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      Genome-Wide Association Studies

Introduction and Lectures
Intro to the Workshop and Core
Dr. Anthony Musolf Talk
What is Bioinformatics/Genomics?
Cheat Sheets
Software and Links
Logging In
Cluster Computing
Data Reduction
Files and Filetypes
Project setup
Preprocessing raw data
Alignment with BWA
Variant calling using GATK
Comparison of freebayes, GATK, and deepvariant output
Data Analysis
Plink Step by Step TDT
Plink Step by Step TDT (solutions)
wAnnovar Annotation
Plink Step by Step (Non FBAT excercise)
Setup in R
GWAS Visualization
Closing thoughts
Workshop Photos
Github page
Biocore website

Files and File Types

The primary file types you’ll see related to DNA sequence analysis are:

Sequence based file types

Sequence based files first started out as fasta with paired qual files (Sanger and 454), with Illumina and quality scores being used more, the fastq file became the default output from DNA sequencers. These days additional file types are being used, including fast5 by Oxford Nanopore and ‘unmapped’ bam files by Pacific Biosciences.



The fasta format uses the ‘>’ to indicate a new sequence followed by the name of the sequence on the same line. The following line(s) are the DNA sequence and may be split on multiple lines (wrapped), until the next ‘>’ is reached. Genome and transcriptome files are most often in fasta format.


Qual files are so rarely used these days and so are not discussed.


fastq files combine the sequence and quality scores into 1 file. Each sequence here has 4 lines (should be enforced strictly), header, sequence, historical ‘+’, and quality.


CASAVA 1.8 Read IDs

@EAS139:136:FC706VJ:2:2104:15343:197393 1:Y:18:ATCACG

Quality Scores

Quality scores are paired 1 to 1 with sequence characters.

Each quality character has a numerical value associated with it (ASCII value). In Illumina 1.8+ you subtract 33 from the ascii value associated with the quality character to get the quality score.




Annotation based file types

Gene Transfer Format (GTF) / Gene Feature Format (GFF)

Describes feature (ex. gene) locations within a sequence file (ex. genome).

The GFF (General Feature Format) format consists of one line per feature, each containing 9 columns of data (fields). The GTF (General Transfer Format) is identical to GFF version 2. The GTF file format tends to have stricter requirements and expectations on how feature descriptions are formated.

Fields must be tab-separated and all fields must contain a value; “empty” fields should be denoted with a ‘.’.




Alignment based file types


The project managing the specifications and common software is http://www.htslib.org/.

SAM (Sequence Alignment/Map) format is the unified format for storing read alignments to a reference sequence (consistent since Sept. 2011). http://samtools.github.io/hts-specs/SAMv1.pdf http://samtools.github.io/hts-specs/SAMtags.pdf

BAM are compressed SAMs (binary, not human-readable). They can be indexed to allow rapid extraction of information, so alignment viewers do not need to uncompress the whole BAM file in order to look at information for a particular coordinate range, somewhere in the file.

Indexing your BAM file, mybam.bam, will create an index file, mybam.bam.bai, which is needed (in addition to the BAM file) by viewers and some downstream tools.

CRAM is an even more compressed SAM/BAM file and has the following major objectives:

  1. Significantly better lossless compression than BAM
  2. Full compatibility with BAM
  3. Effortless transition to CRAM from using BAM files
  4. Support for controlled loss of BAM data

CRAM files are also indexed and produce .cram.crai index files.

The SAM format contain two regions:

The header section - Each header line begins with character ’@’ followed by a two-letter record type code.

The alignment section - Each alignment line has 11 mandatory fields. These fields always appear in the same order and must be present, but their values can be ‘0’ or ‘*’, if the corresponding information if unavailable, or not applicable.

Header section

Each header line begins with the character ‘@’ followed by one of the two-letter header record type codes. In the header, each line is TAB-delimited and, apart from @CO lines, each data field follows a format ‘TAG:VALUE’ where TAG is a two-character string that defines the format and content of VALUE.

The following tags are required:

Many more tags are detailed in the specifications.

Alignment section

The alignment section consists of 11 tab delimited fields.


QNAME: Query name
Read IDs are truncated at first whitespace (spaces / tabs), which can make them non-unique. Illumina reads with older IDs have trailing “/1” and “/2” stripped (this information is recorded in the next field). Illumina reads with newer IDs have second block stripped (read number is recorded in the next field).

