Estimating relationships between phenotypes and subjects drawn from admixed families

Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulat...

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Published inBMC proceedings Vol. 10; no. S7; pp. 357 - 362
Main Authors Blue, Elizabeth M., Brown, Lisa A., Conomos, Matthew P., Kirk, Jennifer L., Nato, Alejandro Q., Popejoy, Alice B., Raffa, Jesse, Ranola, John, Wijsman, Ellen M., Thornton, Timothy
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Abstract Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model. We found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations. Estimates of heritability and association vary with age for longitudinally measured traits. Accounting for local ancestry identified different true associations than those identified by a traditional approach. Principal components aid kinship estimation and tests for association, but their utility is influenced by the frequency of the markers used to generate them. Admixed families can provide a powerful resource for detecting disease loci, as well as analytical challenges. Allele frequencies, although difficult to adequately estimate in admixed populations, have a strong impact on the estimation of kinship, ancestry, and association with phenotypes. Approaches that acknowledge population structure in admixed families outperform those which ignore it.
AbstractList Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model. We found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations. Estimates of heritability and association vary with age for longitudinally measured traits. Accounting for local ancestry identified different true associations than those identified by a traditional approach. Principal components aid kinship estimation and tests for association, but their utility is influenced by the frequency of the markers used to generate them. Admixed families can provide a powerful resource for detecting disease loci, as well as analytical challenges. Allele frequencies, although difficult to adequately estimate in admixed populations, have a strong impact on the estimation of kinship, ancestry, and association with phenotypes. Approaches that acknowledge population structure in admixed families outperform those which ignore it.
Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model.BACKGROUNDEstimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model.We found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations. Estimates of heritability and association vary with age for longitudinally measured traits. Accounting for local ancestry identified different true associations than those identified by a traditional approach. Principal components aid kinship estimation and tests for association, but their utility is influenced by the frequency of the markers used to generate them.RESULTSWe found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations. Estimates of heritability and association vary with age for longitudinally measured traits. Accounting for local ancestry identified different true associations than those identified by a traditional approach. Principal components aid kinship estimation and tests for association, but their utility is influenced by the frequency of the markers used to generate them.Admixed families can provide a powerful resource for detecting disease loci, as well as analytical challenges. Allele frequencies, although difficult to adequately estimate in admixed populations, have a strong impact on the estimation of kinship, ancestry, and association with phenotypes. Approaches that acknowledge population structure in admixed families outperform those which ignore it.CONCLUSIONSAdmixed families can provide a powerful resource for detecting disease loci, as well as analytical challenges. Allele frequencies, although difficult to adequately estimate in admixed populations, have a strong impact on the estimation of kinship, ancestry, and association with phenotypes. Approaches that acknowledge population structure in admixed families outperform those which ignore it.
ArticleNumber 42
Author Thornton, Timothy
Wijsman, Ellen M.
Brown, Lisa A.
Raffa, Jesse
Conomos, Matthew P.
Ranola, John
Blue, Elizabeth M.
Nato, Alejandro Q.
Kirk, Jennifer L.
Popejoy, Alice B.
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Cites_doi 10.1002/gepi.20418
10.1016/j.ajhg.2009.01.005
10.1038/nature02168
10.1038/ng.548
10.1002/gepi.21737
10.1093/bioinformatics/btr330
10.1002/gepi.21896
10.1093/bioinformatics/bts606
10.1038/ng1847
10.1126/science.296.5566.261b
10.1093/bioinformatics/btq559
10.1093/genetics/163.3.1153
10.1016/j.ajhg.2013.06.020
10.1038/nature09534
10.1086/504302
10.1101/gr.7156307
10.1086/519795
10.1101/gr.094052.109
10.1186/1753-6561-8-S1-S5
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References International HapMap Consortium (56_CR15) 2003; 426
P Danecek (56_CR6) 2011; 27
BK Maples (56_CR4) 2013; 93
X Zheng (56_CR8) 2012; 28
