Significantly fewer protein functional changing variants for lipid metabolism in Africans than in Europeans

Hapmap genotype, downloaded from HapMap project, was sampled from 11 human groups (Hapmap Public Release #3 on May 28, 2010, http://​hapmap.​ncbi.​nlm.​nih.​gov/​). The detailed information about these human groups are listed in Table 1. In brief, Human population = {African, East Asian, European, GIH, MEX}, in which African = {ASW, LWK, MKK, YRI}, East Asian = {CHB, CHD, JPT}, European = {CEU, TSI}, and GIH and MEX are two independent groups. The genotype data in some human groups (ASW, CEU, MXL, MKK and YRI) contained family trios data which could bias the results. Therefore the offspring for each trio of these groups were excluded from further analyses. Table 1 shows the final data set used for this study.

1000 Genome low-coverage genotype data (released in July, 2010), downloaded from 1000 Genome project (ftp://​ftp-trace.​ncbi.​nih.​gov/​1000genomes/​ftp/​pilot_​data/​release/​2010_​07/​low_​coverage/​snps/​), was sampled from CEU (European-American), CHB and JPT(East Asian), and YRI (African) (Table 1).

The preparation for the studied genotype data, the methods used for PFCVs selection, and the estimation of the genetic risks (R and Num) were described in details in our previous study [5]. Here we only focus briefly on the points specifically for this study.

The missense mutations of genes that were involved in metabolism of carbohydrates, lipids and amino acids were mainly considered in this study. The harmful impacts of many missense mutations over the genes were estimated using Polyphen-2 [15] collected in dbNSFP [16]. For simplicity, the alleles with minor allele frequency (MAF) were called mutations throughout this article.

The genes involved in the metabolic processes of carbohydrates, lipids, and amino acids were downloaded from Gene Ontology (GO) (http://​www.​geneontology.​org). The name, symbol, and chromosome location of these selected genes are shown in details in Additional file 1: Table S1-S6 of supporting information. The total number of genes for each GO term is shown in Table 2. PFCVs with missense mutations on these genes were downloaded from NCBI dbSNP database (ftp://​ftp.​ncbi.​nih.​gov/​snp/​). The harmful probability for each PFCV estimated by Polyphen-2 was downloaded from http://​genetics.​bwh.​harvard.​edu/​pph2/​dbsearch.​shtml. For a false positive rate of 20%, the true positive prediction rate in PolyPhen-2 trained on HumDiv dataset is 92%, so the HumDiv-trained score for each mutation is referenced [15].

Two indices, R and Num of PFCVs, were used to assess the genetic risk in metabolic processes of carbohydrates, lipids and amino acids. The methods used to calculate R and Num were described in details in our previous study [5].

In this study, we used permutation to reduce the background risk level when assessing the actual genetic risks. Total 18161 genes in human genome were used for the assessment and they were downloaded from http://​www.​geneontology.​org. Two types of permutation tests were conducted in this study. 1. Gene-based permutation. Of these 18161 genes, the given number of genes (the number of genes involved in carbohydrate, lipid or amino acid metabolism (catabolism and biosynthesis), Table 2) were re-sampled randomly up to 2000 times, followed by the calculations of R and Num for these genes. 2. PFCV-based permutation. The number of harmful PFCVs on a given set of genes (the set of genes involved in the carbohydrate, lipid or amino acid metabolism, Table 2) were counted and recorded. The same number of PFCVs was re-sampled randomly from total PFCVs of 18161 human genes up to 2000 times, followed by the calculations of R and Num on these re-sampled PFCVs. Because the results from these two tests were very similar [5], in this article we only showed and discussed the results obtained by using the gene-based permutation test. The results of these permutation tests were used as an estimation to the background risk when analyzing data.

