Inbreeding around the world, gene-environment interactions and sales of genetic tests

12 Responses to “Inbreeding around the world, gene-environment interactions and sales of genetic tests”

• Larry Parnell

  04/03/2011 at 14:51

  Yes, the Ober-Vercelli review is excellent. The challenges facing the GWAS arena in identifying GxE interactions are significant. To try to predict where GxEs are likely to be found, we start with those that have been determined experimentally. That is facilitated with a catalog of known GxEs such as those we’ve put together in a paper just released at http://omicsonline.org/2153-0602/2153-0602-2-106.pdf.
• steve

  08/03/2011 at 10:13

  Hi,

  I don’t want to be overly critical, and maybe I’m mis-understanding the
  target audience for these posts, but I have to say that hitting a phrase
  such as ‘..which estimates consanguinity of parents using runs of homozygosity
  in their children.’ in the second sentence of the article was..jarring to say
  the least. I *think* I know what you mean but surely there was a way of
  writing it in plain english ?

  Ed Yong (among others) has written extensively on the problem of trying to
  convey highly technical information to a lay or non expert audience without
  large amounts of jargon:

  http://blogs.discovermagazine.com/notrocketscience/2010/11/24/on-jargon-and-why-it-matters-in-science-writing/

  Just a thought.
• Luke

  08/03/2011 at 10:29

  Yes, a very good point. I wrote that very rapidly, and was pretty lazy in my use of language. I have updated it to explain in more detail what the paper is looking for.

  Thanks for pointing that out.
• Anne-Louise Leutenegger

  09/03/2011 at 13:13

  Bonjour,

  Thanks for citing our paper.
  The method estimates the level of inbreeding of the individuals or equivalently the level of kinship between their parents, but not the level of inbreeding of parents.
  Maybe something along those lines would read better “which estimates the level of inbreeding (or “consanguinity”) of individuals by looking for sections of the genome where the two parental chromosomes have identical DNA”.
• Luke

  09/03/2011 at 13:21

  @Anne-Louise

  Thanks for pointing that out! I’ve modified it to be a bit less wrong.
• Mary Paniscus

  09/03/2011 at 18:45

  This post was timely. I just calculated my % heterozygosity of 23 and me markers. I find I’ve got about 90% of the variation of the genomes unzipped authors, who were all more heterozygous than me. Perhaps it’s my notoriously consanguineous ethnicity of origin (which I see is somewhat less sad than the decimating population collapse in Native American populations) or perhaps it’s more an issue of cosmopolitan vs rural effects on mate choice. I’d love to see a paper on the strength of the effect of cosmopolitanrural living on calculations of individuals’ genetic variation.
• Luke

  09/03/2011 at 18:50

  @Mary

  Worth remembering that there is (sort of) a difference between lack of homozygosity and runs of homozygosity. Dan is far more homozygous than the rest of us, but that is a result of his Ashkenazi ancestry, not any recent relatedness of his parents.
• Mary Paniscus

  10/03/2011 at 17:32

  Here’s the raw data:

  file: CAA001_genotypes.txt heterozygosity: 30.8358275538311
  file: CFW001_genotypes.txt heterozygosity: 31.4044454411371
  file: DBV001_genotypes.txt heterozygosity: 30.7424563878846
  file: DFC001_genotypes.txt heterozygosity: 30.80868075188
  file: DGM001_genotypes.txt heterozygosity: 30.5667802682519
  file: IPF001_genotypes.txt heterozygosity: 31.4325384680558
  file: JCB001_genotypes.txt heterozygosity: 30.8904669768664
  file: JKP001_genotypes.txt heterozygosity: 31.0774311799696
  file: JXA001_genotypes.txt heterozygosity: 30.7756550246656
  file: KIM001_genotypes.txt heterozygosity: 31.398475283442
  file: LXJ001_genotypes.txt heterozygosity: 30.542745838499
  file: VXP001_genotypes.txt heterozygosity: 30.7749633863993
  file: genome_Mary_Paniscus_Full_20110201104429.txt heterozygosity: 29.5017358220516

  and the script I used to get it:

  **********************************************
  use warnings; use strict;

  unless($ARGV[0]){die “usage: perl $0 *.txt\n”}
  my @files=@ARGV;

  foreach my $file (@files){
  chomp $file;
  open (FH, $file);

  my $het = 0;
  my $total = 0;

  while (my $line = ){
  if ($line=~/^#/){next}
  $total++;
  chomp $line;
  my @elements=split(/\s+/,$line);
  my ($first,$second)=split(”,$elements[3]);

  unless ($first=~/[AGTC]/){next}
  unless ($second=~/[AGTC]/){next}
  unless ($first){next}
  unless ($second){next}

  if ($first eq $second){next}
  $het++;
  }

  my $percent=$het*100/$total;
  print “file: $file heterozygosity: $percent\n”;
  }