TC Matise (56_CR5) 2007; 17
56_CR11
Y Choi (56_CR3) 2009; 33
HM Cann (56_CR14) 2002; 296
J Morrison (56_CR10) 2013; 37
HM Kang (56_CR12) 2010; 42
A Manichaikul (56_CR2) 2010; 26
GR Abecasis (56_CR9) 2010; 467
AL Price (56_CR19) 2006; 38
H Tang (56_CR16) 2006; 79
TA Thornton (56_CR18) 2014; 8
S Purcell (56_CR7) 2007; 81
DH Alexander (56_CR17) 2009; 19
BG Milligan (56_CR1) 2003; 163
BL Browning (56_CR13) 2009; 84
21653522 - Bioinformatics. 2011 Aug 1;27(15):2156-8
20926424 - Bioinformatics. 2010 Nov 15;26(22):2867-73
17989245 - Genome Res. 2007 Dec;17(12):1783-6
25810074 - Genet Epidemiol. 2015 May;39(4):276-93
23910464 - Am J Hum Genet. 2013 Aug 8;93(2):278-88
17701901 - Am J Hum Genet. 2007 Sep;81(3):559-75
14685227 - Nature. 2003 Dec 18;426(6968):789-96
16862161 - Nat Genet. 2006 Aug;38(8):904-9
25519330 - BMC Proc. 2014 Jun 17;8(Suppl 1):S5
23740691 - Genet Epidemiol. 2013 Sep;37(6):635-41
20208533 - Nat Genet. 2010 Apr;42(4):348-54
20981092 - Nature. 2010 Oct 28;467(7319):1061-73
16773560 - Am J Hum Genet. 2006 Jul;79(1):1-12
11954565 - Science. 2002 Apr 12;296(5566):261-2
19333967 - Genet Epidemiol. 2009 Dec;33(8):668-78
19200528 - Am J Hum Genet. 2009 Feb;84(2):210-23
12663552 - Genetics. 2003 Mar;163(3):1153-67
23060615 - Bioinformatics. 2012 Dec 15;28(24):3326-8
19648217 - Genome Res. 2009 Sep;19(9):1655-64
References_xml – volume: 33
  start-page: 668
  issue: 8
  year: 2009
  ident: 56_CR3
  publication-title: Genet Epidemiol
  doi: 10.1002/gepi.20418
– volume: 84
  start-page: 210
  issue: 2
  year: 2009
  ident: 56_CR13
  publication-title: Am J Hum Genet
  doi: 10.1016/j.ajhg.2009.01.005
– volume: 426
  start-page: 789
  issue: 6968
  year: 2003
  ident: 56_CR15
  publication-title: Nature
  doi: 10.1038/nature02168
– volume: 42
  start-page: 348
  issue: 4
  year: 2010
  ident: 56_CR12
  publication-title: Nat Genet
  doi: 10.1038/ng.548
– volume: 37
  start-page: 635
  issue: 6
  year: 2013
  ident: 56_CR10
  publication-title: Genet Epidemiol
  doi: 10.1002/gepi.21737
– volume: 27
  start-page: 2156
  issue: 15
  year: 2011
  ident: 56_CR6
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btr330
– ident: 56_CR11
  doi: 10.1002/gepi.21896
– volume: 28
  start-page: 3326
  issue: 24
  year: 2012
  ident: 56_CR8
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/bts606
– volume: 38
  start-page: 904
  issue: 8
  year: 2006
  ident: 56_CR19
  publication-title: Nat Genet
  doi: 10.1038/ng1847
– volume: 296
  start-page: 261
  issue: 5566
  year: 2002
  ident: 56_CR14
  publication-title: Science
  doi: 10.1126/science.296.5566.261b
– volume: 26
  start-page: 2867
  issue: 22
  year: 2010
  ident: 56_CR2
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btq559
– volume: 163
  start-page: 1153
  issue: 3
  year: 2003
  ident: 56_CR1
  publication-title: Genetics
  doi: 10.1093/genetics/163.3.1153
– volume: 93
  start-page: 278
  issue: 2
  year: 2013
  ident: 56_CR4
  publication-title: Am J Hum Genet
  doi: 10.1016/j.ajhg.2013.06.020
– volume: 467
  start-page: 1061
  issue: 7319
  year: 2010
  ident: 56_CR9
  publication-title: Nature
  doi: 10.1038/nature09534
– volume: 79
  start-page: 1
  issue: 1
  year: 2006
  ident: 56_CR16
  publication-title: Am J Hum Genet
  doi: 10.1086/504302
– volume: 17
  start-page: 1783
  issue: 12
  year: 2007
  ident: 56_CR5
  publication-title: Genome Res
  doi: 10.1101/gr.7156307
– volume: 81
  start-page: 559
  issue: 3
  year: 2007
  ident: 56_CR7
  publication-title: Am J Hum Genet
  doi: 10.1086/519795
– volume: 19
  start-page: 1655
  issue: 9
  year: 2009
  ident: 56_CR17
  publication-title: Genome Res
  doi: 10.1101/gr.094052.109
– volume: 8
  start-page: S5
  issue: Suppl 1
  year: 2014
  ident: 56_CR18
  publication-title: BMC Proc
  doi: 10.1186/1753-6561-8-S1-S5
– reference: 16773560 - Am J Hum Genet. 2006 Jul;79(1):1-12
– reference: 20926424 - Bioinformatics. 2010 Nov 15;26(22):2867-73
– reference: 19200528 - Am J Hum Genet. 2009 Feb;84(2):210-23
– reference: 25810074 - Genet Epidemiol. 2015 May;39(4):276-93
– reference: 11954565 - Science. 2002 Apr 12;296(5566):261-2
– reference: 20208533 - Nat Genet. 2010 Apr;42(4):348-54
– reference: 25519330 - BMC Proc. 2014 Jun 17;8(Suppl 1):S5
– reference: 23060615 - Bioinformatics. 2012 Dec 15;28(24):3326-8
– reference: 14685227 - Nature. 2003 Dec 18;426(6968):789-96
– reference: 23740691 - Genet Epidemiol. 2013 Sep;37(6):635-41
– reference: 23910464 - Am J Hum Genet. 2013 Aug 8;93(2):278-88
– reference: 21653522 - Bioinformatics. 2011 Aug 1;27(15):2156-8
– reference: 12663552 - Genetics. 2003 Mar;163(3):1153-67
– reference: 19333967 - Genet Epidemiol. 2009 Dec;33(8):668-78
– reference: 16862161 - Nat Genet. 2006 Aug;38(8):904-9
– reference: 17701901 - Am J Hum Genet. 2007 Sep;81(3):559-75
– reference: 17989245 - Genome Res. 2007 Dec;17(12):1783-6
– reference: 19648217 - Genome Res. 2009 Sep;19(9):1655-64
– reference: 20981092 - Nature. 2010 Oct 28;467(7319):1061-73
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