For example, the total number of genes involved in lipid catabolic process for Africans and Europeans was 218 (Table 2), therefore 218 genes were re-sampled randomly each time from 18161 human genes and R and Num were calculated on these genes as background risk level of R and Num for Africans and Europeans. At each round of re-sampling, we calculated the mean of R (or Num) and the mean difference R’ (R’ = mean R_African – mean R_Europan). Total 2000 of R’ were obtained for each population for 2000 re-sampling processes and the distribution of R’ was close to normal (Additional file 2: Figure S1-S12). The actual observed mean difference (R’_{lipid_catabolism}) for lipid catabolic process was also calculated. P-Value was approximately equal to the number of re-sampled with R’< R’_{lipid_catabolism} divided by 2000.

The unpaired two-tailed Student’s test, the F test (ANOVA, Analysis of Variance) and permutation tests were performed to assess the genetic risks among three subpopulations (Africans, Europeans and Asians) and 11 human groups (ASW, LWK, MKK, YRI, CHB, CHD, JPT, CEU, TSI, GIH and MEX).

In this study, two genotype datasets, Hapmap and 1000 Genome, were used for the analyses. A few characteristics existed between these two datasets. First, Hapmap had larger number of population groups (three subpopulations with 11 human groups) while 1000 Genome only had three subpopulations with four human groups. Second, sample sizes were similar between Hapmap (58 ≤ size ≤ 156) and 1000 Genome (59 for YRI and 60 for CEU and CHB and JPT). Third, 1000 Genome had the most PFCVs, including total number of PFCVs and total number of PFCVs with missense mutation (Table 1). These features indicated that some differences existed between Hapmap and 1000 Genome, regarding the number of human groups, the density of PFCVs, and the sample size. As a result, we explained the outcomes for each dataset separately although we used the same methods to analyze these data.

Our results showed that in carbohydrate metabolism, most of the background R (on genes randomly sampled from human genomes using permutation test) was bigger than the observed R (on genes involved in metabolism of energy substances) (Figures 1, 2 and 3). It was intriguing that the observed R in carbohydrate catabolic process was significantly bigger than the background R (P << 0.01, Figure 1A). This result suggested that carbohydrate catabolic process might specially harbor more genetic mutations than expected (average on background). We also implemented the comparison of the observed R in catabolic and biosynthetic processes of carbohydrate among subpopulations (African, European and Asian) and among 11 human groups. The results showed no significant differences among subpopulations (P = 0.1903, F test, Table 3) in carbohydrate catabolic process, suggesting that the genetic risk R in carbohydrate catabolic process might be very similar among Africans, Europeans and Asians. However, among 11 human groups in carbohydrate catabolic process, the observed R in GIH was significantly bigger than all other human groups (P < 0.01, t-test, Figure 1A). In carbohydrate biosynthetic process, the observed R was significantly different among subpopulations (P < 2.2 × 10^-16, F test, Table 3), and the R for all African groups was significantly bigger than non-African groups. (P < 0.01, t-test, all pairs, Figure 1B), suggesting that the genetic risk R in carbohydrate biosynthetic process in Africans might be the largest among human groups.

The observed R in lipid catabolic process was significantly smaller than the background R for each human group (P << 0.01, t-test, all pairs, Figure 2A), while the observed R in lipid biosynthetic process was also significantly smaller than the background R among most human groups with two exceptions (CEU and TSI) whose observed R were significantly bigger ( P <0.05, t-test, Figure 2B). In lipid catabolic process, the observed R in Africans was significantly smaller than all non-African groups (P < 0.05, t-test, all pairs, Figure 2A) with the fact that LWK of Africans held the smallest R (Figure 2A). In lipid biosynthetic process, the observed R in Africans was significantly smaller than Europeans (P < 0.01, Figure 2B). As far as the relationship of the observed R between Africans (ASW, LWK, MKK, YRI) and Asians (CHB, CHD, JPT), several different observations were obtained. First, the observed R in ASW and LWK was significantly smaller than CHB or smaller than CHD and JPT with no significance. Second, the observed R in YRI was significantly smaller than all Asian groups (CHB, CHD, and JPT). Third, MKK was the only group whose observed R was not significantly different from all Asian groups (Figure 2B). These observations suggested that the genetic risk R for lipid biosynthetic process in Africans might be smaller than all European groups and most of Asian groups.

