variables, such as minimum sequencing depth, type 1 and type 2 statistical errors, as well as the quality assessment of intermediate steps and final results by block score and concordance score. Validation was successfully performed on a test cohort of 56 pregnancies. Finally, computer simulations were used to estimate the effect of fetal-fraction, sequencing depth and number of informative SNPs on the quality of results. Our workflow proved to be robust, as we obtained conclusive and correctly inferred fetal genotypes in 94.9% of cases, with no false-negative or false-positive results. By standardizing data generation and analysis, we fully describe a turnkey protocol for laboratories wishing to offer eRHDO-based non-invasive prenatal diagnosis for single-gene disorders as an alternative to conventional prenatal diagnosis.Non-invasive prenatal diagnosis of single-gene disorders (SGD-NIPD) has been widely accepted, but is mostly limited to the exclusion of either paternal or  Howede novo variants is based on a qualitative presence/absence approach, most workflows described to date for maternal inheritance determination rely on the fine quantification of each maternal haplotype, called relative haplotype dosage analysis (RHDO), originally described by Lo et al. [While the detection of paternally inherited or o et al. . In thiso et al. –21. Howeo et al. –24.enhanced-RHDO (eRHDO), where we explain in detail our bioinformatics and statistical analyses with precise risk control and present a straightforward and comprehensive result visualization. Using computer simulations, we discuss the impact of the different biological and analytical parameters on the quality of the result, and propose thresholds and objective quality scores for easy implementation in a diagnostic laboratory.In this report, we describe our RHDO-based NIPD workflow, which we call CFTR gene), neurofibromatosis type 1 (NF1 gene), Duchenne/Becker muscular dystrophy (DMD gene) or hemophilia (F8 and F9 genes) were recruited nationwide through the DANNI and NID studies, which were ethically approved by the French "Comité Consultatif sur le Traitement de l’Information en matière de Recherche dans le domaine de la Santé” (ref. 13.386) and the "Comité de Protection des Personnes” (ref. 2014-janvier-13465 and 29BRC18.0055). The French law regulating PND and genetic testing requires approval of the indication for referral by the local ethics committee and written informed consent from both parents. All data were fully anonymized. We divided our cohort into two groups. The first group, called "training cohort", included families for whom only parental, fetal and cfDNA were available, i.e. parental haplotypes were determined using fetal information from the current pregnancy. Blood samples from women in this group were initially used to validate the efficiency, accuracy and multiplexing capacity of our method. When genomic DNA (gDNA) from a first child or close relative was available, pregnant women were included in the “test cohort”. For these patients, we used the proband’s gDNA to identify each parental haplotypes, allowing these families to be tested in a real-world setting.Pregnant women known to be carriers of cystic fibrosis , ii/ fetal fraction assessment , iii/ qualitative paternal haplotype transmission determination , and iv/ quantitative maternal haplotype transmission determination . After technical validation of the result, biological interpretation and medical conclusion can be performed.The SGD-NIPD workflow is shown in Using simulated cfDNA sequencing data and data from the training cohort, we defined adjustable data variables, namely consideration of thresholds used to discriminate a heterozygous position in gDNA and minimum sequencing depth for SNP calling in gDNA (NDP) and cfDNA (PDP). Minimum interSNP distance for eRHDO analysis, and Type I and Type II error risks associated with SPRT haplotype block delineation were defined as output settings. We then tested different combinations of input and output settings and objectively examined their impact on the quality scores in order to select the best combination of input and output settings for test cohort exploration. Finally, the diagnostic performance of our noninvasive test was determined from the test cohort analysis.pc), and the proportion of positions where the SPRT remained non-conclusive (nc) are reported. SNPs were arbitrarily colored in green for the wild-type-linked haplotype, in red for the mutant-linked haplotype, and in gray for inconclusive results. This entire analysis pipeline was developed and refined using our training cohort. Its reliability and robustness, as well as the appropriateness of the chosen thresholds were tested on our test cohort. In this research study, all results were compared with the fetal genotype obtained on invasive sampling.Details of the bioinformatics pipeline for sequencing data analysis are provided in We implemented quality control tags as well as two