The Fundamentals of Modern Statistical Genetics

239 Pages·3.06 MB·english

by  Laird N.M.

Statistics for Biology and Health
SeriesEditors:
M.Gail
K.Krickeberg
J.M.Samet
A.Tsiatis
W.Wong
Forfurthervolumes:
http://www.springer.com/series/2848

·
Nan M. Laird Christoph Lange
The Fundamentals of Modern
Statistical Genetics
123
NanM.Laird ChristophLange
DepartmentofBiostatistics DepartmentofBiostatistics
HarvardUniversity HarvardUniversity
Boston,MA02115,USA Boston,MA02115,USA
[email protected] [email protected]
StatisticsforBiologyandHealthSeriesEditors
M.Gail A.Tsiatis
NationalCancerInstitute DepartmentofStatistics
Bethesda,MD20892,USA NorthCarolinaStateUniversity
Raleigh,NC27695,USA
KlausKrickeberg
LeChâtelet W.Wong
F-63270Manglieu,France DepartmentofStatistics
StanfordUniversity
JonathanM.Samet Stanford,CA94305-4065,USA
DepartmentofPreventiveMedicine
KeckSchoolofMedicine
UniversityofSouthernCalifornia
1441EastlakeAve.Room4436,MC9175
LosAngles,CA90089
ISSN1431-8776
ISBN978-1-4419-7337-5 e-ISBN978-1-4419-7338-2
DOI10.1007/978-1-4419-7338-2
SpringerNewYorkDordrechtHeidelbergLondon
(cid:2)c SpringerScience+BusinessMedia,LLC2011
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Toourfamilies,andallofthefamilies whose
datawehaveanalyzed.

Preface
Statisticalgeneticshasplayedapivotalroleformorethanacenturyinthediscovery
of genes that cause disease in humans. Driven by advances in molecular genetics
andmedicineandthecontinuingimprovementsingenotypingtechnology,statistical
models and methods have adapted over time to the challenges presented by new
studydesigns.
In this book we discuss the statistical models and methods that are used to
understand human genetics from an historical perspective. Starting with Mendel’s
firstexperimentstomorerecentgenome-wideassociationstudies,wedescribehow
genetic information can be incorporated into statistical models to discover disease
genes.Whilewecovermostofthecommonlyusedapproachesinstatisticalgenetics
(e.g.,aggregationanalysis,segregation,linkageanalysis,etc.),thefocusofthebook
is on modern approaches to association analysis. Our treatment of earlier topics
is mainly to help the reader see the larger picture and understand the historical
development of methods. We provide numerous examples to illustrate key points
throughoutthetext,bothofMendelianandcomplexgeneticdisorders.
Most statisticians, biostatisticians and data analysts are aware of the key role
that their disciplines have played in finding disease genes, but have little direct
knowledgeofhowgenediscoveryviagenemappingworks.Thisbookarisesfrom
teachingcoursestograduatestudents,withvaryinglevelsofstatisticalpreparation,
at the Harvard School of Public Health. Our intended audience for this book is
largely quantitatively oriented health scientists, including biostatisticians, statisti-
cians, epidemiologists, physicians and molecular geneticists, who want to learn
aboutstatisticalmethodsforgeneticanalysis,whethertobetteranalyzegeneticdata,
ortopursueresearchinmethodology.Weassumefamiliaritywithelementaryprob-
ability,statisticalinferenceandmethods,specificallydistributionsfortwoormore
variables,conditional,marginalandjointdistributions,Bayesrule,likelihoodmeth-
ods,hypothesistesting,estimation,correlationandtheessentialideasofregression,
including linear, log-linear and logistic. However, the book emphasizes concepts
and examples, and the exercises include problems for students with a broad range
ofskilllevels.Weassumenoformaltrainingingenetics,butfamiliaritywithbasic
conceptsinmoleculargeneticsisnecessaryandwillbereviewedinthefirstchapter.
There are many excellent texts in statistical methods currently available to stu-
dentsandwehaveusedmanyoftheminourteaching.Thisbooksharesmuchwith
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viii Preface
theclassictextsofSham(1998)andLange(2002),bothofwhichwerewrittenwith
asimilaraudienceinmind.Ourbookislessfocusedonlinkageandmorefocusedon
associationanalysisthanthetextbySham,andprovideseasierreadingforstudents
withlessmathematicaltrainingthanthebookbyLange.Wealsosharemuchwith
the newer texts by Thomas (2004) and Yang (2000), being less epidemiologically
orientedthanThomas,withmoreemphasisonhumandiseasethanYang.Thebook
by Foulkes (2009) has a stronger emphasis on software implementation while our
focusisonstatisticaltheoryandmethods.
Boston,Massachusetts NanM.Laird
BadGodesberg,Germany ChristophLange
Acknowledgments
This book would not have been possible without our former students, the staff at
theHarvardSchoolofPublicHealth,ourcolleagues,friendsandourfamilieswhom
we would like to thank for their the support, encouragement and help during the
writing process of the book. The book is based on the statistical genetics courses
that we have been teaching over the last 10 years here at the Harvard School of
PublicHealth. Withtheirfeedback andduringenjoyable discussions,many ofour
former students helped us to improve our courses and thereby the book. Without
theactiveandpatientsupportfromthestaffattheDepartmentofBiostatisticsatthe
Harvard School of Public Health, especially Jelena Follweiler, we would not have
been able to put the book together. We also thank the reviewers, our colleagues,
friends and family members for their keen eye during the review process of the
book.WeareespeciallythankfultoKaustubhAdhikari,GourabDe,MattMcQueen,
Jessica Lasky-Su and Tony Paredo for their help with the exercises, Ross Lazaras
for help with figures, and to Wai-Ki Yip, Deborah Blacker, Garrett Fitzmaurice,
JonathanHaines,AlkesPrice,BenjyRaby,DavidAlexander,LilyAltstein,Sharon
Lutz,CoryZigler,AndreasKraeusling,andseveralanonymousreviewersfortheir
commentsondraftsofthebook.WearealsoindebtedtoTylerVanderWeeleforcon-
tributingSectiononCompositionalEpistatisandCompositionalGene-Environment
Interactions.Last,butnotleast,wewouldliketomentionJohnKimmelforleading
usthroughthebookwritingprocess.
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