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Adapt-Mix: learning local genetic correlation structure improves summary statistics-based analyses.

Park DS, Brown B, Eng C, Huntsman S, Hu D, Torgerson DG, Burchard EG, Zaitlen N - Bioinformatics (2015)

Bottom Line: Approaches to identifying new risk loci, training risk prediction models, imputing untyped variants and fine-mapping causal variants from summary statistics of genome-wide association studies are playing an increasingly important role in the human genetics community.This approach, especially in admixed populations, has the potential to produce misleading results, ignores variation in local structure and is not feasible when appropriate reference panels are missing or small.We applied Adapt-Mix to estimate the genetic correlation structure of both admixed and non-admixed individuals using simulated and real data.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.

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This heatmap shows the average mixture frequency assigned to each reference population when optimizing over independent chromosomes for various datasets
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btv230-F1: This heatmap shows the average mixture frequency assigned to each reference population when optimizing over independent chromosomes for various datasets

Mentions: We applied our method to simulated data over Mexican and Puerto Rican individuals from the GALA II cohort (Borrell et al.., 2013). Figure 1 shows the average frequency assigned to each population when frequencies were optimized per chromosome. When matching reference populations are included in the optimization (MXL for the Mexicans and PUR for the Puerto Ricans), nearly one-third of the mixture is assigned to the matching reference panel. The rest of the frequencies are distributed to populations in a similar manner to the admixture proportions of each group (Baran et al.., 2012). Having predominantly Native American and European ancestry, Mexicans have frequencies distributed among European and East Asian panels in addition to MXL. However, when MXL and PUR are not included, we see an increase in frequency assigned to the East Asian panels. Puerto Ricans have more African ancestry than Native American ancestry, and we observe a correspondingly larger frequency of the YRI (African) panel and lower frequencies of East Asian panels.Fig. 1.


Adapt-Mix: learning local genetic correlation structure improves summary statistics-based analyses.

Park DS, Brown B, Eng C, Huntsman S, Hu D, Torgerson DG, Burchard EG, Zaitlen N - Bioinformatics (2015)

This heatmap shows the average mixture frequency assigned to each reference population when optimizing over independent chromosomes for various datasets
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4553832&req=5

btv230-F1: This heatmap shows the average mixture frequency assigned to each reference population when optimizing over independent chromosomes for various datasets
Mentions: We applied our method to simulated data over Mexican and Puerto Rican individuals from the GALA II cohort (Borrell et al.., 2013). Figure 1 shows the average frequency assigned to each population when frequencies were optimized per chromosome. When matching reference populations are included in the optimization (MXL for the Mexicans and PUR for the Puerto Ricans), nearly one-third of the mixture is assigned to the matching reference panel. The rest of the frequencies are distributed to populations in a similar manner to the admixture proportions of each group (Baran et al.., 2012). Having predominantly Native American and European ancestry, Mexicans have frequencies distributed among European and East Asian panels in addition to MXL. However, when MXL and PUR are not included, we see an increase in frequency assigned to the East Asian panels. Puerto Ricans have more African ancestry than Native American ancestry, and we observe a correspondingly larger frequency of the YRI (African) panel and lower frequencies of East Asian panels.Fig. 1.

Bottom Line: Approaches to identifying new risk loci, training risk prediction models, imputing untyped variants and fine-mapping causal variants from summary statistics of genome-wide association studies are playing an increasingly important role in the human genetics community.This approach, especially in admixed populations, has the potential to produce misleading results, ignores variation in local structure and is not feasible when appropriate reference panels are missing or small.We applied Adapt-Mix to estimate the genetic correlation structure of both admixed and non-admixed individuals using simulated and real data.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, Department of Computer Science, University of California Berkeley, Berkeley and Department of Medicine, University of California San Francisco, San Francisco, CA, USA.

Show MeSH