More than 10% of structural variants in human genome are in potentially problematic regions

535,461,456 bases which corresponds to 16.22% of the genome are considered "problematic regions in the genome assembly". Looking at the structural variants identified by previous studies available in the UCSC tables, we find 26,934 (13.3 % of reported structural variants) overlap these problematic regions.

                                   Study Overlapping Total         Percent_overlapping_problematic_regions

1        1000 Genomes Consortium Phase 1   2067  20554  10.0564367

2  1000 Genomes Consortium Pilot Project    322   7967   4.0416719

3                         Ahn et al 2009    825   3704  22.2732181

4                       Alkan et al 2009    139    177  78.5310734

5                   Altshuler et al 2010    135    844  15.9952607

6                        Arlt et al 2011    347   2522  13.7589215

7                    Banerjee et al 2011      4    733   0.5457026

8                     Bentley et al 2008    405   5646   7.1732200

9                    Campbell et al 2011    281   1180  23.8135593

10                     Conrad et al 2006     89    672  13.2440476

11                     Conrad et al 2009   1144   8489  13.4762634

12                     Cooper et al 2008    135    311  43.4083601

13                   de Smith et al 2007    303   1492  20.3083110

14                       Feuk et al 2005      0      3   0.0000000

15                   Forsberg et al 2012      0      2   0.0000000

16                     Giglio et al 2002      0      1   0.0000000

17                      Gusev et al 2009     15    211   7.1090047

18                      Hinds et al 2006      2     87   2.2988506

19                    Iafrate et al 2004     56    187  29.9465241

20                     Itsara et al 2009    495   4806  10.2996255

21                  Jakobsson et al 2008    165   1424  11.5870787

22                         Ju et al 2010    321   1289  24.9030256

23                       Kidd et al 2008    934   7380  12.6558266

24                       Kidd et al 2010      1     20   5.0000000

25                      Kidd et al 2010b    179    739  24.2219215

26                        Kim et al 2009    126   1300   9.6923077

27                     Korbel et al 2007    411    974  42.1971253

28                       Levy et al 2007    839  10146   8.2692687

29                      Locke et al 2006    233    353  66.0056657

30                  McCarroll et al 2006    100    531  18.8323917

31                  McCarroll et al 2008    282   1313  21.4775324

32                   McKernan et al 2009    581   6925   8.3898917

33                      Mills et al 2006    332   5321   6.2394287

34                       Pang et al 2010    652   6096  10.6955381

35                       Park et al 2010    844   5747  14.6859231

36                      Perry et al 2008    991   2889  34.3025268

37                     Perry et al 2008b    175    341  51.3196481

38                      Pinto et al 2007    304   1029  29.5432459

39                      Redon et al 2006   1372   3308  41.4752116

40                   Schuster et al 2010     90    186  48.3870968

41                      Sebat et al 2004     36     77  46.7532468

42                     Shaikh et al 2009   1133  12844   8.8212395

43                      Sharp et al 2005     81    105  77.1428571

44              Simon-Sanchez et al 2007     64    232  27.5862069

45                 Stefansson et al 2005      0      1   0.0000000

46                     Teague et al 2010    732   4144  17.6640927

47                      Tuzun et al 2005    132    286  46.1538462

48                       Wang et al 2007    213   1291  16.4988381

49                       Wang et al 2008    235   2644   8.8880484

50                    Wheeler et al 2008     17     23  73.9130435

51                       Wong et al 2007    643   5011  12.8317701

52                      Wong et al 2012b   3347  33069  10.1212616

53                         Xu et al 2011   4599  25794  17.8297278

54                      Young et al 2008      3      7  42.8571429

55                        Zhu et al 2011      3      3 100.0000000

Should these structural variants be removed from the UCSC table? Or atleast do they require further validation.

Sequence divergence between human and chimp

Using the data about ancestral state from UCSC we calculate the fraction of sites that are different between Human and Chimp. We can see in below table that on average 14% of the sites are different between human and chimp. ChrX shows a much higher divergence at 23%, which is expected given its lower Ne.