The observed R in amino acid metabolic processes was consistently smaller than the background R. Especially, the ratio of R (the observed to the background) in the amino acid biosynthetic process was the smallest among all studied metabolic processes (Figures 1, 2 and 3), which suggested that, compared to the metabolism of carbohydrates and lipids, the biosynthesis of amino acids were more conservative and might harbor fewer mutations. In amino acid catabolic process, the observed R in most African groups were significantly smaller than Europeans and Asians (P < 0.01) with the exception of MKK whose R was significantly bigger than others (P < 0.01, Figure 3A). In amino acid biosynthetic process, the observed R in Africans was significantly bigger than non-Africans (P < 0.05), especially R in LWK, MKK and YRI were much bigger than other human groups (P < 0.01, Figure 3B). The observed R of Asians in amino acid biosynthetic process was the smallest among all studied human groups.

Additionally, we also implemented the comparison of the genetic risks (R and Num) between males and females for all human groups and results showed that there were no significant difference between males and females (P > 0.05, t- test, Table 3).

The above results for observed R were obtained without using the adjustment for background risk level. However, even with the consideration of background risk level, we still got the similar results (Additional file 2: Figure S13-S15).

1000 Genome provides more variants than Hapmap (Table 1). Therefore here we used 1000 Genome data to replicate the results obtained by using Hapmap data. Without considering background risk level, mean R’ or mean Num’ observed between Africans-Europeans or Africans-Asians (mean R_African – mean R_European or mean Num_African – mean Num_European) was observed to be bigger than 0 in 1000 Genome (Additional file 2: Figure S4-S15). This observation was different from the results observed using Hapmap data (see above) which might be resulted from the difference of total PFCVs between Hapmap and 1000 Genome (Table 1). After adjusting the background risk level using permutation test, we observed that R and Num in both lipid catabolic and lipid biosynthetic processes were significantly smaller in Africans than in Europeans (P < 0.05, permutation test, Table 4, Additional file 2: Figure S5 and S8). However, for carbohydrate and amino acid metabolisms, the significant difference of R or Num between Africans and Europeans was not observed in either catabolic or biosynthetic process (P > 0.05, Table 4, Additional file 2: Figure S4-S15). We also observed that R and Num in lipid catabolic process were smaller in Africans than in Asians with no significance (P = 0.099 for R; P = 0.0125 for Num). but this relationship was not observed in lipid biosynthetic process between Africans and Asians (P = 0.797 for R; P = 0.865 for Num) (Table 4, Additional file 2: Figure S11 and S14). The above observations suggested that the genetic risks (R or Num) in lipid catabolic and lipid biosynthetic processes were smaller in Africans than in Europeans, while in metabolisms of carbohydrates and amino acids, this relationship was not held.

We hypothesized that in order to respond to high-energy food environment, the smaller genetic risks among Africans in lipid metabolism might be translated to higher efficience of utilizing lipids as energy substances metabolism compared to Europeans.

This hypothesis could be tested with a simple experimental design. Triglycerides (TGs), the main energy substances in lipids, are usually biosynthesized in liver and are transported in blood as part of lipoprotein particles to the end of body for energy expenditure or energy storage [3, 17]. With smaller genetic risks (R and Num) among Africans in lipid biosynthetic process, the efficiency of TGs biosynthesis in liver cells might be higher compared to Europeans [5, 10]. Thus based on our hypothesis, the first clinical prediction should be that TGs level in arm arterial serum among Africans should be higher compared to Europeans . It was reported clinically that TGs level in arm venous serum was lower in Africans than in Europeans [18]. The difference of TGs between arterial and venous serum (TG_{arterial-venous}) was usually larger than 0 [19], which suggested that it might not be appropriate to infer TGs in arterial serum using TGs in venous serum. TG_{arterial-venous} represented the net consumption of TGs in lipids expenditure and storage. Our second clinical prediction should be that TG_{arterial-venous} would be higher in Africans than in Europeans. Therefore, the data of TGs in arterial and venous serum obtained at the same time can be used to examine the predictions of our hypothesis.