eRHDO-specific quality scores, which we named concordance score Sc is defined as the difference between the concordance proportion between forward and reverse analyses pc and the proportion of SNPs that remained inconclusive nc. To strongly discriminate between misclassified haplotype blocks (low pc) and low-quality results (high nc), which would reflect a low quality result to be interpreted with caution, Sc is calculated as follows: Sc = −log100(1 − pc) − nc. A Sc closer to 1 reflects a more robust result.The concordance score The block score All statistical analyses were performed using R. To measure the effect of the settings on the quality of the results, we performed Wilcoxon tests and Friedman tests for variables with two (heterozygous position in gDNA) or more than two groups (minimum sequencing depth and minimum interSNP distance). In addition, we presented the associations using box plots.The effect of all quality influencing factors  and their combinations on the quality of the results was quantified using Sobol sensitivity indices, ranging from 0 (minor effect) and 100 (major effect). P-values for ANOVA tests were also calculated to verify their significance. All p-values were adjusted for multiple comparisons using the Bonferroni correction.The training cohort included 43 pregnancies, divided into 27 pregnancies at 25% risk for cystic fibrosis, 9 pregnancies at 50% risk for maternal transmission of neurofibromatosis type 1, and 3 and 4 pregnancies with a male fetus at 50% risk for Duchenne/Becker muscular dystrophy and hemophilia, respectively. Gestational age at sampling ranged from 6 to 29 weeks of gestation .The test cohort included 56 pregnancies from 45 families, divided into 25 pregnancies at 25% risk of cystic fibrosis , 11 pregnancies with a 50% risk of maternal transmission of neurofibromatosis type 1 , and 20 pregnancies with a male fetus with a 50% risk of Duchenne/Becker muscular dystrophy . Gestational age at sampling ranged from 6 to 17 weeks of gestation .Patient information is summarized in To refine our pipeline, we tested the impact of several parameters on result quality using data from the training cohort.We investigated two alternatives for defining heterozygous SNPs in gDNA. In the extensive definition, heterozygous SNPs in gDNA are defined by an allelic frequency of the alternative allele (AF) comprised between 0.15 and 0.85, whereas homozygous positions in gDNA are defined by an AF <0.15 or >0.85. In contrast, the restrictive definition corresponds to heterozygous SNPs in gDNA defined by an AF comprised between 0.35 and 0.65, while the AF of homozygous positions in gDNA remains unchanged at <0.15 and >0.85. An extensive definition of a heterozygous position could retain more informative positions than the restrictive definition but is likely to introduce SNP categorization errors due to misspecification of SNP positions associated with biased allele frequency. We investigated the effect of extensive versus restrictive definition of heterozygous SNPs in gDNA on the results by examining their impact on both scores . As expe results . Thus, wp-value for Friedman test of 4.8 × e−24; Since a lower minimum sequencing depth threshold may retain information at more positions but is likely to be less accurate in determining allelic ratios, we explored different minimum thresholds for sequencing depth for our gDNA and cfDNA analyses, denoted by gDNA sequencing depth (NDP) and cfDNA minimum sequencing depth (PDP), tested at 8x, 15x and 30x, and 15x, 30x, 45x and 60x, respectively. We objectively examined their effect on gher NDP . Similar NDP  to minimize the risk of linkage between two SNPs and a resulting bias that would hinder allelic dosage analysis, but may also discard positions that would be informative , 19. To analyzed . To maxiWhen implementing the RHDO method, the statistical risks associated with the SPRT control the proportion of tolerated misclassification errors, and thus have a major impact on both iated Sb , but thed Sb  can lead to erroneous determination of the fetal fraction, lower confidence in paternal inheritance, or hinder the determination of maternal haplotype ratios. Therefore, we return an evaluation of the proportion of ADO using SNP1 , as the proportion of fetal specific B allele not detected in maternal plasma . Although this parameter does not block subsequent SGD-NIPD analyses, a significant ADO (>20% of SNP1 positions) should lead to a cautious interpretation of the result.The sequencing error rate for each family was estimated using SNP2 density plots, corresponding to positions where both parents are homozygous for the same allele: the fetus is expected to be homozygous as well and any other allele found at the locus can be considered a sequencing error. Again, a significant sequencing error rate associated with one or more samples (>2%) should result in a cautious interpretation of the SGD-NIPD result.f = 2p/(p + q) where p is the number of sequenced reads from the