@FCC6889ACXX:5:1101:8446:45501#CGATGTATC/1 ⇒ @FCC6889ACXX:5:1101:8446:45501
@HISEQ:153:H8ED7ADXX:1:1101:1368:2069 1:N:0:ATCACG ⇒ @HISEQ:153:H8ED7ADXX:1:1101:1368:2069

FLAG: Alignment flags


Tool to help explain flags


RNAME: reference sequence name
Reference sequence ID (from fasta header) matches a @SQ field in the header section.

POS: 1-based leftmost position of (post-clipping) aligned read

MAPQ: mapping quality (phred scaled)
MAPQ, contains the “phred-scaled posterior probability that the mapping position” is wrong.
In a probabilistic view, each read alignment is an estimate of the true alignment and is therefore also a random variable. It can be wrong. The error probability is scaled in the Phred. For example, given 1000 read alignments with mapping quality being 30, one of them will be incorrectly mapped to the wrong location on average.
A value 255 indicates that the mapping quality is not available.

MAPQ explained

The calculation of mapping qualities is simple, but this simple calculation considers many of the factors below:

When you see a read alignment with a mapping quality of 30 or greater, it usually implies:

In practice however, each mapping application seems to compute the MAPQ in their own way.

CIGAR: Compact Idiosyncratic Gapped Alignment Report (CIGAR)
The CIGAR string is used to calculate how a sequence aligns to the reference. Format: [0-9][MIDNSHP][0-9][MIDNSHP]… where M = match, I/D = insertion / deletion, N = skipped bases on reference (splicing), S/H = soft / hard clip (hard clipped bases no longer appear in the sequence field), P = padding.

e.g. “100M” means that all 100 bases in the read align to bases in the reference.



RNEXT: reference sequence name of the primary alignment of the next read in the template.
For the last read, the next read is the first read in the template. If @SQ header lines are present, RNEXT (if not ‘’ or ‘=’) must be present in one of the SQ-SN tag. This field is set as ‘’ when the information is unavailable, and set as ‘=’ if RNEXT is identical RNAME. If not ‘=’ and the next read in the template has one primary mapping (see also bit 0x100 in FLAG), this field is identical to RNAME at the primary line of the next read. If RNEXT is ‘*’, no assumptions can be made on PNEXT and bit 0x20.

PNEXT: 1-based position of the primary alignment of the next read in the template.
This field equals POS at the primary alignment of the next read. If PNEXT is 0, no assumptions can be made on RNEXT and bit 0x20. Set as 0 when the information is unavailable.

TLEN: The signed observed Template length.
If all segments are mapped to the same reference, the unsigned observed template length equals the number of bases from the leftmost mapped base to the rightmost mapped base. The leftmost segment has a plus sign and the rightmost has a minus sign. The sign of segments in the middle is undefined. It is set as 0 for single-segment template or when the information is unavailable.

SEQ: segment sequence
The sequence that was aligned. If hard clipping occurred, only the aligned portion is represented, if soft clipping occurred, the original sequence is present.

QUAL: segment quality scores
The quality scores of the sequence that was aligned. If hard clipping occurred, only the aligned portion is represented, if soft clipping occurred, the original sequence is present.

Variant call file type

VCF is a text file format. It contains meta-information lines, a header line, followed by data lines each containing information about a position in the genome. The format has the ability to contain genotype information on samples for each position. The full description of this file type is at https://samtools.github.io/hts-specs/VCFv4.2.pdf.

An example VCF:


Meta-information lines

Included after the ## string and must be _key=value* pairs. These lines most of the time describing the INFO, FILTER and FORMAT entries used in the body of the VCF file. If present, these lines must be completely well-formed.

Header line

The header line names the 8 fixed, mandatory columns: #CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO. If genotype data is present in the file, these are followed by a FORMAT column header, then an arbitrary number of sample IDS. The header line is tab-delimited.

Data lines

Each data line provide the corresponding information to the fields included in the header line for a position of the genome. There are many annotations associated with each variant and they are listed in the INFO column.

GVCF stands for Genomic VCF. A GVCF is a type of VCF file, with the same basic format specification the same. But it contains extra information. The general term of GVCF refers to a VCF file that contains a record for every position of the genome regardless whether a variation is detected at the site or not. The records in a GVCF generated by HaplotypeCaller include an accurate estimation of how confident the algorith is in the determination that the sites are homozygous-reference or not. GVCF files are very important for joint genotyping.