Application of genome-wide SNP data for uncovering pairwise relationships and quantitative trait loci.

Abstract

The genetic analysis of quantitative traits in humans is changing as a result of the availability of whole-genome SNP data. Heritability analysis can make use of actual genetic sharing between pairs of individuals estimated from the genotype data, rather than the expected genetic sharing implied by their family relationship. This could provide more accurate heritability estimates and help to overcome the equal environment assumption. Quantitative trait locus (QTL) linkage mapping can make use of local genetic sharing inferred from very dense local genotype data from pedigree members or individuals not previously known to be related. This approach may be particularly suited for detecting loci that contain rare variants with major effect on the phenotype. Finally, whole-genome SNP data can be used to measure the genetic similarity between individuals to provide matched sets for association studies, in order to avoid spurious association from population stratification.

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Application of genome-wide SNP data for
uncovering pairwise relationships and
quantitative trait loci
P. C. Sham∗†and S. S. Cherny∗and S. Purcell‡
April 30, 2008
∗Department of Psychiatry, Genome Research Centre, and The State Key Laboratory
of Brain and Cognitive Sciences, The University of Hong Kong
†To whom correspondence should be addressed at Department of Psychiatry, L10-69,
Laboratory Block, Li Ka Shing Faculty of Medicine, 21 Sassoon Road, Pokfulam, Hong
Kong; email: pcsham@hku.hk
‡Center for Human Genetics Research, Massachusetts General Hospital, Harvard Med-
ical School
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Abstract
The genetic analysis of quantitative traits in humans is changing
as a result of the availability of whole-genome SNP data. Heritability
analysis can make use of actual genetic sharing between pairs of indi-
viduals estimated from the genotype data, rather than the expected
genetic sharing implied by their family relationship. This could pro-
vide more accurate heritability estimates and help to overcome the
equal environment assumption. Quantitative trait locus (QTL) link-
age mapping can make use of local genetic sharing inferred from very
dense local genotype data from pedigree members or individuals not
previously known to be related. This approach may be particularly
suited for detecting loci that contain rare variants with major effect
on the phenotype. Finally, whole-genome SNP data can be used to
measure the genetic similarity between individuals to provide matched
sets for association studies, in order to avoid spurious association from
population stratification.
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1 Introduction
Genetic analysis of quantitative traits in humans usually proceeds in three
stages: (1) determining the relative contributions of genetic versus envi-
ronmental factors in explaining phenotypic variation in the population, (2)
identifying the chromosomal locations of the genetic loci involved, and finally
(3) determining the actual genes involved and how variants in these genes
influence the phenotype Lynch and Walsh (1998). The statistical power and
precision with which these questions can be addressed have been improved
over the past decades by developments in molecular genetic technologies that
have progressively enabled a greater number and variety of genetic polymor-
phisms and mutations to be assayed, and created detailed maps of genes
and sequence variants. Commercial genotyping microarray products are now
capable of routinely assaying up to one million single nucleotide polymor-
phisms (SNPs) throughout the genome. This article reviews the likely impact
of this technology on the genetic analysis of quantitative traits in humans,
in particular with regard to the mapping of quantitative trait loci (QTL).
Whole-genome SNP data also have applications for genome-wide association
screens that do not consider familial relationships; such applications are very
important but beyond the scope of this article.
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2 Genetic relationships and heritability anal-
ysis
In traditional genetic analysis, the significance of genetic relationships be-
tween individuals is that, through the random co-inheritance of chromosome
segments from common ancestor(s), the individuals are expected to be ge-
netically identical for certain parts of their genomes. The expected propor-
tion of genomic sharing between two individuals depends on the closeness
of their relationship, being 100% for monozygotic twins, 50% for first-degree
relatives, 25% for second-degree relatives, and so on. The partitioning of
phenotypic variance into genetic and environmental components proposed
by Fisher (1918) is based on the consideration of the phenotypic correlations
for relative classes with different expectations of genetic sharing. If the ef-
fects of genetic variants on the phenotype are additive, then the expected
genetic sharing between pairs of relatives of a certain class (e.g., full siblings)
is also the intraclass correlation of the individuals’ genetic contributions to
the phenotype. Given two different relative classes that are assumed to be
equally correlated with respect to the environmental component, for example,
monozygotic (MZ) and dizygotic (DZ) twins, an estimate of the heritability
is given by
h2=difference in phenotypic correlations
difference in genetic correlations
(1)
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This is equivalent to the familiar Falconer formula, h2= 2(MZ correlation−
DZ correlation) for the classical twin design Falconer and Mackay (1996). If
environmental sharing can be assumed to be absent through the adoption of
one or both members of each pair, then an estimate for heritability can be
obtained from a single class of relatives (e.g., half siblings), as follows:
h2=phenotypic correlation
genetic correlation
(2)
On this basis, heritability estimates have been obtained as the phenotypic
correlation of MZ twins reared apart, or twice the phenotypic correlations of
parent-child pairs, where the children have been adopted away.
3 Local genetic sharing and QTL linkage map-
ping
The above theoretical framework for heritability estimation can be extended
to address specific parts rather than the whole of the genome. For example,
it would be meaningful to partition the genetic component of a quantitative
trait further down to the contributions of the 22 autosomes and the sex
chromosomes, and to subsections of these chromosomes. In principle, all
that is needed is to relate phenotypic correlations to the genetic correlation
with respect to a particular portion of the genome. However, there are some
important conceptual and technical differences.
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