fetal-specific allele and q is the number of sequenced reads from the maternal and fetal common allele.The fetal fraction was estimated without prior knowledge of the SNP category, using the minor allele frequency distribution (MAF). In its density plot, the first peak is expected to correspond to the frequency of the fetal-specific allele at positions where the fetus is heterozygous in a homozygous maternal background, allowing the fetal fraction to be inferred from twice this haploid frequency. Estimation using the SNP1 distribution, where both parents are homozygous for different alleles, was retained for comparison purposes. The fractional fetal DNA concentration is evaluated using the formula Patient informations and eRHDO-based SGD-NIPD results are shown in The sequencing error rate was consistently low across our families, ranging from 0.3% to 1.4% , consistent with the sequencing error profile of the platform . AlthougThe fetal fraction estimated from the distribution of all sequenced positions , as described in supplemental data, ranged from 2.9 to 19.3% (mean 9.3%). Three major discrepancies were found between MAF-based determination and SNP1-based determination, related to an abnormal distribution of the fetal-specific SNP1 allele frequency in maternal plasma caused by major ADO in these three samples .An example of our result visualization is shown in Paternal inheritance was determined only in families at risk for recessive cystic fibrosis (CF). All results were conclusive and concordant with the fetal genotype obtained by invasive sampling except one. In family 2_12, a recombination occurred close to the paternal variant location and the transmitted paternal haplotype at this position could not be inferred. Although the mother was a carrier of a different pathogenic variant, because this position was not targeted by our capture panel, the paternal variant could not be qualitatively detected by direct visualization of the paternal-specific allele on the bam sequence files.CFTR locus and the sample returned with a relatively low fetal fraction (5.9%).Considering maternal inheritance, 59 analyses were performed on 56 samples, as one family (family 2_26) was included in our study three times during three consecutive pregnancies and the cfDNA of one given pregnancy was analyzed twice using either PND’s gDNA for parental haplotype inference. 56/59 samples were conclusive (94.9%) and concordant with the fetal genotype obtained by invasive sampling. Of these, 43/59 results (72.9%) were conclusive with high quality results. All of these high quality results had a than 0.7 . 13/59  reflects that variations in the associated parameter will lead to small variations in the results, while a high sensitivity index (close to 100) implies large changes. Sensitivity analysis also provides an opportunity to explore the interaction effects of parameters on the model. Finally, in addition to the calculation of Sobol indices, we finally performed ANalysis Of VAriance (ANOVA) tests to identify the statistically significant effects of the parameters on both outcomes. The Sobol indices of fetal fraction, number of SNPs, sequencing depth, their combinations of them and the corresponding ANOVA These further analyses confirm the strong effect of the fetal fraction on both Since the initial description of RHDO, no practical recommendations or guidelines have been proposed for data generation and analysis, fetal fraction assessment, quality thresholds, and interpretation . In thisSince our goal is to provide the most direct computational analysis, we tried to consider every situation that might arise in genetic counseling in our workflow construction. We detailed each SNP category, taking into account the relationship of the proband to the couple (previous child or close relative) as well as his status with respect to the parental variants (affected/carrier or unaffected). This allows haplotype phasing and decision threshold calculations to be performed automatically, without the need to customize the informatics analysis pipeline for specific situations.A key point of our approach is that our workflow is designed to allow users to adjust several settings at different steps of the process, according to their needs. The study of the training cohort, as well as simulated data analysis, allowed different values for these variables to be tested to objectively examine their impact on the quality scores. The extensive or restrictive thresholds used to specify a heterozygous position in gDNA, the minimum sequencing depth of cfDNA, the minimum interSNP distance, and the statistical risks associated with SPRT were investigated and adjusted.Quality control points have been implemented at all stages of the workflow, to minimize the risk of error, but also to allow users to assess the robustness and confidence they can have in their results. During SNPs categorization, we added two "WARNING” categories for uninformative and impossible genotype combinations. The