These papers detailing the pilot part of the 1000 Genomes project really mark an enormous resource for biomedical research. These are some of the ways it will help:

1. As we carry out our genomewide association studies for Alzheimer’s and other diseases, when we see an association, it will tell us which variants are on the chromosome we have found association to (this is called imputation). This means we really don’t need to do any sequencing...or at least, much less sequencing...to have a list of candidate pathogenic variants.

2. When we find rare variants that predispose to disease, we can go the other way: We can see what haplotype on which they arose, and understand where the mutation may have come from and who else may have the mutation we have found.

3. A major problem in clinical genetics is knowing when we find mutations in genes which have pathogenic mutations (a good example is the PGRN gene in dementia cases); we cannot always know if the new variant we have found is pathogenic, or harmless, or something in between. These data will help to answer that (see   Read more

These papers detailing the pilot part of the 1000 Genomes project really mark an enormous resource for biomedical research. These are some of the ways it will help:

1. As we carry out our genomewide association studies for Alzheimer’s and other diseases, when we see an association, it will tell us which variants are on the chromosome we have found association to (this is called imputation). This means we really don’t need to do any sequencing...or at least, much less sequencing...to have a list of candidate pathogenic variants.

2. When we find rare variants that predispose to disease, we can go the other way: We can see what haplotype on which they arose, and understand where the mutation may have come from and who else may have the mutation we have found.

3. A major problem in clinical genetics is knowing when we find mutations in genes which have pathogenic mutations (a good example is the PGRN gene in dementia cases); we cannot always know if the new variant we have found is pathogenic, or harmless, or something in between. These data will help to answer that (see Guerreiro et al., 2010 for a more complete discussion of this).

These are immediate practical uses for this huge number of data, but the data are much more than this. The observation that we all have about 250 heterozygous gene knockouts and that we all have about 50 heterozygous loss-of-function genes, which would lead to serious illness if they were homozygous, are interesting pieces of data. The information we will get about gene regulation because we know how it varies among people will be invaluable. Our appreciation of population relatedness will also massively increase. Our understanding of the mechanisms of mutation occurrence...the list just goes on and on.

These publications are landmark events in our appreciation of our own biology.

View all comments by John Hardy

Clearly, the 1000 Genomes Project and related projects are very important for those of us who are interested in the genetic determinants of all diseases. These projects give us the background information for disease association studies. One of the major goals of our lab and other similar labs is to find variants that are present in the order of 0.1-5.0 percent of the general population and substantively increase disease risk. These types of projects tell us what variability is out there in this range. Obviously, as more and more people are sequenced, the lower limit for our studies can fall below even the 0.1 percent level.

View all comments by John Hardy

This study is a major achievement.

In the International Genomics of Alzheimer’s Project (IGAP), which gathers the four largest GWAS consortia on AD, we have been using the 1000 Genomes information since the beginning of our collaboration to perform imputations. This has allowed us to identify around eight million SNPs in common for all the studies. Depending on the study, we were able to impute SNP with minor allele frequency of lower than 0.01 percent for half of the samples. In our present GWAS, 1000 Genomes and similar projects help us to identify specific genome areas where such rare SNPs are located. Once these are found, we have to confirm this imputed information by performing genotyping or deep sequencing. But it is clearly progress in deciphering what is called "hidden heritability."

Indeed, this 1000 Genomes map will help to more efficiently identify rare variants that may be implicated in neurodegenerative diseases. However, you will need very large population samples (as we have obtained in IGAP) to be able to detect variations in rare variants between cases...  Read more

This study is a major achievement.

In the International Genomics of Alzheimer’s Project (IGAP), which gathers the four largest GWAS consortia on AD, we have been using the 1000 Genomes information since the beginning of our collaboration to perform imputations. This has allowed us to identify around eight million SNPs in common for all the studies. Depending on the study, we were able to impute SNP with minor allele frequency of lower than 0.01 percent for half of the samples. In our present GWAS, 1000 Genomes and similar projects help us to identify specific genome areas where such rare SNPs are located. Once these are found, we have to confirm this imputed information by performing genotyping or deep sequencing. But it is clearly progress in deciphering what is called "hidden heritability."

Indeed, this 1000 Genomes map will help to more efficiently identify rare variants that may be implicated in neurodegenerative diseases. However, you will need very large population samples (as we have obtained in IGAP) to be able to detect variations in rare variants between cases and controls.

View all comments by Philippe Amouyel