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1000 Genomes Project

Overview

The goal of the 1000 Genomes Project was to find most genetic variants with frequencies of at least 1% in the populations studied. It was the first project to sequence the genomes of a large number of people, to provide a comprehensive resource on human genetic variation. Data from the 1000 Genomes Project was quickly made available to the worldwide scientific community through freely accessible public databases.

Publication

Sudmant, P., Rausch, T., Gardner, E. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015). https://doi.org/10.1038/nature15394

Populations

  • The super population membership can be found here: (http://www.1000genomes.org/category/population/)

  • We want to capture the allele frequencies for all 26 populations as well as the 5 super populations and the total population.

Small Variants

VCF File Parsing

The original VCF files come with allele frequency fields (e.g. ALL_AF, AMR_AF) but we recompute them using allele counts and allele numbers in order to get 6 digit precision. The allele counts and allele numbers (e.g. AMR_AC, AMR_AN) are not expressed in the INFO field. Instead the genotypes need to be parsed to compute that information. Our team converted the original data to VCF entries with allele counts and allele numbers like the following.

#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO
1       15274   rs62636497      A       G,T     100     PASS    AC=1739,3210;AF=0.347244,0.640974;AN=5008;NS=2504;DP=23255;EAS_AF=0.4812,0.5188;AMR_AF=0.2752,0.7205;AFR_AF=0.323,0.6369;EUR_AF=0.2922,0.7078;SAS_AF=0.3497,0.6472;AA=g|||;VT=SNP;MULTI_ALLELIC;EAS_AN=1008;EAS_AC=485,523;EUR_AN=1006;EUR_AC=294,712;AFR_AN=1322;AFR_AC=427,842;AMR_AN=694;AMR_AC=191,500;SAS_AN=978;SAS_AC=342,633

The ancestral allele, if it exists, is the first value in the pipe separated AA fields (the Indel specific REF, ALT, IndelType fields are ignored).

We parse the VCF file and extract the following fields from INFO:

  • AA

  • AC

  • AN

  • EAS_AN

  • AMR_AN

  • AFR_AN

  • EUR_AN

  • SAS_AN

  • EAS_AC

  • AMR_AC

  • AFR_AC

  • EUR_AC

  • SAS_AC

Conflict Resolution

We have observed conflicting allele frequency information in the source. Take the following example:

That is, the variant 1-20505705-C-CTG has conflicting entries. To get an idea of how frequently we observe this, here is a table summarizing ChrX and all chromosomes. Note that almost all such entries are found in ChrX.

Chromosome
# of alleles
# of conflicting alleles
percentage

chrX

834800

2733

0.33%

Total

21413098

2743

0.013%

Currently, we removed the allele frequency of the conflicting allele (i.e., insertion TG in the example) but keep allele frequencies of all other alleles in the VCF line.

Potential Alternate Solutions

  • Remove all alleles that are contained in the vcf lines which have conflicting allele. (Recommended by 1000 genome group Holly Zheng-Bradley, 7/29/2015)

  • Recalculate the allele frequency for the conflicting allele.

  • Pick the allele frequency that has the highest data support.

Download URL

GRCh37 GRCh38

JSON Output

Field
Type
Notes

allAf

float

allele frequency for all populations. Range: 0 - 1.0

allAc

int

allele count for all populations. Integer.

allAn

int

allele number for all populations. Non-zero integer.

afrAf

float

allele frequency for the African super population. Range: 0 - 1.0

afrAc

int

allele count for the African super population. Integer.

afrAn

int

allele number for the African super population. Non-zero integer.

amrAf

float

allele frequency for the Ad Mixed American super population. Range: 0 - 1.0

amrAc

int

allele count for the Ad Mixed American super population. Integer.

amrAn

int

allele number for the Ad Mixed American super population. Non-zero integer.

easAf

float

allele frequency for the East Asian super population. Range: 0 - 1.0

easAc

int

allele count for the East Asian super population. Integer.

easAn

int

allele number for the East Asian super population. Non-zero integer.

eurAf

float

allele frequency for the European super population. Range: 0 - 1.0

eurAc

int

allele count for the European super population. Integer.

eurAn

int

allele number for the European super population. Non-zero integer.

sasAf

float

allele frequency for the South Asian super population. Range: 0 - 1.0

sasAc

int

allele count for the South Asian super population. Integer.

sasAn

int

allele number for the South Asian super population. Non-zero integer.

Structural Variants

VCF File Parsing

The VCF files contain entries like the following:

Please note that, CNVs are allele-specific. For example, HG00096 is effectively copy number 4, which would be a net gain on chr22.

1000 Genomes contains 5 types of structural variants:

  • CNV

  • DEL

  • DUP

  • INS

  • INV

Since data of 1000 genomes is provided in VCF format, we assume that the coordinates follow the vcf format, i.e., there is a padding base for symbolic alleles. So all the interval can be interpreted as [BEGIN+1, END]. Similarly, for all other variant types except insertion, END is far larger than BEGIN. The distribution of BEGIN and END for insertions is summarized below.

Insertion issues

  • END = BEGIN for 6/165

  • END = BEGIN+2 for 93/165

  • END = BEGIN+3 for 11/165

  • END = BEGIN+4 for 11/165

  • END – BEGIN range from 5 to 1156 for others.

Converting VCF svTypes to SO sequence alterations

The svType will be captured in our JSON file under the sequenceAlteration key. Here's the translation we'll use according to svType in 1000 Genomes.

svType
Alternative Alleles contain <CN*>
sequenceAlteration

ALU

FALSE

mobile_element_insertion

DUP

TRUE

copy_number_gain

CNV

TRUE

copy_number_gain (observed_gains >0 and observed_losses =0) copy_number_loss (observed_gains = 0 and observed_losses > 0) copy_number_variation (otherwise)

DEL

TRUE

copy_number_loss

LINE1

FALSE

mobile_element_insertion

SVA

FALSE

mobile_element_insertion

INV

FALSE

inversion

INS

FALSE

insertion

Exceptions

We discard structural variants without END

CNVs in chrY

  • No other types of structural variants exist in chrY

  • Since copy number is provided in genotype field, we directly parse the copy number from "CN" field.

  • For most CNVs in chrY, the reference copy number is 1, but the refence number for CNVs in segmental duplication sites is 2 (<CN2> in the 2nd example). All segmental duplication calls have identifiers starting with GS_SD_M2.

JSON Output

Field
Type
Notes

chromosome

string

begin

integer

end

integer

variantType

string

id

string

allAn

integer

allele number for all populations. Non-zero integer.

allAc

integer

allele count for all populations. Integer.

allAf

floating point

allele frequency for all populations. Range: 0 - 1.0

afrAf

floating point

allele frequency for the African super population. Range: 0 - 1.0

amrAf

floating point

allele frequency for the Ad Mixed American super population. Range: 0 - 1.0

eurAf

floating point

allele frequency for the European super population. Range: 0 - 1.0

easAf

floating point

allele frequency for the East Asian super population. Range: 0 - 1.0

sasAf

floating point

allele frequency for the South Asian super population. Range: 0 - 1.0

reciprocalOverlap

floating point

range: 0 - 1.

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