former can help expand the range of family members that can be tested, while the latter will be useful in identifying the potential causes of an unusual result. For example, too many SNPs excluded from the analysis because of uninformative combinations may explain an inconclusive result on qualitatively correct experimental data that might reflect the analysis of a consanguineous parental couple. An overrepresentation of impossible combinations may indicate an error in the input data for the sequencing analysis, a sample swap, or contamination during the technical steps.Because ADO can interfere with the analysis or even cause a false result, we added an assessment of the mean ADO rate based on the SNP1 analysis to the output data. Finally, the sequencing error rate density was estimated for each family using SNP2, corresponding to positions where both parents are homozygous for the same allele. A significant ADO or sequencing error rate should lead to a cautious interpretation of the result.The main quality-influencing factors are the fetal fraction, the number of SNP4 tested and the depth of cfDNA sequencing, which affects the number of events tested. Computer simulations allowed the identification of the fetal fraction as the most dominant of these factors. Since the number of SNP4 depends on the combination of parental genotypes and the fetal fraction is biologically determined in a given cfDNA sample, we recommend that sequencing conditions should be optimized in order to obtain sufficient cfDNA sequencing depth, higher than 100x in this study.Because the fetal fraction must be accurately assessed, we chose to use the minor allele frequency distribution of all the SNPs targeted in the capture panel, rather than relying on prior knowledge of their category, as is done for SNP1-based assessment. By increasing the number of events, we hope to obtain a more accurate estimate of the fetal fraction than the SNP1-based evaluation.Paternal inheritance determination relies on the qualitative detection of fetal-specific SNPs that are present at a very low allelic fractions in maternal plasma. We introduced a minimum allelic fraction threshold for considering a SNP3 as significantly detected, depending on the sequencing error rate and the fetal fraction of the sample, to discriminate between background noise and SNPs that were actually transmitted to the fetus. We also divided the analysis into "non-at-risk haplotype detected" and "at-risk haplotype detected" and required concordance between these two analyses.Finally, we designed our output to be as simple and unambiguous as possible, while retaining as much information as possible. To this end, we included a graphical visualization that summarizes the input data, reports the result with a two-color code, and includes several quality parameters to facilitate biological interpretation. To account for the risk of misclassification due to an undetected recombination event, we plotted the distance between two neighboring SNPs included in the analysis as a black curve in this final visualization, as the risk of recombination between two loci increases with genomic distance , 28.Our workflow proved to be very robust, as we obtained 94.9% conclusive and correctly inferred fetal genotypes in our real-life-like cohort, with no false negatives or false positives (56/59 conclusive and correctly inferred cases + 3/59 inconclusive cases). Rather, our quality controls proved to be stringent enough that low quality analyses returned inconclusive results, mostly in the situation of an insufficient number of tested events, either because of a low fetal fraction, low sequencing depth, or because of a small number of informative SNPs in the families. Our objective quality scores consistently supported our biological interpretation, being higher than 0.7 for high-quality analyses and lower than 0.55 for low-quality analyses. However, we chose not to set a threshold for analysis, as we developed these quality scores to support interpretation rather than as strict quality criteria. In fact, they refer only to the quality of the haplotype identification in maternal plasma and do not take into account the position of the variant along the target locus. They could therefore be close to 1 in a family where no SNPs 5’ or 3’ of the variant can be identified, a situation in which the maternal haplotype at the variant’s locus cannot be determined with certainty because a recombination event cannot be excluded. Similarly, if a recombination event is identified near the parental variant position, the analysis may be of overall high overall quality, but the interpretation of the SGD-NIPD should be inconclusive, regardless of the quality scores.This recombination risk is a well-described limitation of the MPS-based NIPD. Because it relies on an indirect approach, the occurrence of a recombination event close to the parental variant position may not allow to conclude . While tde novo, or if no other family