  **********************************************

  It just calculates the % heterozygous markers for each file. I haven’t changed it to only use markers shared by all the files under consideration yet. So these values are obviously affected by using a different gene chips.
• Daniel MacArthur

  10/03/2011 at 18:52

  Hi Mary,

  Is your data from the v3 chip? If so, it’s very likely that this explains at least some of the difference: we’re all on v2, and the extra SNPs on the v3 are probably enriched for rare SNPs that will show lower heterozygosity.
• Mary Paniscus

  11/03/2011 at 02:35

  I like this result better ;) –though I’m not finding Dan has the lowest heterozygosity.

  of 520288 total shared markers
  file: CAA001_genotypes.txt heterozygosity: 31.8610077495541
  file: CFW001_genotypes.txt heterozygosity: 31.8291023433175
  file: DBV001_genotypes.txt heterozygosity: 31.7943139184452 <- Dan V
  file: DFC001_genotypes.txt heterozygosity: 31.8287179408328
  file: DGM001_genotypes.txt heterozygosity: 31.6426671382004 <- Dan M
  file: IPF001_genotypes.txt heterozygosity: 31.7768236053878
  file: JCB001_genotypes.txt heterozygosity: 31.951918937204
  file: JKP001_genotypes.txt heterozygosity: 32.188710867827
  file: JXA001_genotypes.txt heterozygosity: 31.8477458638293
  file: KIM001_genotypes.txt heterozygosity: 31.7710575681161
  file: LXJ001_genotypes.txt heterozygosity: 31.6222938065072 screened”);
  my $line_count=0;

  foreach my $file (@files){ #1st step: screen lines
  chomp $file; # print “file: $file\n”

  open (FH, “$file”);
  while (my $line = ){
  if ($line=~/^#/){next} #no comment lines
  chomp $line;
  my @elements=split(/\s+/,$line);
  if ($#elements screened.sorted”);

  #initialize counters
  my $total = 0;
  my %het; foreach my $f (@files){$het{$f}=0}

  open (FH2,”screened.sorted”);

  #2nd step: count het sites of only shared sites (use same id#)
  my $lines_counted=1;
  my $last_line=;
  my @lines = ($last_line); #where we put all the lines matching last line’s id

  while ($lines_counted < $line_count){

  my $next_line=; $lines_counted++;
  #print “last: $last_line”, “next: $next_line”;
  my ($id,$chr,$pos,$nt,$file_name)=split(/\s+/,$last_line);
  my ($id2,$chr2,$pos2,$nt2,$file_name2)=split(/\s+/,$next_line);
  #print “id:*$id* id2:*$id2*\n”;
  #foreach my $line(@lines){print “in lines array:$line\n”}
  if ($id eq $id2){push(@lines,$next_line)}#add to list of same ids

  else { #if not a match
  $last_line=$next_line; #put in last line for next round
  #print “#lines:$#lines #files:$#files\n”;
  unless ($#lines eq $#files){@lines = ($last_line);next} #do we have reps for all genomes?
  $total++;
  foreach my $line(@lines){
  my ($id,$chr,$pos,$nt,$file_name)=split(/\s+/,$line);
  my ($first,$second)=split(”,$nt);
  if ($first eq $second){next}
  $het{$file_name}++;
  }
  @lines = ($last_line);
  }
  }

  print “of $total total shared markers\n”;
  foreach my $file (@files){
  my $percent=$het{$file}*100/$total;
  print “file: $file heterozygosity: $percent\n”;
  }
• Mary Paniscus

  11/03/2011 at 02:43

  ouch, that really did get maimed up there: here are the complete results with no attempted script attachment:

  of 520288 total shared markers
  file: CAA001_genotypes.txt heterozygosity: 31.8610077495541
  file: CFW001_genotypes.txt heterozygosity: 31.8291023433175
  file: DBV001_genotypes.txt heterozygosity: 31.7943139184452 <- Dan V
  file: DFC001_genotypes.txt heterozygosity: 31.8287179408328
  file: DGM001_genotypes.txt heterozygosity: 31.6426671382004 <- Dan M
  file: IPF001_genotypes.txt heterozygosity: 31.7768236053878
  file: JCB001_genotypes.txt heterozygosity: 31.951918937204
  file: JKP001_genotypes.txt heterozygosity: 32.188710867827
  file: JXA001_genotypes.txt heterozygosity: 31.8477458638293
  file: KIM001_genotypes.txt heterozygosity: 31.7710575681161
  file: LXJ001_genotypes.txt heterozygosity: 31.6222938065072 <—–lowest, also Jewish?
  file: VXP001_genotypes.txt heterozygosity: 31.7914308998093
  file: genome_Mary_Paniscus_Full_20110201104429.txt heterozygosity: 32.1912094839781

  I rewrote my earlier script to only use the subset of markers shared by all individuals files, and to exclude those on X and Y chromosomes. I sent it to the blog's gmail.
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  06/08/2012 at 13:54

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• Anonymous
  Pingback on Mar 18th, 2011 at 08:08