Chromosome	Number of sites that are different	Total number of sites	Percent difference
chr1	574730	4097311	14.03
chr2	571968	4337056	13.19
chr3	460826	3601819	12.79
chr4	483915	3522581	13.74
chr5	432399	3222413	13.42
chr6	430879	3147588	13.69
chr7	426619	2928175	14.57
chr8	380886	2786423	13.67
chr9	333171	2283198	14.59
chr10	351451	2471725	14.22
chr11	354062	2492733	14.20
chr12	326365	2396839	13.62
chr13	234420	1761333	13.31
chr14	228533	1645955	13.88
chr15	207061	1516600	13.65
chr16	244166	1680348	14.53
chr17	215074	1476349	14.57
chr18	190200	1397643	13.61
chr19	207813	1157902	17.95
chr20	164209	1207849	13.60
chr21	106186	719102	14.77
chr22	113969	721194	15.80
chrX	439833	1906519	23.07
			14.54

When we look divergence in windows across chromsome 2 and chromosome X we see higher divergence at the centromere and telomere. (see figures below and note the difference in scale of y-axis).

The fraction of sites that are different between human and chimp in a window is correlated with the DAF's we looked at earlier.  The correlation coefficient for chr2 is 0.4 and for chrX it is 0.2. 

openSNP MDS plot from plink

After downloading all the genotype data from the openSNP website, the first thing to do is to see population structure ofcourse!

Using AIM's (Ancestry Informative Markers) is a rather quick method to determine ancestry using a minimal set of markers. Various groups have used slightly different methods to come up with such markers. Instead of looking at these markers, we look at Lactose tolerance phenotype and the population structure in the genotypes with this trait.

The MDS plot (generated using plink after converting using opengwas) has one weird outlier that does not cluster. It is curious, but i hope they make the data available in a easier to use/standardised format so that i can dig into this.

The number of males that decided to reveal their gender [290/1379(21.03%) ] is double that of females [149/1379(10.8%) ]. Data of birth was shared slightly more reluctantly with 403/1379 (29.22%) sharing their DOB compared to the 439/1379(31.83%) that shared their gender. Not surprisingly most people who shared their gender also shared their DOB (116/149 females and 253/290 males). See below barplot of the distribution of DOB's with mean age of ~40 (minimum is 19 and max is 114). 

Human chr15 SLC24A5 pigmentation gene

Coming back to being human, we look at chr15 DAF's. The most prominent feature is the short_arm (information not available). Having come across the database of recent positive selection across human populations (dbPSHP), i was hoping to see a clear signal of DAF near the SLC24A5 gene. It does not seem to be one of the more prominent DAF peaks. Infact, adding the SV2B gene onto the plot does not bring it near any of the prominent DAF peaks. Will have to see how many of my DAF peaks actually match up those in the dbPSHP.

The correlation coefficient of 0.3277578 between the mean derived allele frequency and Number of CNV's is line with the results from other chromosomes.

The code is not different from before:

 chr <- "chr15";    
 jpeg(paste("DAF.",chr,".jpeg",sep=""),width=1420)    
 par(mfrow=c(2,1))    
 read.table(file=paste("h.",chr,".mean.bed",sep=""),header=F,stringsAsFactors=F)->M    
 plot(as.numeric(M$V2),as.numeric(M$V4),xlab="Position along chromosome",ylab="Mean derived allele Frequency",main=chr)    
 lines(c(10000,17000000),c(0.2,0.2),col="red",lwd=5)    
 text(10000, 0.25,labels="short_arm",col="red")    
 lines(c(101981189,101991189),c(0.3,0.3),col="blue",lwd=5)    
 text(101981189, 0.35,labels="Telomere",col="blue")    
 lines(c(0,10000),c(0.3,0.3),col="blue",lwd=5)    
 text(0, 0.35,labels="Telomere",col="blue")    
 lines(c(48413169,48434589),c(0.25,0.25),col="brown",lwd=5)    
 text(48413169, 0.3,labels="SLC24A5 gene",col="brown")    
 lines(c(91643182,91844539),c(0.25,0.25),col="brown",lwd=5)    
 text(91643182, 0.3,labels="SV2B gene",col="brown")    
 read.table(file=paste("h.",chr,".countdgv.bed",sep=""),header=FALSE)->C    
 plot(C$V2,C$V4,xlab="Position along chromosome",ylab="Count of known structural variants",main=chr)    
 cor.test(as.numeric(M$V4),C$V4,method="spearman")    
 dev.off()