member is available for testing. While de novo haplotyping methods have been reported in the context of NIPD [Another major limitation of haplotype-based SGD-NIPD is the need for genotype information from the proband for haplotype phasing. Care must be taken in the selection of this proband. In some cases, one of the parents and their unaffected sibling may not share a haplotype. Similarly, when parents carry the same variant in an autosomal recessive disorder, the parental origin of the variant in a heterozygous first child will most likely be unknown, unless an indirect technique has been previously performed in these families. The parental haplotypes will be correctly phased but identification of the at-risk and non-at-risk haplotypes will not be possible. Although we have anticipated most situations, this workflow cannot be offered to a family in which a variant has arisen  of NIPD –34, expe of NIPD , 36. An  of NIPD . However of NIPD .F8 gene, which is located at the telomeric end of the X chromosome. Specific adaptations may be required for certain disorders, such as increasing the number of targeted SNPs at the locus of interest by lowering the minor allele frequency threshold used in the capture probe design, or increasing the size of the target region around the gene.MPS-based techniques also require a careful study of the genomic environment of the tested gene before designing the enrichment panel. In our study, we tested hemophilia A carriers in our training cohort, but had to exclude them from our test cohort, because our capture probes did not target enough SNPs at the 3’ end of the With the widespread implementation of non-invasive prenatal testing for fetal aneuploidy, incidental maternal findings have been reported, particularly maternal malignancy , 40. To In conclusion, we report an easy-to-use workflow for SGD-NIPD. We have adapted the previously published relative haplotype dosage analysis approach into a straightforward, easy-to-perform workflow. Although thresholds for statistical risks and quality parameters can be easily adjusted depending on biological expertise, we propose an optimized value for each parameter. The accuracy and reliability of the entire process was then validated on a large real-life-like cohort. By testing multiple transmission modes, variant types, and proband relationships, we hope to review a wide range of situations commonly encountered in the context of PND. Future work will explore the cost of our approach compared to current clinical practice before this safe, non-invasive approach can be readily implemented as an accredited diagnostic service in a public health laboratory.S1 FigIn the extensive definition, heterozygous SNPs in gDNA are defined by an allelic frequency of the alternative allele (AF) comprised between 0.15 and 0.85, while homozygous positions in gDNA are defined by an AF <0.15 or >0.85. By contrast, the restrictive definition corresponds to heterozygous SNPs in gDNA defined by an AF comprised between 0.35 and 0.65 while homozygous positions’ AF in gDNA remain unchanged. An extensive definition of a heterozygous position could retain more informative positions than the restrictive definition but is likely to introduce SNP categorization errors due to misspecification of SNP positions associated with biased allele frequency.(TIF)Click here for additional data file.S2 FigAt PDP fixed, there is no difference in scores with higher NDP. Therefore, the minimal sequencing depth for calling SNP in genomic DNA was fixed at 8x.(TIF)Click here for additional data file.S3 FigAt NDP fixed, PDP variation did not impact the Sc, indicating no impact on the concordance of haplotype blocks classification between forward and reverse orientations. However, Sb value rises at low PDP, which reflects a better quality of analysis. Therefore, the minimal sequencing depth for calling SNP in cfDNA was fixed at 15x.(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S9 Fig(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableADMI = autosomal dominant maternal inheritance; ADPI = autosomal dominant paternal inheritance; ARI = autosomal recessive inheritance; RXLI = X-linked inheritance; AC = affected child; UC = unaffected child; AR = affected close relative; UR = unaffected close relative; DP = depth of sequencing; #SNP4 = SNP4 count; #FWD and #REV = halotype block count in either orientation; #MUA, #MUB, #MUX: mean count of SNP4α, SNP4β or SNP4 (X-linked inheritance); CS: concordance score; BS: block score; Preg = pregnancy number; Inh = genetic inheritance mode; Prob = proband.(PDF)Click here for additional data file.S4 TableThe p-values are adjusted for multiple comparisons.(PDF)Click here for additional data file.S5 TableThe global p-value (Friedman test) is indicated in brackets on the top of the table. All p-values are adjusted for multiple comparisons.(PDF)Click here for additional data file.S6 TableThe global p-value (Friedman test) is indicated in brackets on the top of the table. All p-values are adjusted for multiple comparisons.(PDF)Click here for additional data file.S7 Tablep-values for ANOVA tests are indicated in brackets.(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.


On the basis of these, 41 genes were identified. Previously proposed candidate genes for CC traits were supported by this study, including NFATC2 (Nuclear Factor of Activated T Cells 2), PROP1 (PROP Paired-Like Homeobox 1), UBB (Ubiquitin B), and HAND2 . These promising candidates provide a valuable resource for validating potential meat production genes and will facilitate the development of high-yielding Xinjiang donkey breeds through marker-assisted selection or gene editing.Animal genotyping by means of genome-wide association studies is important for connecting phenotypes of interest with their underlying genetics in livestock. However, the use of whole genome sequencing to investigate chest circumference (CC) in donkeys has rarely been reported. We aimed to use the genome-wide association study approach to detect significant single nucleotide polymorphisms (SNPs) and key genes associated with chest circumference traits in Xinjiang donkeys. We assessed 112 Xinjiang donkeys in this study. The chest circumference of each was measured 2 h before milking. We re-sequenced blood samples from the Xinjiang donkeys, and genome-wide association study analyses were performed using a mixed model with the PLINK, GEMMA, and REGENIE programs. We tested 38 donkeys for candidate SNPs for genome-wide association study using three software programs. Additionally, 18 SNP markers reached genome-wide significance ( Equus asinus), a descendant of the African wild ass, is a common domestic animal  using BWA (version 0.7.17) [http://broadinstitu-te.github.io/picard, accessed on 10 February 2022), the potential PCR duplicates were filtered. Samtools (version 1.9) was used for mapping [We performed a per-base-sequence quality check using Fastp (version 0.19.7) software. The paired-end sequence reads were mapped against the reference donkey genome [ 0.7.17) . Using t mapping . SNP var mapping . Data prThe CC data of 112 Xinjiang donkeys were used for subsequent association analyses. Measurements were taken by the same person using a tape measure and measuring stick, always on the right side of the animal, with the donkey standing with its front and hind legs perpendicular to the ground. Chest circumference measurements were performed after milking.p-value < 0.01; and (3) minor allele frequency (MAF) > 0.05.SNP filtering was conducted using PLINK software  with thep-values for suggestive genome-wide significant associations were set at 0.05/6,205,008 , and a genome-wide Bonferroni correction threshold of 0.01/6,205,008  was used to assess the significance level for each SNP.Single-SNP association analysis was performed using a mixed-model approach in three software packages: PLINK, REGENIE , and GEMhttps://www.genecards.org, accessed on 10 February 2022) was used to find the gene function. We selected 200 kb before and after the locus as the final association interval and used the database for functional gene mining of the association interval.The variants were annotated using snpEff (version 4.0e)  accordinThe descriptive statistics of the CC trait are reported in It can be seen from We examined the normality of the chest circumference values in Xinjiang donkeys  and founWe generated the genomic sequences of 112 Xinjiang donkeys, which yielded 2986.2 Gb of clean data with an average depth of 9.3× for subsequent analysis . An averAfter SNP calling and quality control, 12,039,909 high-quality SNPs were obtained for all 112 individuals. Following SnpEff annotation, we found that the number of SNPs located in the intergenic region was the greatest , followed by those in the intron , upstream gene regions , and downstream gene regions  .After localizing the reads to the reference genome, it was found that the coverage reached an average of 98.49% for 1×, 88.09% for 5× and 47.50% for 10× . In addip-values for PLINK, REGENIE, and GEMMA clearly showed the REGENIE correction for the population structure within this population (The QQ plot of GWAS pulation .p < 8.06 × 10−9) were found for the CC trait, of which 16 SNPs were highly significantly correlated with body size (p < 1.61 × 10−9). In GEMMA, 36 significant SNPs associations (p < 8.06 × 10−9) were found for the CC trait, of which 17 SNPs were significantly correlated with body size (p < 1.61 × 10−9). In REGENIE, nine significant SNPs associations (p < 8.06 × 10−9) were found for the CC trait, of which four SNPs were highly significantly correlated with body size (p < 1.61 × 10−9) (p < 8.06 × 10−9) associated with the CC trait appeared in the results of all three different analyses (p < 1.61 × 10−9) associated with CC traits appeared in the results of all three different analyses  , Table 2analyses A. Additianalyses B. Among analyses C.Chinese donkeys are rich in genetic resources and can be divided into three types according to their body size: large, medium, and small . The Xinp-values in New England Centenarian Study-imputed genotype data, and substantially more conservative p-values in Long Life Family Study WGS data.We performed GWAS for the body size phenotype (CC) in the Xinjiang donkey population. The associations between SNPs and CC traits were analyzed using three previously reported methods: PLINK, GEMMA, and REGENIE ,55,56. AEquus asinus (ass) autosome 7 (EAA 7)  region, EAA10 , EAA15 , EAA19 , and EAA29  were found to be significant with respect to CC traits, and are reported for the first time in this study. One SNP at 39,553,358 bp in nuclear factor of activated T cells 2 (NFATC2) on EAA 15 was identified to have a highly significant effect on CC using the three methods. According to Huang et al. [NFATC2. Calcium is a necessary factor for bone cell proliferation and differentiation. It has been shown that PGF2alpha receptor increase intracellular calcium levels, and the involvement of the calcium-regulated transcription factor nuclear factor of activated T cells (NFAT) in mediating PGF2alpha-enhanced cell growth was further investigated. The results showed that NFAT was activated by PGF2alpha and that NFATC2 is required for PGF2alpha-induced muscle cell growth and nuclear accretion [NFATC2 is essential for regulating myonuclear cell addition and subsequent muscle growth. Valerie determined that NFATC2 regulates the expression of IL-4, a novel molecular signal that controls myoblast fusion with myotubes, during muscle growth. IL-4 is expressed by a subset of muscle cells in fusing muscle cultures and acts through the IL-4 receptor on myoblasts to promote myoblast fusion and muscle growth [NFATC2 gene is mainly involved in the WNT signaling pathway. Wnt signaling occurs through evolutionarily conserved pathways that affect cellular proliferation and fate decisions during development and tissue maintenance [Five regions and eight SNPs were identified as having significant effects on the three methods in the GWAS . The Equg et al. , this geccretion . NFATC2 e growth . The NFAPROP1) on EAA 9 was an important SNP. The PROP1 gene encodes a protein that regulates growth and development in mammals, and the PROP1 gene is a novel important candidate gene for detecting genetic variation and growth, reproduction, metabolism traits selection and breeding [PROP1 was studied in 670 New Zealand Romney sheep; three single nucleotide polymorphisms (SNPs) were detected, and association analysis revealed that the variants were significantly associated with weaning weights [PROP1 gene regulates the expression of PRL and POU1F1 genes. Previous studies have proved that POU1F1 gene polymorphisms are significantly associated with the chest circumference of Nanyang cattle [POU1F1 gene polymorphisms are associated with chest circumference in goats [One SNP at 26419804 bp in PROP paired-like homeobox 1 (breeding . Genetic weights . Previou weights . It is wg cattle . SimilarUBB). It has been reported in a previous study that the loss of UBB can lead to a progressive degenerative disorder affecting neurons within the arcuate nucleus of the hypothalamus. This neurodegenerative cytopathology is accompanied by impaired hypothalamic control of energy balance and obesity [The association with CC on EAA 13 implicated a candidate gene, Ubiquitin B ( obesity .HAND2) gene, which is a critical transcription factor for post-mitotic maintenance of the sympathetic nervous system [HAND2 regulates skeletal muscle sympathetic and motor innervation, improving acetylcholine receptor (AChR) stability and nerve-activated muscle force generation. We found the HAND2 gene to be enriched upon transcriptional regulation by the Runt-related transcription factor 2 (RUNX2) pathway. Previous studies have demonstrated a significant association between a 12 bp insertion within the RUNX2 gene and chest circumference (p = 0.005) in Shaanbei white cashmere goats [HAND2 regulates chest circumference by directly promoting the transcription factor RUNX2. However, little is known about how LOC123277442, LOC123282041, LOC106844757, LOC123278496, and LOC123278509 regulate the CC traits. The functions of these genes are unknown.One SNP (13871622) was located near the heart and the neural crest derivative 2 (s system . Rodrigus system  found thre goats . Here, wNFATC2, PROP1, UBB, and HAND2, may be the main candidate genes affecting the chest circumference of Xinjiang donkeys. More research is needed to verify the extent of the associations detected in this study and to determine the potential of using these genes to improve meat production performance in Xinjiang donkeys.The GWAS performed in this study revealed the presence of genomic regions and putative causal genes associated with CC traits in the Xinjiang donkey. A total of 38 SNPs related to the CC of Xinjiang donkeys were identified. In addition, 41 candidate genes were identified, and several genes, including